Sinkron : jurnal dan penelitian teknik informatika

Design of Intelligent Model for Text-Based Fake News Detection Using K-Nearest Neighbor Method

Hari Murti, Sulastri, Dwi Budi Santosa, Dwi Agus Diartono, Kristiawan Nugroho — Wed, 08 Jan 2025 00:00:00 +0000

Text-based fake news detection is a crucial issue considering its negative impacts on society and individuals. One of the main impacts that has a significant and detrimental impact on society is disinformation, where false or misleading information can cause confusion and uncertainty in society. This can lead to misunderstandings and develop into riots in society which can lead to legal problems that are detrimental to society. In order to overcome this problem, a method is needed to detect fake news. This study aims to build a fake news detection method using machine learning, which is a technology widely used by researchers to detect and analyze past data. Various methods have been produced using machine learning, including the K-Nearest Neighbor (K-NN) method which is proposed as an effective solution to detect fake news. K-NN is a machine learning algorithm that works by classifying text based on its proximity to known data in feature space. This method is proposed because of its ability to handle non-linear data and its low complexity. The application of K-NN can increase the accuracy in detecting fake news by utilizing the characteristics of relevant text, thus helping in efforts to filter information and maintain the integrity of news circulating in the community. In a study conducted using the FakeNewsDetection dataset, the model evaluation results showed that KNN produced a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.077, better than the performance of other methods such as SVM and Neural Network.

Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management

Wed, 08 Jan 2025 00:00:00 +0000

In the digital era, ensuring customer satisfaction with IT services is crucial for business success. However, the complexity of IT infrastructure makes it difficult to manage services, requiring companies to focus on improving efficiency and reducing operational costs. One of the strategies used is Information Technology Service Management (ITSM), the main component of which is incident management, which aims to minimize service disruptions. While various studies on ITSM exist, research focused on Machine Learning models for predicting incident resolution times is relatively limited. This research aims to develop an incident resolution duration prediction model using a Random Forest Regressor-based regression approach. The dataset used is an event log from the ServiceNow system containing data on 24,918 incidents. The model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics, where the model achieved a MAE of 14.33 hours, RMSE of 69.8 hours, and R2 of 0.98. These results show that the model can provide accurate predictions and support better decision-making in IT incident handling. Time-related features, such as sys_update_month and closed_month, proved to be the most influential factors in predicting incident resolution duration.

Detection of Plastic Bottle Waste Using YOLO Version 5 Algorithm

Jamilatur Rizqil Yasiri, Rastri Prathivi, Susanto — Wed, 08 Jan 2025 00:00:00 +0000

Plastic bottle waste management has become one of the most pressing environmental issues, especially in countries with high plastic usage rates, such as Indonesia. This research uses the YOLOv5 (You Only Look Once version 5) algorithm to detect plastic bottle waste automatically. The YOLOv5 algorithm was chosen because it has efficient detection performance and high accuracy in small object recognition. The dataset consists of 500 images of plastic bottles obtained through cameras and internet sources. The data is processed through several stages: annotation (bounding box and labeling using Roboflow), split dataset (70% for training, 20% for testing, and 10% for validation), pre-processing (resizing images to 460x460 pixels), and augmentation (adding data variations to improve model performance). Training and evaluation of the YOLOv5 model using the precision metric of 89.8% indicates the ability of the model to accurately identify plastic bottles from the overall prediction, recall of 83.1% indicates the success of the model in detecting the majority of plastic bottles in the test data, and mean average precision (mAP) of 89.2% represents the average precision at various prediction thresholds. Test results on varied bottle image test data obtained detection accuracy between 82%-93%, indicating the model can recognize plastic bottles consistently. Sometimes, this model needs help detecting overlapping picture objects. However, this research proves the potential of the yolov5 algorithm as an automated litter detection solution that will be integrated with a system and support faster and better plastic waste management.

Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization

Fatih Fauzan Kartamanah, Aldy Rialdy Atmadja, Ichsan Budiman — Mon, 06 Jan 2025 00:00:00 +0000

Text summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content.

Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation

Wed, 08 Jan 2025 00:00:00 +0000

Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizontal flipping, rotation, and zoom transformations to address dataset limitations. Our methodology incorporates transfer learning through ImageNet pre-trained weights and custom layer modifications to optimize the MobileNetV2 architecture for batik-specific features. The model achieves 100% accuracy on validation data, with precision, recall, and F1-scores consistently above 0.98 across all classes. The confusion matrix analysis reveals minimal misclassification between similar motif patterns, particularly in the Batik Blekok Warak and Batik Kembang Sepatu classes. This research contributes to cultural heritage preservation by providing an efficient, resource-conscious solution for automated batik pattern recognition, potentially supporting educational and commercial applications in the batik industry.

Leveraging Label Preprocessing for Effective End-to-End Indonesian Automatic Speech Recognition

Wed, 08 Jan 2025 00:00:00 +0000

This research explores the potential of improving low-resource Automatic Speech Recognition (ASR) performance by leveraging label preprocessing techniques in conjunction with the wav2vec2-large Self-Supervised Learning (SSL) model. ASR technology plays a critical role in enhancing educational accessibility for children with disabilities in Indonesia, yet its development faces challenges due to limited labeled datasets. SSL models like wav2vec 2.0 have shown promise by learning rich speech representations from raw audio with minimal labeled data. Still, their dependence on large datasets and significant computational resources limits their application in low-resource settings. This study introduces a label preprocessing technique to address these limitations, comparing three scenarios: training without preprocessing, with the proposed preprocessing method, and with an alternative method. Using only 16 hours of labeled data, the proposed preprocessing approach achieves a Word Error Rate (WER) of 15.83%, significantly outperforming the baseline scenario (33.45% WER) and the alternative preprocessing method (19.62% WER). Further training using the proposed preprocessing technique with increased epochs reduces the WER to 14.00%. These results highlight the effectiveness of label preprocessing in reducing data dependency while enhancing model performance. The findings demonstrate the feasibility of developing robust ASR models for low-resource languages, offering a scalable solution for advancing ASR technology and improving educational accessibility, particularly for underrepresented languages.

Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency

Wed, 08 Jan 2025 00:00:00 +0000

Stunting, characterized by impaired growth and development in children, is one of the most serious public health problems often caused by chronic malnutrition. This study aims to identify patterns among stunting cases through clustering analysis of child health data. The algorithm used in this research uses K-Means. The dataset used in this study uses health data from 599 children in the Sambas Regency area of East Kalimantan Province. This dataset has several features that are quite diverse such as height, weight, age, nutritional intake, socioeconomic status, and others. This research process begins with cleaning the data, as well as looking at the correlation between features. One of the methods used is to conduct a data analysis process using Principal Component Analysis (PCA) which aims to reduce the dimensions of the data. After that, the process of finding the number of clusters using the Elbow method is carried out to determine the optimal number of clusters. This research uses 4 clusters in the process. The clustering results revealed that family structure (main family vs extended family) and parental income levels significantly influence stunting prevalence in the region.

Maturity Level Analysis of SPBE Service Domain Using Capability Maturity Model Integration at the Kominfo Palembang City

Nyimas Hamidah Purnama Agustriani, Titah, Tata Sutabri — Mon, 06 Jan 2025 00:00:00 +0000

This journal aims to analyze the maturity level of the implementation of Electronic-Based Government Systems (EBS) in the service domain at the Communication and Information Technology Office of Palembang City. This research uses the Capability Maturity Model Integration (CMMI) approach to evaluate processes, identify weaknesses, and provide recommendations for improvement. CMMI was chosen because it can measure process effectiveness and help organizations achieve optimal performance. The research was conducted using survey and interview methods to collect data related to SPBE implementation. The collected data was analyzed using the CMMI framework to determine the maturity level from level 1 (Initial) to level 5 (Optimizing). The results of the analysis show that the maturity level of the SPBE service domain at the Communication and Information Technology Office of Palembang City is at level 3 (Defined) with a maturity level value of 3.66 from a recapitulation of a value mapped to each process area: OPF, OPD, MA, CAR and PPQA. Some areas need to be improved, especially related to lack of clearly defined and consistently applied standard operating procedures (SOPs) leads to variations in service delivery and hampers the overall effectiveness of SPBE implementation, performance monitoring mechanisms such as tracking and evaluation of service delivery outcomes are inadequate which makes it difficult to assess the effectiveness of SPBE services, existing systems are not fully compatible or lack the necessary features to support technology integration within the SPBE framework leading to inefficiencies and failure to leverage technology to improve public services. This research contributes by providing strategic recommendations to improve the maturity of SPBE implementation at the Communication and Informatics Office of Palembang City. The recommendations given are increasing the capacity of human resources, consistent application of standard operating procedures (SOPs), and the use of more integrated technology to support more effective and efficient services. The results of this study are expected to serve as a guide for the Communication and Information Technology Office of Palembang City in implementing SPBE more optimally.

Food and Physical Activity Tracking Application with Simple Dietary Pattern Analysis

Radinal Setyadinsa, Ulka Chandini Pendit, Novi Trisman Hadi — Thu, 02 Jan 2025 00:00:00 +0000

This study focuses on developing a mobile application for tracking food intake and physical activity, designed to provide users with a simple dietary pattern analysis. With growing interest in health monitoring tools, the primary objective was to create an accessible application that allows users to log meals, record physical activity, and receive feedback on caloric balance, without requiring advanced computational resources. The application was developed using Agile methodology, with stages in requirements gathering, system design, incremental development, and user testing. Key features include interfaces for logging food and activity data, a dashboard for visualizing caloric intake and expenditure, and feedback that helps users understand their dietary patterns. Data collected during a one to two-week testing phase was analyzed qualitatively and quantitatively to evaluate ease of use, accuracy of data logging, and clarity of feedback. Results indicated high user satisfaction, with users finding the app intuitive and valuable for understanding their eating and activity habits. However, limitations were noted, including a focus solely on caloric balance without detailed macronutrient breakdown, and lack of integration with wearable devices, which could enhance tracking accuracy. Future developments are recommended to include macronutrient analysis, compatibility with fitness wearables, and engagement features like goal-setting and reminders. The findings suggest that a simple, user-friendly app can effectively promote better health awareness, although further improvements could broaden its applicability and impact.

Median-Average Round Robin (MARR) Algorithm for Optimal CPU Task Scheduling

Rakhmat Purnomo, Tri Dharma Putra — Wed, 08 Jan 2025 00:00:00 +0000

Abstract: In operating systems, multitasking or multiprocessing terms are used. If more than one task operating consecutively, but the users feel that they are running simultaneously, than it is called multitasking. Round robin algorithm is a noted algorithm in multitasking. Several modifications of classical round robin algorithm have been proposed by experts. The idea behind these modifications are to get lower turnaround time and lower waiting time. The main topic’s discussion is about median-average round robin (MARR) algorithm. In this algorithm, the processes are arranged in ascending order. Then we get the median of the burst time. Afterwards, calculation of the average burst time is done. The summation of average and median, divide by two is the time quantum. So, the time quantum will be dynamic, based on each iteration of round robin. First iteration can have different time quantum compared to the second and so on. Each iteration will have one time quantum. Three analysis’s are given. Each with five processes. In the first analysis, time quantum for 1^st iteration is 11 and the 2^nd iteration is 4. The average turnaround time is 29. The average waiting time is 19. For the second analysis, time quantum for 1^st iteration is 10 and the 2^nd iteration is 8. The average turnaround time is 24.2. The average waiting time is 13.6. For the third analysis, time quantum for 1^st iteration is 10 and the 2^nd iteration is 9. The average turnaround time is 23.2. The average waiting time is 12.8.

Comparison of ARIMA and GRU Methods in Predicting Cryptocurrency Price Movements

Wed, 08 Jan 2025 00:00:00 +0000

This study compares the effectiveness of the ARIMA and GRU models in predicting Bitcoin price movements, addressing the need for reliable predictive tools amidst the high volatility of the cryptocurrency market. Previous research has highlighted the strengths of each model in financial forecasting: ARIMA for short-term, stationary data and GRU for capturing complex temporal patterns. The purpose of this study is to evaluate which model performs better in the context of Bitcoin price prediction, offering insights for investors to minimize risks and enhance decision-making in this unpredictable market. The research methodology involves applying both models to Bitcoin price data and comparing their accuracy using the Mean Absolute Percentage Error (MAPE) across various forecasting intervals. Results indicate that GRU achieves higher accuracy in long-term forecasts, while ARIMA performs optimally for shorter time frames. However, both models demonstrate limitations, especially as the prediction horizon extends, underscoring the inherent challenges of cryptocurrency price forecasting. These findings suggest that GRU may be better suited for longer investment horizons, while ARIMA remains effective for short-term predictions. The conclusions affirm the potential of using these models selectively to align with specific investment strategies in cryptocurrency markets, although further research is recommended to improve predictive accuracy under evolving market conditions.

Stock Price Prediction Using TCN-GAN Hybrid Model

Lim Yong Teck, Angelina Pramana Thenata — Wed, 08 Jan 2025 00:00:00 +0000

The stock market plays a vital role in national economies, offering significant profit opportunities for investors while exposing them to substantial risks due to market uncertainties. PT Indo Tambangraya Megah Tbk (ITMG), one of Indonesia’s largest coal mining companies, also faces these challenges, with its stock price experiencing significant fluctuations since its listing on the Indonesia Stock Exchange (IDX). Temporal Convolutional Network (TCN) and Generative Adversarial Network (GAN) are the deep learning method proposed for this research. The aim of this research is to evaluate the performance of the TCN-GAN model in predicting stock prices, specifically on ITMG. Previous researches show both TCN and GAN perform well on time series data. TCN excels in analyzing time-series data while GAN enhances training by generating realistic simulations. By combining the strength of both models, this approach aims to enhance stock price prediction accuracy. The proposed model employs TCN as the generator within the GAN framework and a Multilayer Perceptron (MLP) as the discriminator. TCN handles the prediction task and is trained using the GAN model. The model is trained over 500 epochs, with a learning rate of 0.0004 for the generator and 0.0001 for the discriminator. During each epoch, the generator is updated twice to enhance its performance. The resulting model achieves a MAPE score of 2.16% and an RMSE score of 814.25 on the testing dataset, demonstrating excellent performance on ITMG stock price predictions, despite the significant price range.

Parameter Testing on Random Forest Algorithm for Stunting Prediction

Wed, 08 Jan 2025 00:00:00 +0000

Stunting is a significant public health problem, especially in developing countries like Indonesia. It is often caused by chronic malnutrition in the first 1,000 days of life, which can impact a child's physical growth and cognitive development. To find risk factors and find effective solutions, data analysis was conducted by utilising machine learning to predict stunting. This research uses the Random Forest algorithm with a focus on setting parameters such as n_estimators, max_depth, and the number of features to optimise model efficiency and accuracy. Using the 2023 Indonesian Health Survey data consisting of 25,800 data, this study managed to get the highest accuracy of 91.65% by a combination of Random Forest with parameter settings n_estimators 200, max_depth 30, and Synthetic Minority Oversampling Technique (SMOTE). Despite the high accuracy results, there are limitations such as potential noise coming from synthetic data from SMOTE and the limited number of features analysed. It is hoped that this research can contribute to the field of machine learning model development that is practically used to predict stunting, and support the government's efforts to reduce the stunting prevalence rate to 14% as targeted. This model also provides strategic insights for policy makers to design more effective data-driven interventions, which can help in decision making.

Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform

Anjis Sapto Nugroho, Kristiawan Nugroho — Wed, 08 Jan 2025 00:00:00 +0000

As of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality.

Implementation of LSA for Topic Modeling on Tweets with the Keyword ‘Kemenkeu’

Wed, 08 Jan 2025 00:00:00 +0000

This research explores public discourse on financial policies by analyzing tweets mentioning the keyword 'Kemenkeu' (Ministry of Finance). Using Latent Semantic Analysis (LSA), the study examined 10,099 tweets to uncover key topics that reflect public sentiment toward the Ministry’s policies. Preprocessing steps, such as stopword removal and stemming with Sastrawi, were essential to ensure the effectiveness of the analysis. The results revealed three main topics: Finance and Budget, Salaries and Employee Welfare, and Excise and Customs Regulations. These insights provide a better understanding of public opinion on financial issues and highlight the importance of proper text preprocessing in topic modeling. This approach demonstrates how LSA can be used as a tool for analyzing large-scale social media data, offering valuable input for policymakers. Future research could expand on this by using more advanced models or larger datasets to gain deeper insights.

Usability Evaluation of Lecturer Information System in ITB STIKOM Bali

I Gusti Made Surya Dwipayana, I Made Candiasa, Luh Joni Erawati Dewi — Wed, 08 Jan 2025 00:00:00 +0000

This research aims to provide a deeper understanding of the usability aspects of information systems, as well as help create more effective and efficient solutions in supporting academic activities in higher education. The ITB STIKOM Bali, Lecturer Information System (Sistem Informasi Dosen/ SID) is a system developed to assist lecturers in carrying out their academic responsibilities. This system has become a very vital tool in supporting various academic activities of lecturers. This research will be conducted using the Concurrent Think Aloud (CTA) method, Performance Measurement, and System Usability Scale (SUS) to assess effectiveness, efficiency, and user satisfaction of the system. The system was found effective, with a success rate exceeding 78%. Advanced users achieved a 95% success rate, while beginner users achieved 86%, with errors primarily in navigation-related tasks. User satisfaction analysis via SUS showed skilled users rated the system at 84.75 (Grade A, Acceptable), whilst beginner respondents scored 52.5 (Grades D, Marginal Low), reflecting usability challenges for beginners. Performance Measurement highlighted issues with small font sizes and unclear navigation, while CTA identified difficulties with the logout button, lack of search functionality, unreadable interface text, and unclear functional position menus. Recommendations include increasing font size to Arial 14, redesigning the logout button, adding search bars, and enhancing functional menus to include research and community service options. These improvements aim to enhance system usability and user experience across all proficiency levels.

Thyroid Disease Prediction Using Random Forest with KNNImputer for Missing Values

Raffy Nicandra Putra Pratama, Sri Winarno, Tan Nicholas Octavian Wijaya — Wed, 08 Jan 2025 00:00:00 +0000

Thyroid disease is a health dysfunction that requires immediate and accurate diagnosis. This research seeks to design a classification model based on the Random Forest algorithm to detect the type of thyroid disease utilizing data from the UCI Repository. In the data processing stage, KNNImputer is used to handle missing data by calculating the average value of the nearest neighbors based on Euclidean distance, thus ensuring better data quality for model training. The developed model was evaluated utilizing the confusion matrix, which showed an accuracy of 98%, with precision, recall, and F1 score values reached 98% based on weighted avg.These results corroborate that the proposed model is highly reliable in detecting various types of thyroid diseases, such as Negative, Hypothyroid, and Hyperthyroid. This research makes an important contribution to the application of data mining technology for medical diagnosis, while proving that optimal data processing through methods such as KNN Imputer can significantly improve model performance.

Assessment Clusterization Teacher Performance With K-Means Algorithm Clustering And Agglomerative Hierarchical Clustering (AHC)

Rodiatun, Sri Lestari — Thu, 02 Jan 2025 00:00:00 +0000

Abstract: Research This aims to do clustering evaluation teacher performance with the application of the K-means clustering algorithm and agglomerative hierarchical clustering (AHC). Background study This is based on needs to increase quality teaching through analysis and evaluation and better teacher performance. The methods applied involving assessment data collection performance from teachers in the environment education local, processed using a second algorithm The results of the research show that the silhouette score value for K-means reached 0.364, while AHC produced a value 0.343. With Thus, K-means is proven more effective in grouping assessment data and teacher performance compared to AHC. The conclusion of the study This confirms the importance of implementation of the K-means algorithm to get more insight into good evaluation teacher performance. Author Ready to do repairs or revisions to the manuscript. This is in accordance with comments and suggestions from the reviewer as a condition beginning. For processing more, carry on.

Keywords: AHC, Clustering, K-Means, Silhouette Value

Optimizing Twitter Sentiment Analysis on Tapera Policy Using SVM and PSO

alkaaf Ahmad Al Kaafi, Suparni, Hilda Rachmi, Ahmad Maulana, Ririn Nurtriani — Thu, 09 Jan 2025 00:00:00 +0000

This study aims to analyse the sentiment of Twitter users towards the Public Housing Savings (Tapera) policy in Indonesia using the Support Vector Machine (SVM) algorithm optimised by Particle Swarm Optimization (PSO). In recent years, social media has emerged as a primary platform for individuals to express their views and opinions on public policies. The government programme, Tapera, which was designed to increase access to housing for the public, attracted considerable attention, with a range of responses, including both positive and negative sentiments. The methodology employed in this study comprised the collection of data from Twitter, the processing of text, and the application of SVM-based classification techniques, reinforced by PSO, with the objective of enhancing the accuracy and efficiency of the model. The results demonstrated that the PSO-optimised SVM model exhibited an accuracy of 85%, accompanied by an Area Under Curve (AUC) value of 0.84 and a ROC curve that indicated the model's notable capacity for differentiating between positive and negative sentiments. These findings indicate the existence of certain sentiment patterns that can be utilised for the evaluation and improvement of Tapera policies. In conclusion, this research is expected to provide a comprehensive picture of the public response to the Tapera policy and present an analytical model that can be applied to evaluate other policies. Further research is recommended to expand data coverage and develop algorithms to achieve more accurate results.

Implementation of Case-Based E-Consultation to Handle Student’s Stress Levels

Frestiany Regina Putri, Artika Fristi Firnawati , Shifa Andila — Thu, 09 Jan 2025 00:00:00 +0000

Despite the declaration of the COVID-19 pandemic's end, the effects of some cases persist in the new normal era of 2023. Several cases indicate a decline in the learning motivation of students and university students, which significantly affects aspects of understanding, creativity, productivity, and learning outcomes. University students transition from learners who, during their high school years, spent more time studying online without directly interacting with peers or teachers. One of the causes of university student dropouts is internal issues due to students' inability to adapt to the university environment. The purpose of this research is to compile cases frequently experienced by university students that cause stress and lead to the decision to discontinue their studies. This is done to find solutions and prevent similar incidents from recurring. The implementation of e-counselling helps provide solutions in the form of action recommendations on how to address student issues. We conducted the research in several stages, including data collection, literature review, modelling, model evaluation, and prototype building and testing. We obtained the solution to the collected cases from the counsellor through a focus group discussion (FGD). This research employs case-based reasoning, utilizing four reasoning processes: retrieve, reuse, revise, and retain. We chose the modified weighted average similarity function to measure the case's similarity value with the cases in the case base. Through the case-based e-counselling system, the calculation results reveal the similarity between the new case and the old cases, recommending actions that counsellors have validated as valid solutions.

Embedded Smart Farming System for Soil and Hydroponic Planting Media Based on The Internet of Things

Silfia Rifka, Ramiati, Ratna Dewi, Ummul Khair, Herry Setiawan — Thu, 09 Jan 2025 00:00:00 +0000

Smart agricultural technology by applying the internet of things (IoT) purposes to make farmers' work more efficient due to the automation system and assist farmers in monitoring the condition of their agricultural land. The focus of discussion in this research is the application of smart agriculture system technology that uses the concept of embedded systems for soil and hydroponic planting media. This system applies an automation system for water irrigation and fertilizer irrigation using four tanks, namely a water source, a water irrigation tank, a fertilizer tank, and a water circulation system in hydroponics. The system is also equipped with weather monitoring based on temperature, rainfall, and light intensity. Other parameters contained in this system are soil pH, water pH, TDS, fertilizer availability, and irrigation pump status. The monitoring system based on the Android application displays all parameters and the status of the devices used.

Usability Evaluation of GetContact Application Using Post-Study System Usability Questionnaire and Retrospective Think Aloud

Nabilah Zahirah, Dwi Rosa Indah, Mgs. Afriyan Firdaus — Thu, 09 Jan 2025 00:00:00 +0000

GetContact, aplikasi manajemen dan proteksi panggilan spam dengan lebih dari 700 juta unduhan di Google Play Store, masih memiliki ruang untuk peningkatan kegunaan. Studi ini bertujuan untuk memanfaatkan kegunaan GetContact berdasarkan ulasan Quora, komentar Google Play Store, dan wawancara dengan pengguna di area Palembang. Metode yang digunakan adalah Post-Study System Usability Questionnaire (PSSUQ) dan Retrospective Think Aloud (RTA). Hasil PSSUQ dari 190 responden menunjukkan tingkat kegunaan keseluruhan yang baik dengan skor 2,73. Skala Kegunaan Sistem mencatat nilai 2,60, dan Kualitas Informasi mencapai 2,80, menunjukkan kegunaan yang memuaskan. Namun, kualitas antarmuka dengan skor 2,89 masih perlu ditingkatkan. Temuan dari metode RTA juga mengidentifikasi kendala dalam fitur dan antarmuka aplikasi. Studi ini menyimpulkan bahwa meskipun tingkat kegunaan GetContact secara keseluruhan baik dan diterima oleh pengguna, pengembangan lebih dari antarmuka dan fitur masih diperlukan untuk meningkatkan kegunaan secara keseluruhan dan menciptakan pengalaman pengguna yang lebih baik.

Sentiment Analysis of Tokopedia App Reviews using Machine Learning and Word Embeddings

Muhammad Idris, Ahmad Rifai, Ken Ditha Tania — Mon, 06 Jan 2025 00:00:00 +0000

Tokopedia, a prominent e-commerce platform in Indonesia, generates vast amounts of user feedback, offering valuable insights into customer satisfaction through sentiment analysis. However, sentiment analysis of app reviews specifically on Tokopedia reviews remains underexplored. Sentiment analysis, also known as opinion mining, categorizes user sentiments into positive or negative, offering insights into user preferences and service shortcomings. While traditional text representation techniques like TF-IDF are widely used for sentiment analysis, they lack the semantic richness provided by advanced word embeddings such as Word2Vec and FastText, which excel at capturing contextual relationships between words. These methods have shown potential to enhance the performance of machine learning models in sentiment analysis tasks. This study evaluates the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (NB)—combined with Word2Vec and FastText feature extraction. A dataset of 59,811 Tokopedia app reviews was scraped from the Google Play Store, preprocessed, and subjected to feature extraction using Word2Vec and FastText. SVM achieved the best performance, with an accuracy of 89.06% using FastText and 89.02% using Word2Vec. RF ranked second with accuracies of 88.07% for FastText and 88.14% for Word2Vec. NB showed the lowest performance, achieving 84.26% with Word2Vec and 83.73% with FastText. Differences in performance between Word2Vec and FastText embeddings were minimal across all algorithms, highlighting their comparable effectiveness. These results underscore SVM’s consistent superiority across various configurations for sentiment analysis.

Analysis of Social Assistance to Donors at the Putra Muhammadiyah Orphanage in Medan City Using the Support Vector Machine Method

Mon, 06 Jan 2025 00:00:00 +0000

The Putra Muhammadiyah Orphanage in Medan City is a social institution that depends on donor support to fund various social programs. Effective management of social assistance necessitates a thorough understanding of donor patterns and characteristics, including the amount of donations, frequency of contributions, and types of assistance provided. This research aims to conduct a comprehensive analysis and classification of donors using the Support Vector Machine method, a powerful technique in machine learning for addressing classification challenges with high accuracy. The research data comprises donor information with several key characteristics, such as the number of donations, frequency of contributions, and types of assistance. The data undergoes preprocessing, which includes normalization and division into training and testing sets. Subsequently, the model is trained to classify donors into two categories: Potential Donors and Non-potential Donors. The evaluation results indicate that the SVM method effectively identifies donation patterns, as evidenced by a significant level of accuracy compared to other evaluation models, including precision, recall, and F1-score. These findings offer substantial benefits for managing social assistance, particularly in helping orphanages understand donor profiles and develop more efficient collection strategies based on that information. Therefore, it is anticipated that this research will positively impact the sustainability of social programs at the Putra Muhammadiyah Orphanage in Medan City.

Transforming Real Estate: Leveraging TOGAF ADM for Digital Optimization in Enterprise Architecture

Herman Widjaja, Richardus Eko Indrajit, Erick Dazki — Thu, 09 Jan 2025 00:00:00 +0000

In this research paper, we propose an Enterprise Architecture (EA) design for PT XYZ, a middle up class real estate development company in Indonesia, leveraging the TOGAF ADM framework. The study centers on optimizing five key business processes—commercial leasing, residential sales, hotel banquet rentals, waterpark ticket sales, and parking fee collection—to enhance operational efficiency and support digital transformation. Using ArchiMate modeling for clear visualization, this architecture spans from the Preliminary Phase, Phase A Architecture Vision, Phase B Business Layer, Phase C Information System Architecture (Application Layer) to the Phase D Technology Architecture. It provides a strategic blueprint to address common challenges like data fragmentation, reliance on manual processes and human resources readiness. By implementing this EA, PT XYZ can expect improvements in scalability, flexibility, and overall agility. This approach aims to position PT XYZ as a modern, digitally-driven entity, aligning technology investments with business objectives for long-term success. Future research is recommended to explore later phases of TOGAF ADM (Phase E – Phase H) and potentially integrate additional business areas for a holistic digital transformation.

Development of Mobile Application by Applying Content-Based Filtering

Nandang Hermanto, Irma Darmayanti; Dimas Saputra, Aden Hidayatuloh — Fri, 10 Jan 2025 00:00:00 +0000

The rapid advancements in information technology have transformed modern lifestyles, driving changes in consumer behavior and expectations, especially in the retail industry. This study focuses on developing a mobile application for Ampu Mart, a newly established retail business in Indonesia, to optimize product recommendation systems using the Content-Based Filtering (CBF) approach. The research integrates CBF with string matching and cosine similarity algorithms to provide personalized product recommendations based on customer preferences, enhancing user satisfaction and supporting more efficient purchasing decisions. The methodology involves several stages, including problem identification through observation and interviews, data collection on product attributes and customer preferences, system design, prototype development, implementation, and testing. The application leverages advanced algorithms to analyze product characteristics, ensuring relevant recommendations by matching user preferences with product attributes. User Acceptance Testing (UAT) conducted with 30 participants—customers, administrators, and management—evaluated the application's functionality, usability, accuracy, and performance. Results indicate that the mobile application improves the shopping experience and boosts sales by offering accurate, user-centered recommendations. The findings highlight the strategic importance of integrating intelligent technology into e-commerce platforms to enhance competitiveness in the retail market. Future work recommends incorporating Collaborative Filtering techniques to further enrich the recommendation system by analyzing collective customer behavior.

Implementing KNN to Assess the Feasibility of Using Scientific Publications as Final Assignment Substitutes

Fri, 10 Jan 2025 00:00:00 +0000

This study aims to explore the feasibility of using scientific publications as a substitute for traditional final assignments in higher education by applying the K-Nearest Neighbors (K-NN) algorithm. Traditional final assessments, such as theses, are widely used in evaluating students, but with the increasing availability of peer-reviewed scientific publications, there is potential to use them as a more dynamic and relevant assessment tool. This study uses a dataset containing scientific publications and theses, with features such as research quality, relevance, methodology, and clarity. This study applies the K-NN algorithm to classify these materials and determine whether scientific publications can serve as an effective substitute. The results show that the K-NN algorithm, using k=4, achieved 95% accuracy, successfully distinguishing between scientific publications and theses. However, some misclassifications occurred, indicating areas for improvement, such as incorporating additional features such as citation counts or peer-review scores. These findings suggest that scientific publications, if properly classified, can indeed replace traditional final assignments, encouraging critical thinking and engagement with current research. Future research should refine the feature set and explore other machine learning models to improve accuracy. The practical implications of this research are the potential to develop more innovative and relevant approaches to assessment in higher education, which are more aligned with modern educational practice.

Fraud Detection in Mobile Phone Recharge Transactions Using K-Means and T-SNE Visualization

Irwin Sakti, Arvin, Ito Wasito — Sat, 11 Jan 2025 00:00:00 +0000

The surge in digital transactions has introduced vulnerabilities in mobile recharge systems, making them susceptible to fraudulent activities that compromise financial security and operational integrity. This study presents to address these challenges by employing a novel fraud detection framework that integrates K-Means clustering and t-Distributed Stochastic Neighbour Embedding (t-SNE) visualization. This work advances the field by integrating scalable, unsupervised learning techniques with robust visualization tools, offering a practical framework for fraud detection in mobile recharge systems. Leveraging a dataset of over 200,000 transactions, this research systematically identifies anomalies indicative of fraudulent behaviour, focusing on key transactional attributes such as processing times, geographic patterns, and error frequencies. The methodology begins with data preprocessing to ensure consistency, followed by the application of K-Means clustering to partition transactions into meaningful clusters. To enhance interpretability, t-SNE visualization is employed, enabling a clear representation of high-dimensional data and the identification of anomalous patterns. A comparative analysis with Autoencoders highlights the strengths of K-Means in terms of computational efficiency, interpretability, and clustering quality, as evidenced by higher Silhouette Scores (0.6215) and lower Davies-Bouldin Index values (0.7074). The combination of K-Means and t-SNE enables service providers to identify fraudulent activities with greater precision, offering actionable insights to mitigate financial risks. This study not only addresses the critical need for robust fraud detection systems but also lays a strong foundation for future advancements through the integration of hybrid models and enhanced feature engineering, demonstrating its adaptability to similar domains.

Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear

William Watasendjaja, Billy Chandra, Ito Wasito — Sat, 11 Jan 2025 00:00:00 +0000

As the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics.

Performance Level Analysis On Learning Vector Quantization And Cohonen Algorithms

Roni Fredy Halomoan Pasaribu, Muhammad Zarlis, Erna Budhiarti Nababan — Sun, 12 Jan 2025 00:00:00 +0000

Biometric identification is an alternative for a security system that consists of physiological characteristics and behavioral characteristics. Physiological characteristics are relatively stable physical characteristics such as fingerprints, hand lines, facial features, tooth patterns, and the retina of the eye. Behavioral characteristics such as signature, speech patterns, or typing rhythm. The function of a signature is proof in a document which states that the party signing, knows and agrees to all the contents of a document. There are several stages in the signature pattern image recognition system, namely the signature pattern image is produced through a scanning process, then the resulting digital signature image is cut (scaling) manually, the next process is thresholding, edge detection, image division, and representation. input value. The method used in recognizing signature patterns is the learning vector quantization (LVQ) artificial neural network method and kohonen self-organizing map (SOM). In Learning vector quantization, the initial weights are updated using existing patterns. Meanwhile, in the self-organizing map method, Kohonen takes initial weights randomly, then these weights are updated until they can classify themselves into the desired number of classes. The processes that occur in the artificial neural network method require a relatively long time. This is influenced by the large number of data samples used as a means of updating the trained weights. From the results of the research conducted, it shows that the learning rate value that was built around 0.2 < α ≤ (10) ^ (-2) can produce better signature pattern recognition accuracy.