Sinkron : jurnal dan penelitian teknik informatika

Implementation of the C4.5 and Naive Bayes Algorithms to Predict Student Graduation

Lianah Lianah — 2024-07-26

This research aims to determine student graduation using two data mining methods, namely the Naive Bayes Classifier and the C4.5 Algorithm. Research stages include data analysis, data pre-processing, model design in data mining, classification results, method evaluation, and evaluation results. This research uses student data consisting of training data and testing data to evaluate the performance of the two methods in predicting student graduation based on attributes such as attendance scores, behavior scores, Final Semester Examination (UAS) scores, and report card scores. The classification results show significant differences between the two methods. The Naive Bayes Classifier produces predictions that 37 students pass and 17 students do not pass, while the C4.5 Algorithm predicts that 30 students pass and 24 students do not pass. This difference in results indicates that there are differences in the approaches of the two methods to student graduation data, with the Naive Bayes Classifier tending to provide more positive predictions than the C4.5 Algorithm. Evaluation of the performance of the method shows that the Naive Bayes Classifier has an accuracy rate of 100%, which is a perfect result, while the C4.5 Algorithm has an accuracy rate of 89%. This significant difference in evaluation results confirms that the Naive Bayes Classifier is superior in classifying student graduation compared to the C4.5 Algorithm in the context of this research. These findings can help in making decisions regarding student graduation evaluations in the future.

Composite Performance Index in Decision Making for Social Assistance

Andini Wulandari — 2024-07-26

The majority of the residents in this village, approximately 90%, worked as farmers or farm laborers. Given the economic conditions, social assistance became crucial in reducing social inequality and enhancing the welfare of vulnerable communities. The role of village governance was significant in improving community welfare. The Village Hall served as the center of village administration, managing various activities, including the distribution of social assistance. The Village Hall was responsible for ensuring that social assistance was distributed fairly and effectively to recipients according to prevailing policies. However, the Village Hall faced issues such as inefficiency and inequality in the distribution of social assistance. The process of selecting social assistance recipients was still conducted conventionally, where Village Hall staff collected data on the community based on certain criteria. This method was prone to errors in decision-making and incorrect distribution of assistance, such as recipients who did not actually qualify still receiving aid, while those in need often did not receive appropriate support. These issues were caused by a lack of thorough analysis. The village government needed to establish a decision-making system that was accurate and precise. The operation of this system included all steps of problem identification, selection of relevant information, and determination of the approach used for decision-making through to the resolution of the issues. To achieve accurate results, this research applied the Composite Performance Index method. The aim of this research was to create a decision support system (DSS) for selecting social assistance recipients in the village. This DSS was expected to help staff improve the speed of social assistance classification, avoid errors, and produce accurate decisions.

Edge Computing Architecture Sensor-based Flood Monitoring System: Design and Implementation

Djarot Hindarto — 2024-07-26

The purpose of this research is to develop and execute a system for monitoring floods using sensors and edge computing architecture. The goal is to make flood detection and prediction more accurate and faster. The growing frequency and severity of flood disasters in different parts of the world has prompted the necessity for a better system to track these events. The primary goal of this study is to design a system that can reduce network load and latency by processing sensor data locally at edge devices before sending it to the cloud. To detect and anticipate flood events, the research method incorporates several environmental sensors that measure things like soil moisture, water level, and rainfall. These readings are subsequently processed by edge nodes using machine learning algorithms. Compared to more conventional methods that depend only on cloud computing, the results demonstrate that the system can deliver quicker and more accurate predictions. Results showed a detection and prediction accuracy of 98.95% for floods. Edge computing also succeeded in drastically cutting down on bandwidth consumption and communication latency. This research concludes that edge computing architecture based on sensors can effectively monitor floods and has excellent potential for use in many different areas prone to flooding. Improving the prediction algorithm and investigating its potential integration with a more thorough early warning system should be the focus of future research.

Sentiment Analysis towards the 2024 Vice Presidential Candidate Debate Using the Support Vector Machine Algorithm

Raihan Rizieq Harahap — 2024-07-26

In today’s digital era, social media plays an important role in disseminating information and influencing public opinion. For instance, YouTube. At the 2024 Vice Presidential Debate, YouTube became a medium where people gave various comments. This study aimed to analyze public sentiment through comments on the 2024 Vice Presidential Debate on the Metro TV YouTube channel. This study used descriptive quantitative methods with the Support Vector Machine algorithm to identify various public comments. The results show that from the data experiment taken as many as 1012 data, 80% data training amounting to 809 data and 20% data testing amounting to 203 data is carried out. An accuracy of 82% was obtained with a precision value of 80%, a recall value of 87%, and an f1-score value of 83%. With a fairly high accuracy value, the support vector machine model can be said to be the right model to calculate the accuracy value in sentiment analysis.

Comparison Of Exponesial Smoothing With Linear Regression Predicting Amount Of Goods Sales

Erwin Panggabean — 2024-07-18

A trading business is a business that operates in the sales sector with the aim of obtaining maximum profits through sales activities. To be able to sell efficiently, a prediction system is needed, so that there is no excess or shortage of inventory and the sales process can run smoothly. Human limitations in solving prediction problems without using tools that apply prediction methods are one of the obstacles in finding the right prediction value. Therefore, we need a prediction system that can help find accurate and fast values. So the problem formulation is how to design and build a sales prediction system using exponential smoothing and linear regression methods, then compare the two and find out which method is the best, both of which use periodic data prediction models. The data collection method used is secondary data from previous research and journals, as well as combining library study methods, namely information obtained from books, references and scientific works related to predictions. The tool used to build applications is MS-Visual Studio 2010 and WEB based system

Design E-Learning User Interface On Website-Based Edspert.Id With Kansei Engineering Methods

Adhitya Rinda Wahyu Purnama — 2024-07-01

The development of information technology has encouraged people to rely on information systems, especially through websites. Websites provide easy access to information and learning with various educational materials. Although e-learning has been implemented in many educational websites, Edspert. id, a company in the education sector, has not implemented it yet. User interface design development is one of the important processes in e-learning website development. A user interface that is easy to use will improve the learning experience of learners. This research proposes a solution to design the user interface of Edspert.id e-learning website by using the Kansei Engineering method. This approach has been done beforefor web-based e-learning based on users' emotions. Principal component analysis (PCA) is used to reduce Kansei Word variables that are relevant to users' emotions. The e-learning website element design was then designed based on the PCA results. The next step is to determine the design elements in the e-learning design. Then, partial Least Square (PLS) was used to analyze the relationship between Kansei Word and element design. The results show that there multiuser interface design has two concepts whose element designs are in accordance with user needs.

Deep Learning for Exchange Rate Prediction Within Time Constrain

Ruly Sumargo — 2024-07-01

The implementation of an open economic system in Indonesia since 1969 has significant impact to the national economic growth. The high demand and supply of goods from within the country involved in international trade demonstrate a close correlation between export and import activities with the exchange rate of the rupiah. Economic stability is measured through the stability of the rupiah exchange rate against foreign currencies. The balance between demand and supply in the global market is considered crucial for creating a stable economy. History has recorded the Indonesian economic crisis in 1998, where the exchange rate of the rupiah against the US dollar drastically raises and causing challenges to the domestic production cost. This research aiming to make predictions using data science approach based on historical (time series) data. GRU, LSTM, and RNN algorithm being assess to perform the prediction. Results show that RNN algorithms generally outperform GRU and LSTM in making the prediction, particularly with limited time series data. Although RNN is typically superior, in one instance, GRU achieved slightly higher accuracy (0.047% difference) for the CNY to IDR pair over five years. Furthermore, the research highlights the substantial impact of batch size on algorithm accuracy, considering external factors such as interest rates. These findings offer valuable insights for economic decision-making and policy formulation.

Leveraging Enterprise Architecture to Empower KOMINFO's Business Core Operations: A PMO Perspective

Ratna Amalia Purawidjaja — 2024-07-01

The Sky Bridge (Tol Langit) Program is an Indonesian government’s strategic project aimed at digital transformation in the 3T regions (Tertinggal, Terdepan, Terluar - Underdeveloped, Frontline, Outermost). It requires thorough planning and integrated management for its implementation. A specialized unit with a helicopter view perspective is needed to ensure and oversee the alignment of processes. This important role is managed by the Project Management Office (PMO). One of the challenges PMO faces in ensuring an end-to-end process alignment is identifying the appropriate digital resources to support the process. This is where the Enterprise Architecture (EA) framework plays a crucial role as a blueprint for the organization's digital landscape. This reference helps map out existing data, applications, and business processes. Having this blueprint allows PMO to have a holistic view and make targeted decisions. EA also helps identify existing applications that can be integrated with new programs, avoiding unnecessary duplication. The use of ArchiMate, a language for enterprise architecture modeling, assists PMOs in planning digital transformations considering all aspects - business needs, applications, and technology. In short, a well-defined EA framework empowers PMOs to navigate the complexities of digital transformation in the telecommunications sector to ensure the successful implementation of the Sky Bridge Program.

Comparison of MAGIQ, MABAC, MARCOS, and MOORA Methods in Multi-Criteria Problems

Gede Dharma Sahasra Muni — 2024-07-01

Determining the best alternative from many criteria is one of the core problems in decision making, both routine and non-routine problems. One of them is in the problem of determining egg suppliers. Eggs are one of the basic needs of the community so that the demand for eggs is always increasing, this makes the emergence of many egg agents in distributing and fulfilling needs. Selective and careful selection is needed in order to get a supplier that meets the desired expectations. Problems then arise in the selection of egg suppliers that are not in accordance with the expectations of the manager. In determining egg suppliers that have been carried out by UD Taluh Subur, only by means of a simple comparison between several factors such as price, production quantity, and quality without considering other factors. In addition to this, business managers have limited knowledge in statistical and business decision making. To optimize the supplier selection process, a Decision Support System can be used to help provide recommendations for selecting prospective suppliers of fixed eggs. Based on the situation of decision makers who have limited knowledge in statistical decision making, the MAGIQ method is suitable for weighting. To provide a more accurate ranking, additional methods such as the MABAC, MARCOS, and MOORA methods are used. The purpose of this research is to focus on which method is most recommended for the case study faced in the research based on the analysis results of the sensitivity test. The results of the sensitivity test show that the MAGIQ-MABAC method has the highest value of 4.42737%, then the MAGIQ-MOORA method with a value of 2.34415% and the MAGIQ-MARCOS method with a value of 0.45729%.

A Literature Review: Development of Electronic Medical Records In Hospital Management Information Systems

Dhau 'Atha Yudhistira — 2024-07-01

Introduction: Health technology today is developing very quickly from the initially conventional using paper to being computerized. This literature review aims to map and critically summarize the scientific evidence on the cost-effectiveness and acceptability of Computerized Physician Order Entry (CPOE) and Electronic Health Reports. This Journal have Question that need to be answered how does the development of CPOE in medical records affect cost-effectiveness improvement so that it can be accepted by many parties? Method used in this journal is literature review is conducted on journal articles related to costs and receipts in CPOE. The systematic search was conducted from 5 databases namely PubMed, Science Direct, ProQuest, DOAJ and Ebscohost. Journal articles are selected and selected following PRISMA guidelines. Twenty-five journal articles qualified based on predetermined criteria. At the end as result, Cost-effectiveness with CPOE is more likely to be found that it is easier to reach compared to conventional methods. In addition, the acceptance of patients and health workers is also high. These factors can have a positive or negative influence on the hospital management system because developing countries still need adequate resources so that they can run these methods. Discussion and conclusion CPOE systems can improve patient safety by detecting drug interactions and actions. It is necessary to develop medical records in order to provide effective financing and acceptance.

Determination of MSMEs Business Feasibility Decisions using the Profile Matching Method

Salsa Bila Jihan Maulidah — 2024-07-01

Micro and Medium Enterprises (MSMEs) in Bali contribute to the local economy. When operating a business, it is crucial to evaluate the viability of MSME enterprises to enhance the calibre of business offerings and services. Nevertheless, the lack of competence to establish the parameters or criteria for evaluating the viability of a firm poses challenges for MSMEs in decision-making. This study presents a business feasibility assessment model utilising the Profile Matching method to aid in resolving issues and supporting Micro, Small, and Medium Enterprises (MSMEs) in making informed decisions for the long-term viability of their businesses. This study examines the feasibility of MSME businesses using the Profile Matching method. The method involves assessing 13 criteria and selecting from 10 alternatives. The process includes determining initial and target values, weighting criteria, grouping core and secondary factors, calculating total values, and ranking. The final results indicate which MSMEs are feasible and which ones require further evaluation. According to the calculations using the Profile Matching method, MSME 5 has a value of 27.80, indicating its feasibility.

Analysis Of Improving Service Quality At The Ssctelkom Surabaya Institute Of Technology Using The Lean Six Sigma Method

Ahmad Nur Rosyid — 2024-07-01

Student Service Centre (SSC) is a center that provides services and information to active students at InstitutTeknologi Telkom Surabaya (ITTS). ITTS provides SSC with academic, student, and faculty services to support its students' academic and non-academic development. One of the main services provided by SSC is the Active Certificate. However, SSC users need help obtaining the letter. This study aims to measure the quality of Active Certificate services using the Lean Six Sigma method and provide recommendations for improvement. The results showed that the quality of SSC services still needs to be improved, with a DPMO value of 289686, a sigma value of 2.07, and the highest negative gap in the Responsiveness dimension. The total Non Value Added time was obtained at 10 hours 31 minutes, and the total Value Added time was 4 hours 8 minutes. Proposed improvements include the deployment of QR Codes to provide information on document requirements and using Value Stream Mapping (VSM) to reduce the time spent on non-value added. Lean Six Sigma method can reduce the total value-added time and improve the efficiency of SSC services.

Implementing Moving Average Forecasting System for Apparel Sales: Predicting Inventory Needs with Enhanced Accuracy

Ivfa Tut Tazkiyah — 2024-07-01

Forecasting the supply of goods is one of the company's planning strategies to increase sales. However, there are several obstacles in forecasting the supply of goods in one of the boutiques in Jember Regency such as manual sales data collection, namely by recording clothing sales data in the sales book. So that there can be errors in predicting the supply of goods in the future. The purpose of this study is to apply a clothing sales forecasting system using the moving average method to forecast the supply of goods. This study applies the waterfall model to build a system with stages of analysis, design, implementation and testing. Analysis will be carried out by collecting data related to system requirements through observation, interviews and literature studies. While at the design stage there are usecase diagrams and system flow diagrams. Furthermore, the implementation stage was carried out in boutiques in Jember Regency by piloting the boutique owners. System testing uses black box testing to ensure there are no system functional errors. The findings show that the system in the form of a website can be run properly and can be accessed as long as there is an internet network. In addition, our system is already running well based on the results of black box testing. So that this system can be used by companies as forecasting considerations in providing inventory of goods.

Deployment of Web-Based YOLO for CT Scan Kidney Stone Detection

Adnin Ramadhani — 2024-07-01

This research aims to develop a kidney stone object detection system using machine learning techniques like YOLO and object detection, integrated into a Flask-based web interface to support early diagnosis by medical professionals. The trained model demonstrates strong pattern learning capabilities. Evaluation of the public dataset model reveals an average mean Average Precision (mAP) of 0.9698 for 'kidney stone' labels. This detection model exhibits high performance with an accuracy rate of 96.33%, precision of 96.98%, recall of 99.23%, and an F1-score of 98.1%. Clinical data evaluation shows that the YOLOv5-based detection system performs exceptionally well, with an average mAP of 0.9571, accuracy of 93.06%, precision of 95.71%, recall of 97.1%, and F1-score of 96.49%, indicating the model's capability to detect kidney stones with high precision and accuracy. Thus, both the evaluation on the public dataset and clinical dataset performance support accurate diagnosis processes and further treatment planning. Moreover, this research advances to the stage where the detection model can be directly utilized through implementation via Flask web deployment.

Innovative Design of ITTS Mart Application with Design Thinking & System Usability Scale Method

Habib Mirza Alfansuri — 2024-07-01

Including ease of accessing the internet through mobile devices. The emergence of social media applications, such as virtual friend applications, has also played a role in increasing the number of Internet users, primarily through mobile devices. In addition to functioning as a forum for virtual friends, social media also acts as a means of promotion, one of which is to promote online shopping applications, which contribute to an increase in online shopping transactions in Indonesia. One of the strategic choices taken is to use online shopping platforms to market educational institutions' products in the hope that they can make it easier for customers to shop and stimulate significant growth. Design thinking is used in idea formulation and problem-solving. As for creating applications that describe the emotional desires of users, this research uses the Kansei Engineering approach. Data collection was conducted through questionnaires, interviews, and literature studies. Later, it will generate several selected Kansei Words. Furthermore, to determine the best design that suits user needs, application prototypes are tested through Performance Metrics tests to determine the level of Effectiveness, efficiency, and errors, as well as performance and usability evaluations using System Usability Scale (SUS) questionnaires.

Designing Claim Systems in Health Insurance Companies with Microservices and Event-Driven Architecture Approach

Steve Sentosa — 2024-07-01

Through digital transformation, insurance companies, especially in the health sector, are increasingly adopting modern technologies to enhance efficiency and service quality. Health insurance allows individuals or families to mitigate the financial risks associated with high and unexpected medical expenses. One crucial area is insurance claim, where a fast and accurate process is key to customer satisfaction. This study proposes the design and architecture of an insurance claim system using a microservices and event-driven approach. This approach enables insurance companies to break down applications into separate components, facilitating scalability, flexibility, and easier maintenance. Additionally, with an event-driven approach, the system can quickly respond to changes and events in the business environment. A comprehensive analysis shows that implementing microservices and event-driven architecture in the insurance claim system can enhance overall system performance, scalability, and resilience. For insurance companies, adopting microservices and event-driven architecture can lead to increased operational efficiency, reduced time to market for new products, and improved customer experiences through faster claim processing. Policyholders will benefit from quicker claim resolutions and a more transparent and responsive claim process. This study provides valuable insights for health insurance companies looking to upgrade their IT infrastructure to meet future challenges. The findings from this research will be documented to support the development of insurance business technology, specifically for health insurance claims in Indonesia.

A Modification Depth First Search (DFS) Algorithm for Troubleshoot Rotating Equipment Diagnosis

Gellysa Urva — 2024-07-01

Rotating Equipment has a role in the industrial production process. There are times when the equipment that is being operated has trouble. Operators have difficulty dealing with problems of rotating equipment due to limited knowledge. To solve this problem we must have an expert with knowledge and experience. Based on this, the problem is building an expert system application to diagnose troubleshoot on rotating equipment which aims to transfer the knowledge that an expert has into the computer so that operator can find out what problems occur. This paper use Depht First Search (DFS) method, namely inward tracing techniques and Forward Chaining, namely the inference method that uses reasoning where to test a hypothesis starts from a fact. This system is equipped with an expert menu for knowledge management, so that experts can add, edit, and delete knowledge. The results showed that DFS and Forward Chaining are very suitable for diagnosing troubleshooting on Rotating Equipment. Based on the reasoning of the experts in their field and adjusted to the symptoms experienced by equipment so that the type of damage is found. It can also assist operators in diagnosing troubleshoot on Rotating Equipment so that operators can take preventive action to prevent further damage to the equipment.

UX Analysis on SpeedID Application Using Usability Testing Method and System Usability Scale

Fiqhan Arslayandi — 2024-07-01

The SpeedID application is a smart city application developed by a subsidiary of PT Bamboomedia Cipta Persada, namely PT Inovasi Solusi Nusantara since 5 years ago. The SpeedID application wants to present a solution to the city's problems to become a new digital identity for the smart city community. Because it was only developed 5 years ago, the SpeedID application is classified as a new application and has never been analyzed for usability before. Usability analysis is carried out to improve user experience, so that the SpeedID application can be accepted and used more easily by users. This research uses Usability Testing method with Performance Measurement and Retrospective Think Aloud (RTA) techniques and System Usability Scale (SUS). The results obtained are the SpeedID application has a quality that cannot be said to be effective, efficient and meet user satisfaction. In addition, the average score of the System Usability Scale (SUS) Questionnaire is 70.33. The score is rated "C" with an adjective rating of "Good" with the acceptance range included in the "Marginal" category, and finally the net promotion score (NPS) is included in the "Passive" category, which explains that the use of the SpeedID application gets an assessment that is still marginalized by its users. This shows that the SpeedID application still urgently needs correction to improve quality to its users and design improvements also need to be made so that the SpeedID application is even better at meeting user expectations in the future.

Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators

Syafira Eka Wardani — 2024-07-01

Poverty has many negative effects on people's lives, such as difficulty meeting basic needs, limited access to adequate health and education services, and limited economic opportunities. North Sumatra faces significant poverty problems as one of the largest provinces in Indonesia. This requires special attention and a thorough investigation. Reducing poverty is a very important issue for the government of North Sumatra Province. Poverty-alleviation strategies can no longer be applied uniformly. Instead, it is necessary to consider all the factors that cause poverty in each region. This means that the approach that must be given to each regency or city based on its poverty level must be adjusted. To overcome this problem, clustering must be carried out to identify areas with different levels of welfare. The aim of this research is to cluster regencies and cities in North Sumatra Province using the K-means method based on poverty indicator variables. This research only uses three poverty indicators: gross regional domestic product, human development index, and unemployment rate. The optimal number of clusters is determined based on the results of the silhouette coefficient. The research method begins with dataset collection, exploratory data analysis, data preprocessing, and k-means clustering. The value k = 6 produces a silhouette coefficient of 0.4135. This research produced six regency/city clusters. Cluster 1 consists of 11 regencies and 1 city; cluster 2 consists of 1 regency and 2 cities; cluster 3 consists of 4 regencies; cluster 4 consists of 7 regencies; cluster 5 consists of 4 cities; and cluster 6 consists of 2 regencies and 1 city. The variables gross regional domestic product, human development index, and unemployment rate have a big influence on the cluster results. This will enable the government to adopt policies to tackle poverty quickly and effectively.

Sentiment Analysis of Public Responses on Social Media to Satire Joke Using Naive Bayes and KNN

Rasyid Ihsan Putra Selian — 2024-07-01

This study examines the use of Satire Joke as a humorous communication style in conveying criticism of the government through social media. Satire Joke is often used to depict the government's inability to address important social issues, such as slow bureaucratic processes and unfulfilled political promises. The aim of this research is to analyze public sentiment towards Satire Joke expressed on the YouTube social media platform. The methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN) due to their effectiveness in data classification. The results of this study are expected to help gain an understanding of social issues for the community and public knowledge. This research is also expected to contribute to the development of sentiment analysis methods in the future. The analysis results show that 400 data have neutral sentiment, 850 data have negative sentiment, and 947 data have positive sentiment. Based on testing, both Naive Bayes and KNN methods show good performance. The Naive Bayes method achieved the best accuracy of 90.29%, while the KNN method achieved an accuracy of 60.75%.

Comparative Analysis of Machine Learning Algorithm Performance in Predicting Stunting in Toddlers

Nur’aini Indah Syahfitri — 2024-07-01

Stunting is a condition where the growth of children and toddlers is stunted, which causes children to be shorter than they should be. In the long term, stunting can reduce reproductive health, study concentration, and work productivity, thereby causing significant state losses. The prevalence of stunting in Indonesia, which is still above 20 percent, shows that there are still chronic nutritional problems among toddlers. To prevent this from happening, identification as early as possible can be done using machine learning for predictions. The aim of this research is to conduct a comparative analysis of the performance of machine learning algorithms for predicting stunting in toddlers. Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting are the algorithms that are compared for their performance. The performance of each algorithm is measured using evaluation matrices such as accuracy, precision, recall, and f1-score. The research method starts with data collection, data preprocessing, data splitting, application of machine learning algorithms, evaluation of algorithm performance, and comparison of results. The performance evaluation matrix measurement results show that Random Forest has an accuracy of 99.95%, precision of 99.89%, recall of 99.94%, and f1-score of 99.91%. K-Nearest Neighbors has an accuracy of 99.93%, precision of 99.87%, recall of 99.88%, and f1-score of 99.88%. Meanwhile, Extreme Gradient Boosting has an accuracy of 99.36%, precision of 98.86%, recall of 98.95%, and f1-score of 98.90%. From the results of all performance evaluation matrices, it can be concluded that the random forest algorithm is the best algorithm for predicting stunting in toddlers.

Enhancing Vehicle Routing Efficiency through Branch and Bound and Heuristic Methods

Ahmad Zaki Mubarak — 2024-07-01

The Vehicle Routing Problem (VRP) is a critical challenge in logistics, impacting delivery efficiency and costs. Traditional VRP solutions often fail to address real-world dynamics such as fluctuating traffic conditions and varying customer demands. This research proposes a novel VRP model integrating real-time data to enhance route optimization. By combining the precision of the Branch and Bound (B&B) approach with the flexibility of heuristics like Genetic Algorithms and Simulated Annealing, the hybrid method dynamically adjusts routes based on live traffic and demand updates. The objective is to reduce operational costs and improve logistical performance. The hybrid model’s effectiveness is validated through comparative analysis with traditional VRP solutions, demonstrating significant improvements in cost reduction, fuel consumption, vehicle wear and tear, and customer satisfaction due to timely deliveries. These advancements highlight the potential of real-time data integration and advanced optimization techniques in providing robust solutions for modern logistics challenges. Future research should focus on incorporating more advanced data sources and testing the model in various real-world scenarios to further enhance its practicality and performance, ensuring businesses remain competitive in a dynamic market. This study underscores the importance of continuous innovation in VRP solutions to achieve sustainable, efficient, and customer-centric logistics operations.

Optimization Model for Relief Distribution After Flood Disaster

Perli Pujiana — 2024-07-01

Logistics planning is critical and a key component in meeting initial emergency needs in the aftermath of a disaster. The rapid and efficient distribution of logistical aid becomes critically important. In such situations, the construction of temporary depots in strategic locations and the determination of optimal distribution routes play an important role in ensuring that logistics aid can be distributed to the affected areas evenly. In this study, the Multi Depot Vehicle Routing Problem (MDVRP) is used which aims to minimize the total cost of distributing logistics aid which includes shipping costs, vehicle usage costs, temporary depot construction costs, and vehicle travel costs from distribution centers to temporary depots, while still meeting constraints such as logistics aid demand, vehicle capacity, area visits, maximum mileage, and depot construction. This model uses two types of vehicles where vehicle is tasked with carrying logistics aid from the distribution center to the temporary depot and vehicle is tasked with delivering logistics aid directly to the point of demand.

Usability Testing of Industrial Engineering UPNVJT Website Using Eye Tracking and System Usability Scale

Bintara Putra Dicya — 2024-07-01

Websites are essential for agencies and organizations to ensure their information is accessible to the public. The eye tracker method is effective for evaluating user usability. The Industrial Engineering Study Program at Universitas Pembangunan Nasional "Veteran" East Java has an official website, tekindustri.upnjatim.ac.id, which has not been tested for usability. Initial problem identification revealed issues such as inaccessible menus and hidden information. This study aims to assess and improve the user experience on the website using a combination of Eye Tracking and System Usability Scale (SUS) methods. The average effectiveness score for 39 respondents is 89.10%, with 8% rated as ineffective, 28% as effective enough, and 64% as very effective. The efficiency value, measured across 39 respondents and 4 tasks, is 0.0276 goals/second, indicating each respondent completes 2.76% of tasks per second, requiring about 36.23 seconds to reach 100% task completion. The initial SUS score was 69.49%. Five issues were identified in the Home, Facilities, Education, Thesis, and MBKM sections and one design issue. A prototype was developed and tested, resulting in a final SUS score of 80.06%, placing the website in the marginally high acceptability range, category B for grade scale, and excellent for adjective ratings. The SUS score improvement was 10.57%. This research shows that combining Eye Tracking and SUS is an effective method for increasing website usability. The implications of this research can help organizations improve the quality of their websites and provide a better user experience.

Decision Support System for Selecting Online Teaching Methods Using the Fuzzy MCDM Algorithm

Marsono — 2024-07-01

The global pandemic that has hit the world recently has forced educational institutions to adopt online teaching methods. However, choosing an effective online teaching method is a major challenge. This research develops a Decision Support System (DSS) that uses the Fuzzy Multi-Criteria Decision Making (FMCDM) Algorithm to select the best online teaching method. This system is designed to assist decision making in educational institutions by considering various criteria such as learning effectiveness, technology affordability, ease of use, and user satisfaction. This research uses data collection methods that involve surveys from lecturers and students to obtain their preferences and experiences with various online teaching platforms. The data collected is then processed using the FMCDM model to evaluate and rank teaching methods based on predetermined criteria. Fuzzy systems are used to overcome uncertainty and subjectivity in criteria assessment. The results of this research show that the system developed is able to effectively assess and rank various online teaching methods. From the analysis carried out, interactive teaching methods using videos and real-time quizzes received the highest ranking based on predetermined criteria. This suggests that the combination of engaging visual content and high interactivity is highly valued in online teaching contexts

Student Organization Website with E-Voting Feature by Using Student Card Verification Concept Design

Yohanes Maria Jonathan Glenn Paskalis — 2024-07-01

Student organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal results

Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification

Adya Zizwan Putra — 2024-07-01

Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.

Combination of Lexical Resources and Support Vector Machine for Film Sentiment Analysis

Putri Agustina — 2024-07-01

Text data generated by internet users holds potentially valuable information that can be researched for new insights. One strategy for obtaining information from a text data set is to classify text into predetermined categories based on existing data. Text classification is an aspect of Text Mining. One of the popular approaches in Text Mining uses the Support Vector Machine (SVM) classification algorithm, which aims to classify text and separate data into different classes. However, in some cases, SVM classification algorithms may face difficulties in understanding the context of the text properly due to unclear wording, varying sentence structures, or a lack of understanding of interpretation. To address this problem, applying SVM classification using lexical resources can be an effective solution. In this research framework, the first step is to obtain data, which in this case is a film review dataset taken from the kaggle.com site. After obtaining the data, the next step is preprocessing. The results of the preprocessing are then divided into 80:20 percentages. The 80% training data is used to search for the form of polarization, and this training data lexicon is used for training the SVM model. Based on the modeling results, the overall model accuracy is around 85%, calculated using the confusion matrix. The precision value, which shows the proportion of correct positive predictions, reached 88%. The precision for negative predictions reached 80%, and for neutral predictions, it reached 0%. These results show that the Lexicon+SVM model has good performance, with an accuracy of 85%.

Indonesians Perception on the South China Sea Dispute: Support Vector Machine and Naïve Bayes Approach

Adinda Aulia Hafizha — 2024-07-01

In recent years, relations between Indonesia and China have become increasingly cordial. However, a potential source of tension is emerging in the form of a heightened dispute in the South China Sea. The government of Indonesia is considered an ally, however there has been a long-standing negative opinion among Indonesians regarding China, which has influenced the way both the general public and the political elite have perceived the relations between Indonesia and China. This research has two objectives. The first is to examine Indonesian perceptions regarding the South China Sea conflict. The second is to compare the performance of Support Vector Machine (SVM) and Multinomial Naïve Bayes as a method of sentiment analysis. Using 7.051 Indonesian-language posts from social media X as a dataset, the result shows that a significant portion of Indonesians view the dispute negatively, fearing potential escalation and threats to national security. Despite these concerns, there is reason to believe that Indonesia can play a proactive role in resolving the conflict through ASEAN and UNCLOS frameworks. Meanwhile, SVM has been demonstrated to be an effective method for handling sentiment analysis data, achieving an accuracy of 87.95%. This work contributes to the field of sentiment analysis by highlighting social media as a valuable platform and by demonstrating the effectiveness of SVM. Furthermore, the study offers new insights for the field of international relations by analyzing the South China Sea dispute through a machine learning lens, which may lead to the development of novel perspectives.

Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN)

Anita Loi — 2024-07-01

A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.

Whoosh User Sentiment Analysis on Social Media Using Word2Vec and the Best Naïve Bayes Probability Model

Muhammad Dinan Islamanda — 2024-07-01

By using the Twitter microblogging feature, users can post short tweets with limited characters that express their thoughts and opinions regarding a matter. The newest transportation in Indonesia, a high-speed train namely Whoosh is one of the things that Twitter users responded to. This latest transportation has led to the emergence of opinions from the Indonesian people which are shared publicly in various media, one of which is social media. Therefore, to make it easier for business people or companies to understand public opinion regarding service improvements in the future, sentiment analysis on social media is needed to determine user opinions regarding high-speed train transportation. In this research, sentiment analysis of high-speed train users will be carried out on social media Twitter using Word2Vec and Naïve Bayes as classification methods. In this research, a comparison of Naïve Bayes models will also be carried out to find out the best Naïve Bayes method opportunity model. Simultaneously, the Word2vec feature extraction method was chosen because Word2Vec can be used to improve model performance and increase the accuracy of sentiment classification. This research found that the Word2Vec Skip-Gram model outperformed the Word2Vec CBOW model. The best model obtained was the use of the Gaussian Naïve Bayes and Word2Vec Skip-Gram models with an accuracy score of 77.18%, precision 70.35%, recall 76.09%, and f1-score 73.10%.

Investigating the Impacts of A Simulation-Based Learning Model Using Simulation Virtual Laboratory on Engineering Students

Hanapi Hasan — 2024-07-01

Considering electrical engineering students at Universitas Negeri Medan as a case study, this research looks at how an SVL affected their grades. Integrating the TAM with the ABET Laboratory Learning Objectives, it provides a comprehensive framework for quality engineering and technology education. This research is the first of its kind to theoretically compare the two concepts in a VL context. This research examines the relationship between student performance in the classroom and the TAM's usability components as well as the ABET's learning objectives. The results from the surveys given to first-year Electrical Engineering students are analyzed using Structural Equation Modeling (SEM) and Partial Least Squares (PLS). Because it enhances student performance and satisfies their learning goals, the results demonstrate that utilizing simulation-based virtual laboratories (SVL) in engineering education offers substantial educational benefits.

Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules

Ichsan Alam Fadillah — 2024-07-01

Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.

Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine

Sava Irhab Atma Jaya — 2024-07-01

Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.

Comparison of Genetic Algorithm and Particle Swarm Optimization in Determining the Solution of Nonlinear System of Equations

Eva Mindasari — 2024-07-01

Nonlinear systems of equations often appear in various fields of science and engineering, but their analytical solutions are difficult to find, so numerical methods are needed to solve them. Optimization algorithms are very effective in finding solutions to nonlinear systems of equations especially when traditional analytical and numerical methods are difficult to apply. Two popular optimization methods used for this purpose are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This study aims to compare the effectiveness of GA and PSO in finding solutions to nonlinear systems of equations. The criteria used for comparison include accuracy and speed of convergence. This research uses several examples of nonsmooth nonlinear systems of equations for experimentation and comparison. The results provide insight into when and how to effectively use these two algorithms to solve nonlinear systems of equations as well as their potential combinations

Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression

Na'il Muta'aly Muhtar — 2024-07-05

COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.

Integration of AHP and Modified VIKOR Method to Select the Optimum Destination Route

Miranda Melania Nathasia Simbolon — 2024-07-06

One common approach to rating options is group decision making using many criteria. Here, we use the same criteria to evaluate each option. Sometimes, decision makers are faced with some situations where they have to choose from a set of alternatives that have several different criteria. Thus, the decision maker cannot use a common method. Therefore, in this research, a modification to a method is carried out. To address the issue of developing alternate routes to Medan City's historical tourism attractions, the AHP and VIKOR approaches have been suggested. When considering options with both specific and broad requirements, this study adapts the VIKOR technique to find a workable solution. In order to demonstrate the suggested model's use and evaluate the efficacy of this approach change, this study offers numerical examples based on case studies. The findings demonstrate that the revised approach is both practical and efficient.

Simulation and Modelling of Pre-emptive Priority CPU Scheduling Algorithm

Tri Dharma Putra — 2024-07-08

A model is a representation of an idea, thing or object in a simplified form. Model contains information about a system that is created with the aim of studying the actual system. Simulation is an imitation of system using a computer model. In this journal simulation is done by using OS-SIM, an operating system simulator. Process scheduling in an important part in operating systems. Several scheduling algorithms exist in the field. Shortest job first, round robin, first come first serve, priority and all of their variants. In this journal discussion about pre-emptive priority scheduling algorithm is presented thoroughly. Pre-emptive priority scheduling algorithm is an algorithm based on priority. The higher the number of the priority, the higher the priority. Five processes are available and given. Each with burst time, priority and different arrival times. Simulation and modelling with OS-SIM are discussed to understand this algorithm more easily. Some statistics numbers in the system are calculated automatically by the OS-SIM. Some screen shot pictures of the simulator are given to describe the model. It is concluded that for these processes the average turnaround time is 42/5 = 8.4 ms and for average waiting time is 28/5=5.6 ms and the total burst time is 14 ms.

Augmented Reality Learning Media Application In Computer Networking Courses

Novi Hendri Adi — 2024-07-10

In computer network learning, there is still little use of media which has an impact on students' understanding of device material and computer network topology. Augmented Reality (AR) based learning media can answer these problems by providing dynamic visualization and interactive simulations. The research objective is that AR applications can be used to help visualize abstract concepts for understanding and structure of an object model. The development method used is MDLC (Multimedia Development Life Cycle) which consists of six stages, namely concept, design, material collecting, assembly, testing, and distribution. The results of the AR application research show that the value of the learning media application in terms of material is declared valid at 0.85 and in terms of design it is declared valid at 0.86. The AR application was also stated to be very practical, this can be seen from the responses of lecturers and students with the practicality of the learning media application being 87% as seen from ease, motivation, attractiveness, and usefulness. From the results of this research, the AR learning media application is very practical to apply to students, especially in computer networking courses.

Analyzing Public Sentiment Towards BSI Service Disruptions Through X: Naïve Bayes Algorithm

Yudhistira Yudhistira — 2024-07-10

Disruptions to banking services can negatively affect customer trust and happiness, thus affecting the bank's reputation in the eyes of the public. Analysis of sentiment expressed on social media is very important because it can provide a direct picture of individual perceptions and responses in real time. This research aims to analyze public sentiment towards disruptions in Bank Syariah Indonesia (BSI) services through social media using the Naive Bayes algorithm. Through this analysis, the research seeks to understand the pattern of public responses and perceptions of BSI disruptions and evaluate the performance of the Naive Bayes algorithm in classifying sentiment on related tweet data. The data used came from specific social media platforms, where sentiment analysis was conducted by categorizing the data into positive, negative, and neutral categories. The research findings show that the sentiment analysis of the community towards BSI service disruptions through X social media platforms shows a diverse pattern of responses and perceptions. This finding recorded 525 data points with negative sentiment, 325 data points with neutral sentiment, and 141 data points with positive sentiment. The research also compared the performance of the Naive Bayes algorithm with the Google Cloud Natural Language API, which showed an accuracy rate of 81.03%. This research provides valuable insights for Bank Syariah Indonesia in understanding public perception of BSI services on social media.

Implementation of Random Forest Algorithm for Graduation Prediction

Fajar Riskiyono — 2024-07-10

University also has responsibility for the period of study taken by students in accordance with the level of education taken. The prediction of student study duration is designed to support the study program in guiding students to graduate on time. In this problem, data mining techniques can be applied to make predictions, namely by using the Random Forest classification method. The stages used in this study are data collecting, namely collecting student data, the data selection stage of 300 students with 5 (five) input data attributes including personal data (gender, age, marital status, job status) and academic data (grade) and 1 (one) attribute as an output containing choices about on time and late. The next stage is preprocessing with the aim of eliminating duplication, noise, and missing values, the stage of data transformation by normalizing age attributes (young and old), grade (large and small). Then the data split stage 3 times, namely 50/50, 40/60, and 30/60, the modeling stage with random forest, and finally, the evaluation stage by analyzing the confusion matrix consisting of accuracy, precision, and recall. The results of the study show that the proposed model can do well with predictions, that is, with the same results for all three data splits. The test value is 100% accuracy, 100% recall, and 100% precision. With this value, the success rate for predicting the timeliness of student graduation will be more accurate

Comparison of K-Means and K-Medoids Clustering Algorithms for Export and Import Grouping of Goods in Indonesia

Hazrul Anshari Ulvi — 2024-07-08

International relations affect the economic growth of each country, which can affect the economic growth of each country. As a result, global economic growth is necessary, which means that the global economy has a greater capacity to produce goods and services. Exports and imports are very important to drive economic growth. but if exports and imports are not balanced, it will have a bad impact if the value of imports is greater than exports, export prices abroad will definitely fall. An analysis comparing export and import categories is needed to determine which goods are most imported and exported in Indonesia in 2021-2023. This study uses a quantitative methodology and machine learning methods, namely k-means and k-medoids algorithms. These two methods will be compared to determine which is the most effective for export and import data of goods in Indonesia in 2021-2023. The results of the study were obtained by K-Means more effectively in handling data on the grouping of exports and imports of goods in Indonesia in 2021-2023. The dataset shows the results of the evaluation of K-Means using DBI of 0.59, while the results of the evaluation using K-Medoids show a result of 1.7868. Because the evaluation value of K-Means has low computing performance compared to K-Medoids. The largest amount of the value and weight of exports and imports of goods in Indonesia is in C1 where in the HS code [27], namely Mineral fuels with a total export value of goods in 2021 to 2023 of 134,999,470,522 US$ and a total import value of 113,714,568,740 US$. Meanwhile, the total export weight of goods from 2021 to 2023 in mineral fuel goods is 1,505,006,250,327 Kg or around 1,658,985,413 tons and the total import weight is 186,446,782,134 Kg or around 205,522,397 tons.

Deep Learning Approach for Traffic Congestion Sound Classification using Circular Neural Networks

Muhammad Ariq Muthi — 2024-07-15

Traffic congestion has become one of the main problems that occur in big cities around the world. Traffic congestion also has a negative impact if not handled seriously. Traffic congestion occurs because there is a buildup of vehicle volume that exceeds the capacity of the road. The efficiency and quality of living in cities can be negatively impacted by traffic congestion, which can also result in higher fuel consumption, pollution, and delays. There needs to be a method that can overcome and identify this. Therefore, by classifying sounds, this research aims to reduce traffic congestion. The author uses deep learning with the Convolutional Neural Network (CNN) method as the algorithm model. The model employs Mel-Frequency Cepstral Coefficients (MFCC) as the primary feature extraction technique to capture the essential characteristics of the audio signals. This research is expected to be able to classify traffic congestion sounds with good accuracy, so it can be used as a solution to overcome traffic congestion. Experiments were conducted using a training dataset, and for testing, the road sound dataset has been collected at traffic light intersections. To evaluate the proposed method, the implementation showed promising results, achieving an accuracy of 97.62% on the training data and 88.19% on the test data in classifying traffic congestion sounds.

A CNN Model for ODOL Truck Detection

Nurul Afifah Arifuddin — 2024-07-15

This study developed a Convolutional Neural Network (CNN) model as one of artificial intelligence method to detect trucks experiencing over-dimension and over-loading (ODOL). The primary goal of this research is to enhance the efficiency of truck monitoring, reduce road infrastructure damage, and support the sustainability of transportation using artificial intelligence approaches. The model was trained using a dataset consisting of ODOL and non-ODOL truck images, and successfully achieved a testing accuracy of 94.23%. The confusion matrix analysis demonstrated the model's ability to classify trucks with high precision. Additional testing on truck images not included in the training or testing dataset showed the model's potential for good generalization.

Decision Support System Using the TOPSIS Method in New Teacher Selection

Dedek Indra Gunawan Hts — 2024-07-16

Every school needs teachers who have good competence to educate students to become outstanding students. Getting teachers who have good competence is certainly not an easy thing, it must be a very strict selection process. This research aims to help determine teachers who are eligible to be accepted at IT Al Munadi Private Elementary School Medan by using the TOPSIS method. The selection consists of 5 criteria, namely education, microteaching, teaching experience, tahsin and memorization of the Koran. The TOPSIS method is widely used for Multi Attribute Decision Making (MADM) decision making. The TOPSIS method is used as a ranking to see teachers who have competencies that are worthy of acceptance. Based on the results of the TOPSIS calculation where there are 6 alternatives that have been determined, the results obtained are G6 in the first place with a preference value of 2.82, 2nd place with a preference value of 2.48, 3rd place with a preference value of 2.09, 4th place with a preference value of 1.72, 5th place with a preference value of 1.67, while the 6th place is G1 with a preference value of 1.00. It is hoped that the decision support system using TOPSIS can help schools in determining teachers who have good competence so as to produce outstanding students.

Classification of Breast Cancer with Transfer Learning on Convolutional Neural Network Models

Bayu Angga Wijaya — 2024-07-18

Breast cancer is a serious medical condition and a leading cause of death among women. Early and accurate diagnosis is crucial for improving patient outcomes. This study explores the use of Convolutional Neural Networks (CNNs) with Transfer Learning using DenseNet121 and ResNet50 models to enhance breast cancer classification via mammography. Transfer Learning enables CNN models to leverage knowledge learned from larger datasets such as ImageNet to improve performance on specific breast cancer datasets. The dataset comprised medical images with three breast variations: benign, malignant, and normal, totaling 531 data points. Data was split with a 70% training and 30% validation ratio. Two CNN models, AlexNet and ResNet50, were evaluated to compare their performance in classifying these breast cancer types. The experimental results show that AlexNet achieved a training accuracy of 98.01%, while ResNet50 achieved 64.07%. AlexNet demonstrated superior performance in identifying complex patterns in mammography images, resulting in more accurate classification of different breast cancer types. These findings highlight the potential of deep learning applications to support more precise and effective medical diagnostics for breast cancer. This research contributes significantly to the development of AI technologies in healthcare aimed at improving early detection of breast cancer. The implications of this study could expand our understanding of Transfer Learning applications in medical contexts, driving further advancements in this field to enhance patient care and prognosis

Sales Trend Analysis With Machine Learning Linear Regression Algorithm Method

Alwidahyani Sipahutar — 2024-07-19

The development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process.

Sentiment Analysis Of Indonesian State Army Police Neutrality Sentiment Towards The 2024 Election On X Using The Support Vector Machine Algorithm

Muhammad Yudha Pratama Yudha — 2024-07-24

The accompanying goals are created: One method for figuring out the order of feeling examination in the balance of military police towards the 2024 political decision depends on popular assessment in SVM technique in arranging opinion investigation in the balance of military police towards the 2024 races in light of 2024 general assessment in X. In a leading examination, the stages utilized are the exploration system. This was finished to coordinate the exploration stages. The technique of this examination is quantitative. An exploration area is where a specialist completes research, particularly in catching peculiarities or examination that really happens at the exploration area to get precise and genuine examination information. The consequences of the testing did were to decide the capacity of the framework that had been made to complete feeling investigation on opinion towards the lack of bias of the TNI and Polri during the political race Research begins with compiling, specifically determining the points to be discussed. The subject of this research is the execution of message mining in testing the balance of military police feelings towards the 2024 political decisions in X using the Help vector machine1 algorithm. Tweet Information Collection,In this review, scientists utilized 800 tweet information.. The consequences of the opinion examination did will be introduced as a disarray framework, where through the disarray network and characterization report the degree of exactness of the exploration that has been completed can be determined.It is trusted that the aftereffects of this assessment can give a thorough image of the public's discernment on Twitter with respect to the lack of bias of the TNI and Polri in sorting out races.