Sinkron : jurnal dan penelitian teknik informatika 2024-02-19T04:02:33+00:00 Muhammad Khoiruddin Harahap Open Journal Systems <p><a href=""><strong>Sinkron</strong> <strong>: Jurnal dan Penelitian Teknik Informatika</strong></a> is<strong> The<a href=""> Kemdikbud Accredited National Scientific Journal Rank 3 (Sinta 3), Number: 148 / M / KPT / 2020 on August 3, 2020</a></strong>. Start from 2022, SinkrOn is published Quarterly, namely in January, April, July and October. SinkrOn aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about computer science. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and will be reviewed by colleagues. Articles sent to the Sinkron journal may not be published elsewhere. The manuscript must follow the writing style provided by SinkrOn and must be reviewed and edited.</p> <p>Sinkron is published by <strong><span style="text-decoration: underline;"><a href="">Politeknik Ganesha Medan</a></span></strong>, a Higher Education in Medan, North Sumatra, Indonesia. </p> <p><strong>E- ISSN: <a href="">2541-2019</a> </strong>(Indonesian | LIPI)<strong> | </strong><strong>P-ISSN: <a href="">2541-044X</a> </strong>(Indonesian | LIPI)<strong> | </strong><strong>DOI Prefix: 10.33395</strong></p> <p><strong>E- ISSN: <a href="">2541-2019</a> </strong>(International)<strong> | </strong><strong>P-ISSN: <a title="International ISSN" href="">2541-044X</a> </strong>(International)</p> <p><strong>Author Submission<br /></strong>plagiarism check is responsibility by the author and must include the results of the plagiarism check when making the submission process.</p> <p> </p> <p><strong><strong style="font-size: 18pt;">Become Reviewer and Editor</strong></strong><br />The editor of Sinkron: Jurnal dan Penelitian Teknik Informatika invites you to become a reviewer or a editor. <a href="">Please complete fill this form</a></p> Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments 2024-01-11T16:25:53+00:00 Maswan Pratama Putra Yuliant Sibaroni <p>In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Maswan Pratama Putra, Yuliant Sibaroni Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment 2024-01-11T02:42:28+00:00 Keisha Priya Harmandini Kemas Muslim L <p>Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Keisha Priya Harmandini, Kemas Muslim L Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter 2024-01-12T15:21:23+00:00 Lisyana Damayanti Kemas Muslim Lhaksmana <p><strong>Abstract: </strong>This analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy.</p> <p><strong>&nbsp;</strong></p> <p><strong>Keywords: Sentiment Analysis, Indonesian Presidential Election 2024, Twitter, SVM, Word2Vec.</strong></p> <p>&nbsp;</p> <p>&nbsp;</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Lisyana Damayanti, Kemas Muslim Lhaksmana Implementation of Cloud Run and Cloud Storage as REST API Service on OutfitHub Application 2024-01-16T16:26:21+00:00 Derryl Reflando Tarigan Muhammad Irsan Muhammad Faris Fathoni <p>The development of Cloud Computing technology has progressed rapidly in recent years especially with the emergence of Google Cloud Services (GCR) which has become one of the leading cloud service providers. This research focuses on the OutfitHub application, which plays a role in assisting users in determining clothing styles using a personalized recommendation system. In developing this application, the research seeks to implement cloud computing services to improve application performance. The purpose of this research is to implement Cloud Computing, especially Cloud run and Cloud Storage services as Rest API in the Outfithub application. By implementing these two services, it is expected that there is no need to pay attention to the problem of Storage needs that are growing at any time and no need to worry about the need for server configuration because both of these things will be fully done by GCR. Implementing Cloud Computing will provide a variety of benefits in addition to those previously mentioned, such as: being able to access data from anywhere and at any time. This implementation is expected to be able to run OutfitHub applications in a Cloud environment in a serverless computing manner without requiring the design of unnecessary virtual machines.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Derryl Reflando Tarigan, Muhammad Irsan, Muhammad Faris Fathoni Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms 2024-01-27T15:53:58+00:00 Juwariyem Sriyanto Sri Lestari Chairani <p>Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. This research uses the Random Forest method to see the level of accuracy of stunting predictions in toddlers using a dataset of 10,001 data records, 7 attributes and 1 attribute class. Based on test results using the Bagging and Random Forest methods, accuracy results were obtained at 91.98%. From the results obtained from the tests that have been carried out, it is known that the Bagging and Random Forest methods are better methods for predicting stunting accuracy.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Juwariyem, Sriyanto, Sri Lestari, Chairani Comparison of Smartphone Technology using AHP, ELECTRE, and PROMETHEE Methods 2023-10-09T16:16:26+00:00 Akmaludin Akmaludin Adhi Dharma Suriyanto Nandang Iriadi Kudiantoro Widianto <p>The progress of smartphone technology is now very rapid, supported by many renewable features, even many users are competing to get the latest products without regard to the costs that have been incurred. The problem that arises is that it is increasingly difficult to select technology-based products with many criteria. The purpose of writing this paper is to provide the best solution for selecting technology-based products with multi-criteria to suit user needs by taking into account the costs incurred effectively and the use of contradictory multi-criteria applications. The presence of technology products always has many criteria that make it more difficult for users to choose products as the right choice according to their needs, thus the right method is needed as a solution to obtain technology-based products such as smartphones. The Analytic Hierarchy Process (AHP) method is used for the evaluation and selection process. This AHP method will collaborate with the ELECTRE and PROMETHEE methods as a comparison solution for smartphone product selection. The resulting comparison will be an applied model for smartphone selection that produces the best decision-making support according to user needs. The results of the collaborative implementation process of the ELECTRE and PROMETHEE methods provide a decision on the rating system. The collaborative application of the AHP method to the ELECTRE and PROMETHEE methods provides optimal decision support for the selection process, so that this can be used as a comparison material in making decisions regarding the selection of smartphones as technology-based products.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2023 Akmaludin, Adhi Dharma Suriyanto , Kudiantoro Widianto Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process 2024-01-04T20:19:21+00:00 Ahmad Ilham Kushartanto Fauziah Rima Tamara Aldisa <p>Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Ahmad Ilham Kushartanto, Fauziah, Rima Tamara Aldisa Development of an Intelligent Imaging System for Determining Maturity of Copra Flesh in Coconuts Using Shape and Texture Extraction 2024-01-10T16:20:01+00:00 Yogi Wiyandra Firna Yenila suci wahyuni <p>Copra is dried coconut meat that is used to produce coconut oil. According to the Central Statistics Agency (BPS), Indonesia's copra production in 2020 reached 2.3 million tonnes. This is one form of the process of improving the economy of people living on the coast. This research was conducted to educate farmers in determining the level of maturity of the copra meat produced. This research was conducted using an extraction method that involves colour extraction and texture extraction. the method is used to provide convenience in seeing the level of maturity of the two characteristics of copra obtained in the field, namely texture and colour. The process obtained in the training with one of the images used as a test image in colour extraction produces area, perimeter, metric and eccentricity values in label 3 with values of 651.00, 184.69, 0.24 and 0.89. while in the feature extraction method the results are obtained with an average intensity value of 243.31, standard deviation of intensity 39.76 and entropy value of the tested image 4.57. The method is able to perform a detection process so that it can determine the level of maturity of copra seen from the existing types of copra such as asalan copra, regular copra, black copra and wet copra, each of which provides different functions in the copra processing stage. The process will be carried out using KNN which is seen from all test data and training data stored after the detection process. The results of the process carried out using digital images involving the extraction method for detection and KNN for classification are able to provide the right value. This is evidenced by the better accuracy value of 98%.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Yogi Wiyandra, Firna Yenila, suci wahyuni Clothing Recommendation System Using the K-Nearest Neighbor Method 2024-01-11T02:59:05+00:00 Arya Maghrizal Putra Muhamad Irsan Muhammad Faris Fathoni <p>The world of fashion and the way we interact with it has been transformed by advances in information and communication technology. Clothing recommendation applications have become increasingly common, helping people choose clothes that suit their style and preferences. This study suggests using the KNN Method as a basis for building a more intelligent and personalized clothing recommendation system. To address the growing need for accurate clothing recommendations that match users' preferences, The goal of this research is to create a clothing recommendation system that can help users choose more appropriately because advances in technology have made it possible to gather and examine user data more thoroughly. In this study, the clothing recommendation system was implemented using the KNN Method. We ran simulations by setting the clothing dataset's parameter K value from 3 to 11. The simulation results show that the system's performance reaches its peak at parameter value K=8. We measured the system's accuracy, precision, and recall at this K value in order to assess its performance. The results show that the clothing recommendation system uses the KNN Method. A clothing recommendation system based on the KNN Method with the parameter K=8 has proven successful in classifying clothes with an accuracy of 83,67%.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Arya Maghrizal Putra, Muhamad Irsan, Muhammad Faris Fathoni Rice Plant Disease Detection System Using Transfer Learning with MobilenetV3Large 2024-01-11T23:47:22+00:00 Rifqi Raenanda Faqih Muhamad Irsan Muhammad Faris Fathoni <p>In this study, we address that foliar diseases of rice (Oryza sativa L.) pose a serious threat to agricultural productivity and propose an effective method for disease detection using Convolutional Neural Network (CNN). We use transfer learning on the MobilenetV3Large model to improve the model's performance. Our study involves a curated dataset containing images of infected rice leaves, followed by a careful preprocessing step. This dataset is then used to train a CNN model. The results show a commendable accuracy rate of over 90% and almost reaching 95% when the model is trained over 200 epochs. The model performance graph shows a consistent upward trend in accuracy coupled with decreasing loss during the training process. Furthermore, the classification results highlight the ability of the model to discriminate between different types of diseases affecting rice leaves. This study demonstrates the effectiveness of our proposed method and positions it as a valuable tool for leaf disease detection in rice. By providing faster and more accurate control measures, our approach has the potential to significantly improve agricultural productivity. The successful application of the CNN model using MobilenetV3Large highlights its adaptability and robust performance in addressing the pressing problem of rice leaf diseases and provides a promising path for future advances in precision agriculture.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Rifqi Raenanda Faqih, Muhamad Irsan, Muhammad Faris Fathoni Optimizing Attendance Data Security by Implementing Dynamic AES-128 Encryption 2024-01-19T15:11:02+00:00 Andri Novandri Mukhsin Nuzula Yuwaldi Away Kahlil Kahlil <p>The protection of data security is crucial, particularly when dealing with the transmission of sensitive information through communication networks. This article explores the Advanced Encryption Standard 128-bit (AES-128) algorithm as an effective and secure cryptographic solution. The paper proposes the dynamic development of the AES-128 cryptography method by implementing a dynamic key to enhance the security of employee attendance data. The dynamic key involves changing the encryption key every minute, providing an additional security layer and reducing the risk of decryption by unauthorized parties. Test results indicate that the dynamic AES-128 encryption algorithm demonstrates optimal performance. The consecutive encryption and decryption speeds for sending attendance data are 14656.78 bit/s and 21898.21 bit/s, respectively. The consistent duration of the encryption and decryption processes, at 6.66ms and 2.44ms, along with an Avalanche Effect rate of 50.73% and an Entropy of 6.67 bit/symbol, emphasizes the algorithm’s efficiency and stability. This research not only reinforces the desired level of security but also outperforms several previous studies. Analyzed performance data indicates that this method is not only efficient but also stable in maintaining data security, addressing significant variations in data length. Thus, the implementation of dynamic AES-128 cryptography in attendance systems provides a significant advantage in addressing information security challenges in the current digital era.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Andri Novandri; Mukhsin Nuzula, Yuwaldi Away, Kahlil Kahlil Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine 2024-02-07T03:26:47+00:00 Tiara Sari Ningsih Tiara Teguh Iman Hermanto Teguh Teguh Iman Hermanto Imam <p>To choose a mobile provider to use, prospective users often rely on reviews left by previous users of the mobile provider application. One source of information for finding reviews of cellular provider applications is the Google Play Store. The purpose of this research is to analyze user reviews of cellular provider applications and find out the comparison of the accuracy levels of the two algorithms to be used, namely the Naïve Bayes Classification (NBC) and Support Vector Machine (SVM) algorithms. The object of this research is focused on the three most popular applications in Indonesia, according to the Goodstate website, namely Telkomsel, IM3, and XL Axiata. After testing using the Naïve Bayes Clasification method, the accuracy value obtained in the MyTelkomsel application is 75%, MyIM3 is 80%, and MyXL is 72%. While the Support Vector Machine method obtained an accuracy value of 77% for MyTelkomsel,&nbsp; 80% for MyIM3, and 76% for MyXL.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Tiara Sari Ningsih Tiara; Teguh Iman Hermanto Teguh, Teguh Iman Hermanto Imam Comparative Study: Preemptive Shortest Job First and Round Robin Algorithms 2023-06-13T09:04:24+00:00 Rakhmat Purnomo Tri Dharma Putra <p>Abstract: Operating system is a software acting as an interface between computer hardware and user. Operating system is known as a resource&nbsp; manager. The main responsibility of operating system is to handle resources of computer system. Scheduling is a key concept in computer multitasking and multiprocessing operating system design by switching the CPU among process. Shortest job first (SJF) and round robin are two wellknown algorithms in CPU processing. For shortest job first, this algorithm can be preemptived. In preemptive shortest job first, when a new process coming in, the process can be interupted. Where with round robin algorithm there will be time slices, context switching, or also called quantum, between process. In this journal we wil discuss comparative study between preemptive shortest job first and round robin algorithms. Three comparative studies will be discussed to understand these two algorithms more deeply. For all comparative study, the average waiting time and average turnaround time is more for round robin algorithm. In the first comparative study, we get average waiting time 52% more. For average turnaround time, 30% more. In second comparative analysis, we get 52 % average waiting time more and we get 35 % average turnaround time more. For third comparative analysis, average waiting time we get 50% more and for average turnaround time, we get 28% more. Thus it is concluded in our comparative study for these kind of data the preemptive shortest job first is more efficient then the round robin algorithm.</p> <p>&nbsp;</p> <p>Keywords: comparative study, premptive shortest job first algorithm, round robin algorithm, turn around time, average waiting time, time slice</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2023 Rakhmat Purnomo, Tri Dharma Putra LSB-2 Steganography with Brotli Compression and base64 Encoding for Improving Data Embedding Capacity 2023-12-22T23:09:57+00:00 Muhammad Yiko Satriyawibawa Pulung Nurtantio Andono Lim Way Soong Ng Poh Kiat <p>Steganography functions as a technique for embedding messages or data in various forms of media, such as images, audio, video, or text, with the aim of avoiding detection by unauthorized parties. Steganography techniques can be used as a solution to hide and protect data. In this research, steganography will be carried out using images as the transmission object. This research was conducted to offer a modification of the Least Significant Bit (LSB) steganography technique using the LSB-2 method with Brotli compression and base64 encoding. Modification and use of Brotli compression and base64 coding aims to increase the message capacity that can be embedded in a transmission object while maintaining the quality of the transmission object. Experiments using small data and big data. The experimental results will be presented in tabular form by comparing the original image with the steganographically processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as a comparison. The experiments carried out resulted in an increase in image capacity by reducing capacity usage with an average of 47.13% for small data and an average of 71.34%. The big data experiment resulted in an increase in the PSNR value of around 3.49%, accompanied by a decrease in the average MSE value of 33.85%, and a constant SSIM value of 0.9999, thus proving that the proposed method was successful in increasing image capacity and improving stego-image quality. when embedding big data.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Muhammad Yiko Satriyawibawa, Pulung Nurtantio Andono, Lim Way Soong, Ng Poh Kiat Depression Detection of Users in Social Media X using IndoBERTweet 2024-01-08T11:09:18+00:00 Muhammad Fadhel Warih Maharani <p>According to the Ministry of Home Affairs, the population of Indonesia stands at 273 million, Indonesia has approximately 167 million active subscribers to virtual entertainment platforms, including YouTube, Facebook, Instagram, and Twitter. The use of online entertainment is huge, particularly on Twitter, and has been associated with mental health implications, such as depression. This research objective is to do a comprehensive study about the IndoBertweet deep learning framework to investigate the prevalence of depression in social media, focusing on Twitter. Utilizing the DASS-42, the research estimates depression levels based on user interactions and reactions to tweets. The results of this research showed that the IndoBERTweet method achieved an accuracy rate of 82% in detecting depression using Twitter data. This research highlights the importance of intervention strategies to support the mental health of social media users, emphasizing the importance of proactive measures in addressing mental well-being issues in the digital space.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Muhammad Fadhel, Warih Maharani Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering 2024-01-10T09:38:05+00:00 Z. K. A. Baizal Alvian Daniswara Adhipramana Putra <p>In the era of ever-evolving information technology, choosing the best laptop can be complicated for many users, especially as users have difficulty understanding increasingly complex technical specifications. We propose a solution to a laptop recommender system that considers the user's preferences and functional requirements. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures deep connectedness between functional needs and technical specifications of laptops. At the same time, Collaborative Filtering (CF) increases the diversity of recommended products by utilizing similar user preference data.&nbsp; The importance of this approach lies in the system's assistance in identifying laptops that best match the user's functional needs. The system's user-friendly interface streamlines the selection process, making it more intuitive and enjoyable. The recommendations are based on the user's preferences and needs, providing a personalized touch and high relevance. Overall, this laptop recommendation system is an effective tool that helps users make informed and intelligent decisions and can potentially optimize users' time and energy. The evaluation showed that the success rate of recommendation accuracy reached 93.33%, validating the system's effectiveness in helping users choose the right laptop according to their functional needs. As such, the system is expected to bring significant benefits, increase productivity, and provide higher satisfaction for users.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Z. K. A. Baizal, Alvian Daniswara Adhipramana Putra Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method 2024-01-12T06:38:42+00:00 Wijdan Khalil Muhammad Irsan Muhammad Faris Fathoni <p>Rice, as a key element of Indonesia's food security, plays a crucial role in agricultural ecosystems. Despite its high economic value, rice plants are susceptible to various diseases that can reduce productivity and harvest quality. Farmer's limited knowledge about disease types, identification, and proper handling poses a serious challenge to sustainable agriculture. Previous studies highlight farmers' inadequate understanding of pests and diseases in rice plants, leading to a high dependency on pesticides. Furthermore, lack of training data and a shallow understanding of rice diseases present significant challenges in disease management efforts. This research aims to develop an Android-based Smart Farm application. This application utilizes image processing and artificial intelligence technologies to assist farmers in identifying leaf diseases in rice plants. Requirements analysis involves literature review and field observations around Bandung Regency. It can be concluded; Smart Farm application has been successfully developed with three functional and two non-functional requirements. Validation testing indicates a 100% functionality rate and an 80% accuracy in disease detection. Nevertheless, further attention is required to enhance accuracy by providing more training data and improving image quality. The implications of this research extend to enhancing farmers' knowledge, reducing pesticide dependency, and supporting sustainable agriculture in the future.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Wijdan Khalil, Muhammad Irsan, Muhammad Faris Fathoni Implementation of App Engine and Cloud Storage as REST API on Smart Farm Application 2024-01-13T06:08:47+00:00 Khoirul Azkiya Muhamad Irsan Muhammad Faris Fathoni <p>Smart Farm is an agricultural application that uses machine learning and cloud computing technology to improve efficiency in the farming process. Technological advancement and sustainable agriculture are two essential aspects of supporting global food security. This research investigates the implementation of App Engine and Cloud Storage in developing REST API in Smart Farm applications. By utilizing cloud computing technology, such as App Engine, and cloud storage, such as Cloud Storage, we can create efficient solutions to monitor and manage agriculture better. This research implements an App Engine and Cloud Storage to develop a REST API that allows Smart Farm application users to access data and control farming devices efficiently. The authors designed, developed, and tested this system to ensure optimal performance and reliability in agricultural data collection and distribution. This method has several significant advantages. First, App Engine allows for easy scalability, ensuring the system can handle increased data demand without disruption. Secondly, Cloud Storage provides secure and scalable storage for agricultural data, which can be accessed from anywhere. This provides easy and quick access to critical data for farmers. Moreover, the use of cloud technology also reduces infrastructure and maintenance costs. The developed system integrates the App Engine and Cloud Storage with the Smart Farm application. The App Engine is a processing engine that receives user requests via the REST API, processes the required data, and provides appropriate responses. Like image data, farm data is stored and managed on Cloud Storage. Users can access this data through the Smart Farm app or other devices, enabling better farming monitoring and decision-making.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Khoirul Azkiya, Muhamad Irsan, Muhammad Faris Fathoni Comparison Of Performance Of K-Nearest Neighbors And Neural Network Algorithm In Bitcoin Price Prediction 2024-01-21T12:56:06+00:00 Eko Aziz Apriadi Sriyanto Sri Lestari Suhendro Yusuf Irianto <p>This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Eko Aziz Apriadi, Sriyanto, Sri Lestari, Suhendro Yusuf Irianto Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods 2023-10-03T12:20:01+00:00 Akmaludin Akmaludin Erene Gernaria Sihombing Rinawati Rinawati Linda Sari Dewi Ester Arisawati <p>The teacher's role is very important in improving the national learning system. Many honorary teachers are empowered in curriculum development in a number of schools who want to collaborate in improving the quality of their students. The purpose of this research is to provide rewards to honorary teachers who have long served for the progress of the nation in the world of education to be appointed as permanent teachers. The selection method was carried out through a criteria weighting technique with the MCDM-AHP method which was integrated with the WASPAS method. The technique of developing the MCDM-AHP method as an eigenvector measurement concept with proof of optimization through mathematical algebra matrices that is correlated with the Expert Choice application to get optimal values. The result optimization value is integrated with the WASPAS method as a determinant of the ranking system for permanent teacher candidates. This method is a unification of the concepts of the weight product model and weight sum model methods, so that it has special stages to support decision making with the WASPAS method. The results of selecting twelve honorary teachers for appointment as permanent teachers can be seen from the acquisition of the Qi optimization value as a ranking. The results of support for decision making for permanent teacher appointments with the highest optimization value were given to TC04 with a weight of 0.878; followed by a significant difference in the next rank. The findings of this study provide evidence that the integration of the MCDM-AHP and WASPAS methods provides continuous optimization results for decision-making support.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2023 Akmaludin, Erene Gernaria Sihombing, Rinawati, Linda Sari Dewi, Ester Arisawati Sentiment Analysis of Twitter Users on Qatar 2023 World Cup Polemic Using TF-IDF and K-Nearest Neigbor Weighting 2023-12-28T21:42:25+00:00 Sayyid Muh. Raziq Olajuwon Kusrini Kusrini Kusnawi Kusnawi <p>This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral.The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods. The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup.</p> 2024-02-19T00:00:00+00:00 Copyright (c) 2024 Sayyid Muh. Raziq Olajuwon, Kusrini, Kusnawi