Edumatic: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) is a scientific journal in the field of informatics education published by Universitas Hamzanwadi three times a year, namely in April, August, and December. Since 7 April 2022, the Edumatic: Jurnal Pendidikan Informatika has been accredited with a rank of 3 (SINTA 3) based on the Decree of the Director General of Higher Education Tinggi (SK No. 105/E/KPT/2022). This means that articles published in Vol. 5 No. 2, December 2021 are included in SINTA 3. The focus and scope of this journal are (1) Computers and Informatics in Education, (2) Learning Models and ICT Models, (3) Development of Learning Media Based on Information Technology, (4) Human-Computer Interaction, (5) Information Systems and Information Technology, (6) Data Mining, (7) Internet of Things (IoT), (8) Artificial Intelligence, (9) Algorithms and Numerical Computation, (10) Computers and Networks, and other focuses related to computers and education. All articles in Edumatic: Jurnal Pendidikan Informatika have a Digital Object Identifier (DOI) with the DOI Prefix: 10.29408 by Crossref. In addition, Edumatic: Jurnal Pendidikan Informatika has been indexed by a number of indexing agencies, including GARUDA, Dimensions, Google Scholar, and others. Nama Jurnal Edumatic: Jurnal Pendidikan Informatika Inisial Edumatic Singkatan Edumatic Jadwal Terbit Tiga Terbitan per tahun | April, Agustus, dan Desember DOI Prefix 10.29408 e-ISSN 2549-7472 Ketua Penyunting Muhammad Zamroni Uska Penerbit Universitas Hamzanwadi Analisa Kutipan Google Scholar | SINTA
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Sistem Pendukung Keputusan berbasis Web dalam Pengangkatan Pekerja PKWT Menjadi Pekerja Tetap
The uncertainty of the status of certain time work agreement (PKWT) workers, the appointment of which is still done manually, takes a long time, thus causing certainty problems among workers at CV. Pelita Berjaya Bersama. This study aims to design a decision support system for the appointment of non-permanent workers to permanent workers using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. This research chose a structured and linear waterfall development model, ensuring each stage of development is completed sequentially with complete documentation. The system was developed by going through the stages of requirements gathering, architecture design (database, interface, AHP, and SAW algorithms), design realization (PHP code writing and MySQL database development), and testing using a black box to ensure the system functions properly. The result of our findings is a decision support system that can help companies in making decisions objectively and simplify the appointment process. The system produces output data in the form of ranking results from each alternative so that it is easy to understand. The use of AHP and SAW algorithms in the system is expected to increase accuracy in the selection process. The results of system testing using black box are declared successful starting from the login to logout process. With this system, the company can appoint PKWT workers to become permanent workers.
Copyright (c) 2025 Audiya Ananda Chaniago, Mohd Siddik, Ahmad Muhazir https://creativecommons.org/licenses/by-sa/4.0
Optimasi Deteksi Objek pada Video dengan Kompresi Region of Interest menggunakan Model YOLOv8
The demand for real-time object detection systems, such as those used in video surveillance and autonomous vehicles, drives the need for efficient data storage and transmission without compromising accuracy. One promising approach is Region of Interest (ROI)-based video compression, which preserves visual quality in important areas. This study aims to evaluate the impact of video compression on object detection accuracy using the YOLOv8 model through statistical analysis using Analysis of Variance (ANOVA), and to compare the effectiveness of uniform and ROI-based compression methods. Videos from the VIRAT Video Dataset were compressed using the Constant Rate Factor (CRF) parameter and evaluated based on mAP_50, mAP_50_95, and file size. ANOVA results indicate no statistically significant differences between the two methods. At CRF 50, file size can be reduced by over 60%, but mAP_50 accuracy drops below 50% due to quality degradation in non-ROI areas, which disrupts the spatial context required by the model. This study contributes by examining the compression tolerance limits of YOLOv8 and reveals that overall visual quality, rather than just object-focused quality, plays a crucial role in model performance. These findings have important implications for real-time applications such as CCTV and autonomous vehicles, where a balance between compression efficiency and detection accuracy is critical. Future studies may explore adaptive ROI approaches that consider dynamic object movement.
Copyright (c) 2025 Azhryl Akbar Assagaf, Muis Muhtadi https://creativecommons.org/licenses/by-sa/4.0/
Analisis Sentimen Publik Program PPPK di Media Sosial X menggunakan Naïve Bayes dan SVM
Sentiment analysis of the Government Employee Program with Work Agreement (PPPK) is important to understand public perception and as a basis for policy evaluation. This study aims to analyze public sentiment towards the PPPK policy and evaluate the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying public opinion on social media X. This study is a quantitative study with a data mining approach. The stages begin with collecting data collection of 7,508 tweets and processed through the stages of preprocessing, labeling, feature extraction using TF-IDF, and classification with SVM and Naïve Bayes. Data balancing is done using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that SVM produces the highest accuracy of 95%, while Naïve Bayes reaches 87%. The application of SMOTE has been shown to improve the performance of both models, especially in recognizing negative sentiment. The advantage of SVM lies in its ability to optimally separate classes through maximum margin, which is effective for high-dimensional text data. Meanwhile, SMOTE plays an important role in balancing class distribution, thereby increasing accuracy, precision, and recall. These findings provide an important basis for policy makers to respond to public opinion more appropriately based on valid and representative data.
Copyright (c) 2025 Lisyo Hileria Sarumpaet, Ryan Randy Suryono https://creativecommons.org/licenses/by-sa/4.0/
Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN
The development of online lending services in Indonesia has led to various responses from the public on social media, including complaints about billing methods and concerns about high interest rates. This study aims to compare the performance of Naive Bayes, Decision Tree, and K-Nearest Neighbors (KNN) algorithms. This type of research is quantitative, and the data used is 5,941 tweets through crawling techniques from X social media, followed by preprocessing, data labeling with a lexicon-based feature extraction using TF-IDF, and sentiment classification using the three algorithms. The evaluation stage uses a confusion matrix, which can calculate accuracy, precision, recall, and the F-1 score. The results show that the decision tree provides the most consistent performance with 69% accuracy due to its ability to recognize complex data patterns and understand relationships between features. Naive Bayes excels in negative sentiment classification with 68% accuracy, while KNN shows the lowest performance with 44% accuracy because it is not effective in handling high-dimensional text data. These results can be utilized by online loan service providers and regulators to build an accurate public opinion monitoring system in order to respond to issues of public concern and improve service quality on an ongoing basis.
Copyright (c) 2025 Khyntia Miranda, Ryan Randy Suryono https://creativecommons.org/licenses/by-sa/4.0/
Kinerja Naive Bayes dan SVM pada Data Survei Tidak Seimbang: Studi Klasifikasi Kepuasan Masyarakat
The utilization of Public Satisfaction Survey (SKM) data has not been optimal, highlighting the need for an effective classification method to determine the level of public satisfaction. This study aims to classify satisfaction levels using the 2024 SKM data from the Regional Civil Service and Training Agency (BKPPD) of Grobogan Regency, employing Naive Bayes and Support Vector Machine (SVM) algorithms. This quantitative research uses nine service elements rated on a scale of 1 to 4 as features, with satisfaction level as the target variable. The dataset consists of 303 entries: 156 “very satisfied,” 115 “satisfied,” and 32 “dissatisfied.” Random oversampling was applied to address class imbalance. Model performance was evaluated using accuracy, precision, recall, and F1-score, both before and after oversampling. Results showed Naive Bayes achieved 96.72% accuracy, while SVM scored 95.08%. After oversampling, SVM accuracy significantly improved to 98.36%, while Naive Bayes slightly decreased to 95.08%. Precision, recall, and F1-scores also demonstrated strong performance across all classes. This study is expected to support the improvement of public service delivery at BKPPD Grobogan and similar institutions.
Copyright (c) 2025 Mellynda Noor Romadhoni, Nurul Anisa Sri Winarsih https://creativecommons.org/licenses/by-sa/4.0/
Real-time Petty Cash Monitoring: Inovasi Web untuk Pengelolaan Anggaran Operasional yang Efisien
The petty cash management at PT PLN (Persero) UP3 Garut was previously conducted manually using Excel files, which were then uploaded to the SAP ERP system for financial reporting. This process lacked real-time monitoring and automated alerts when expenditures approached budget limits. This study aims to develop a web-based application to digitally monitor petty cash through weekly transaction recording, nominal validation, automated notifications, and real-time reporting. The application was developed using the waterfall model through stages of requirement analysis, UML-based system design, implementation using PHP and MySQL, and black box testing. The main result of this study is a web-based petty cash monitoring system with key features such as login, transaction input, budget validation, budget recap, and automatic notifications. Testing results showed that all functions operated properly without errors and improved weekly recording efficiency by over 70%. Data is stored locally using XAMPP to ensure financial information security. The developed application meets the unit’s internal needs and contributes to better transparency and budget control. However, the system is not yet integrated with SAP, making automated integration a future development goal.
Copyright (c) 2025 Witsqa Inayatussholiha Ridwan, Ira Siti Sarah https://creativecommons.org/licenses/by-sa/4.0/
IKN Public Opinion on TikTok Before and After Efficiency Policy: CNN-LSTM on Imbalanced Data
Growing polarization in Ibu Kota Nusantara (IKN) stems from conventional sentiment analysis tools’ inability to decode TikTok’s contextual complexities, particularly multimodal sarcasm and vernacular-policy relationships (e.g., mangkrak for project cancellations). This study develops a policy-aware hybrid model (CNN-BiLSTM + Policy Knowledge Graph) to decode TikTok’s multimodal sarcasm and vernacular-policy links (e.g., mangkrak), enabling: youth sentiment quantification post-IKN’s 73.3% budget cuts, social criticism-socio-political reality mapping, and evidence-based interventions mitigating Global South strategic project polarization. Using the Knowledge Discovery in Databases framework, we analyzed 2,950 high-engagement TikTok comments (≥10 interactions) from verified accounts (@Polindo.id and @geraldvincentt) across two periods: pre-policy (June-August 2024) and post-policy (January-March 2025). Methodologically, slang normalization, stemming, and minority-class weighting (15×) preceded classification via a CNN-BiLSTM architecture integrated with Policy Knowledge Graphs. Results showed an 18.88% reduction in negative sentiment (83.2%-8.7%), model accuracy of 94.13% (AUC-PR 0.91), and strong correlations between vernacular terms (e.g., mandek [stagnation]) and policy outcomes (r = -0.89; p < 0.01), with investor asing mentions surging 463% post-policy. These validate deep learning-enabled social listening for real-time policy diagnostics, with implications for fiscal transparency dashboards, algorithmic bias mitigation, and context-driven policy communication prioritizing vulnerable groups in SDG infrastructure governance.
Copyright (c) 2025 Ikhwanus Sufiya, Khotibul Umam, Maya Rini Handayani https://creativecommons.org/licenses/by-sa/4.0/
Analisis Sentimen Ulasan Game dengan KNN: Perbandingan Rating dan Kamus Sentimen
The growth of the global gaming industry makes sentiment analysis of user reviews a crucial tool for understanding satisfaction and identifying technical issues. This study aims to evaluate three labelling methods (rating-based, Sentiwords_id, and InSet) for classifying the sentiment of Indonesian-language reviews for the game Zenless Zone Zero (ZZZ) using the K-Nearest Neighbor (KNN) algorithm. The study analyzes 4,282 reviews from the Google Play Store, which underwent a Data Preprocessing stage, including Null Handling, Cleaning, Case Folding, Tokenization, Stopword Removal, and Stemming. The KNN's performance for each labelling method was evaluated using accuracy, precision, recall, and F1-score metrics on 80:20 train-test split. The labelling results reveal different sentiment perceptions: the rating-based method tends toward positive, InSet toward negative, while Sentiwords_id is dominated by the positive and neutral classes. The KNN performance evaluation shows that rating-based labelling achieved the highest accuracy (72%), excelling on the positive class (86% recall) but performing poorly on the neutral class (9% recall). Conversely, the lexicon-based labelling methods (both 69% accuracy) have specific strengths: InSet in negative detection (81% recall) and Sentiwords_id in recognizing the neutral class (83% recall). Main challenges of this study include the lexicon's limitations in handling slang and game-specific terms, as well as the inconsistency between ratings and text. This study is expected to provide empirical evidence on performance trade-offs among automatic labelling methods to aid in identifying player satisfaction and advancing the quality of game development.
Copyright (c) 2025 Wisesa Sat Sunyaruri, Novita Kurnia Ningrum https://creativecommons.org/licenses/by-sa/4.0/
Aplikasi Pengawasan Kepatuhan Minum Obat Pasien Tuberkulosis (TBC) berbasis Mobile
Based on WHO data from 2024, Indonesia ranks second after India as the country with the highest number of tuberculosis (TB) cases in the world. Low patient adherence to regular medication intake, caused by the weakness of manual monitoring systems by healthcare workers, remains a major challenge in TB control. This study aims to develop a mobile application to monitor tuberculosis treatment and improve patient medication adherence. The application was developed using a software engineering approach with the Personal Extreme Programming (PXP) method, involving stages of planning, design, implementation, and iterative testing. Data collection techniques included observation, structured interviews, and User Acceptance Testing (UAT) through an online Likert-scale-based questionnaire. The outcome of this study is a mobile-based application featuring medication reminders, daily consumption reporting, monitoring by healthcare workers, and the provision of educational information. Black-box testing results indicated that all application features functioned as intended, while UAT results showed that the application is user-friendly and supports healthcare workers in effectively monitoring patient treatment. These findings demonstrate that the application can serve as a digital solution to improve TB treatment adherence and strengthen medication monitoring systems, particularly in high-burden areas.
Copyright (c) 2025 Naufal Erdian, Herliana Rosika, Ida Bagus Ketut Widiartha https://creativecommons.org/licenses/by-sa/4.0/
Klasifikasi Jenis Kejahatan berdasarkan Teks Amar Putusan Pengadilan Hukum Pidana KUHP menggunakan IndoBERT
The increasing number of the court’s rulings each year presents a challenge for the judiciary. One strategic solution is the application of Artificial Intelligence (AI). Indonesian-based models such as IndoBERT is potential to ease workloads by automatically classifying legal cases. This study aims to explore the capability of IndoBERT to automatically classifying the verdict of section of Indonesian KUHP rulings to accelerate crime type identification. This is an experimental study using supervised text classification. The dataset consists of 12000 verdicts collected from the Indonesian Supreme Court website, classified using IndoBERT fine-tuned with various hyperparameter configuration. Our findings show that the model with a batch size of 8 and learning rate 5e-5 achieved accuracy of 92.59%, precison of 92.93%, recall of 92.59%, and F1-Score of 92.59% on unseen test data. The high accuracy is supported by the explicit mention of crime types within verdict texts. To date, no study has specifically utilized IndoBERT or other models for automatic classification of KUHP articles. This finding has the potential to be integrated into the Supreme Court’s Directory of Decision as a support tool for automatic classification and legal document archiving.
Copyright (c) 2025 Tirtanusa Kurnia Adhi Perdana, Dewi Soyusiawaty https://creativecommons.org/licenses/by-sa/4.0/
K-Means Clustering untuk Segmentasi Pelanggan: Mengungkap Pola Pembelian Strategi Pemasaran pada Sektor Ritel
Digital transformation has posed new challenges for retail companies in understanding consumer behavior due to the increasing volume of data and continuously changing preferences. This study aims to uncover purchasing patterns among retail customers and to provide data-driven marketing strategies through customer segmentation using the K-Means Clustering algorithm. This research adopts a quantitative exploratory approach using 3,900 synthetic entries from the Kaggle platform, representing retail transactions. The analysis focuses on variables such as age, gender, product category, location, purchase amount, and transaction frequency. The analytical process includes data preprocessing, dimensionality reduction using PCA, and segmentation with the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, while the quality of the clustering was evaluated using internal metrics, namely the Calinski-Harabasz Score (491.47) and the Davies-Bouldin Score (2.02). These values indicate a well-structured and reliable clustering result. Our findings reveal five distinct customer segments with varying characteristics, ranging from teenagers with small and periodic purchases to high-value adult customers who transact infrequently. These insights serve as the foundation for developing marketing strategies such as loyalty programs, seasonal promotions, and exclusive approaches.
Copyright (c) 2025 Andrean Maulana Artiarno, Pratomo Setiaji, Fajar Nugraha https://creativecommons.org/licenses/by-sa/4.0/
Implementasi Algoritma Random Forest dalam Klasifikasi Ulasan Pengunjung Mall Semarang untuk Pengambilan Keputusan Layanan
Visitor preferences for malls in Semarang are not optimal because bold reviews have not been utilized optimally in decision making. Our research aims to classify the sentiment of Google Maps reviews from 13 malls in Semarang with a total of 2,600 reviews. Labeling is done manually based on ratings, where ratings 1–3 are considered negative reviews and 4–5 as positive reviews. The classification method used is Random Forest because the ensemble approach (bagging) provides optimal results. The research process includes data collection, labeling, cleaning, data sharing, classification, and model evaluation. The data used is unbalanced and dominated by positive reviews, so the Synthetic Minority Over-sampling Technique (SMOTE) technique was applied. The overall accuracy before and after SMOTE remained the same at 84%. However, the model's performance in detecting negative reviews increased from 27% to 44% in recall and F1-score from 0.40 to 0.52, but these values ​​are still relatively low. Java Supermall Semarang is the mall with the best reviews, with a classification accuracy reaching 90%. This model is better at recognizing positive reviews, but less reliable for negative reviews. Therefore, its use as a decision-making preference needs to be done with caution. This research opens up opportunities for further development, including the use of other models such as BERT which are superior in understanding context and language in reviews.
Copyright (c) 2025 Annisa Maizaliyanti, Khothibul Umam, Wenty Dwi Yuniarti, Maya Rini Handayani https://creativecommons.org/licenses/by-sa/4.0/
Sistem Penjadwalan dan Presensi Adaptif untuk Optimalisasi Manajemen Operasional di Rumah Sakit
Mataram University Hospital faces problems in managing work schedules that are still manual using excel and are not integrated with the attendance system. This has an impact on operational management in hospitals that are less than optimal. This research aims to produce an adaptive scheduling and attendance system display that is integrated with the registration module in the Hospital Management Information System (HMIS). The method used to build the system is the User Centered Design (UCD) Method integrated with the Extreme Programming (XP) Method, which includes the stages of user identification, identification of user needs, design and testing iteratively. Data was collected through observation and interviews with the hospital and analyzed qualitatively. Our findings resulted in the appearance of a scheduling and attendance system that aims to optimize operational management in hospitals. Testing the system interface using Usability Testing Method, System Usability Scale (SUS) and User Experience Questionnaire (UEQ) to test the functionality and user experience conducted to 30 respondents consisting of staff and doctors. The SUS test results obtained a score of 76.41 and the “Good” category for all aspects of the UEQ test. Overall, the system has a good level of usability and user experience. This system successfully optimizes operational management in hospitals.
Copyright (c) 2025 Gita Mailand Sari, Ida Bagus Ketut Widiartha, Herliana Rosika https://creativecommons.org/licenses/by-sa/4.0/
Sistem Pemantauan Kemajuan Skripsi dengan Visualisasi Timeline-Milestones untuk Skripsi Tepat Waktu: Pendekatan Manajemen Waktu
A thesis is a mandatory final assignment for undergraduate students, however its compiling process often presents challenges. Especially in terms of time management and progress monitoring. Students struggle to systematically plan the stages of their thesis. While lecturers have difficulty monitoring progress effectively. Therefore, a system is needed to assist both students and lecturers. The aim of this research is to develop a web-based thesis monitoring system with timeline and milestones visualization. This study employs a project management-based time management approach and uses the Waterfall system development model. The requirement analysis stage uses the PIECES framework, while the system design stage includes use case and activity diagrams, followed by implementation through coding and testing with black-box testing. Our research results is a prototype of web-based thesis monitoring system featuring timeline and milestone visualization. This visualization provides a clear overview of a thesis progress, making it easier for students to understand the entire process and for lectures to monitor progress effectively. The testing results show that the prototype system runs well. This system is expected to enhance the effectiveness of the thesis preparation process, support timely completion, and address challenges in monitoring the progress of thesis at universities.
Copyright (c) 2025 Rahmi Rahmi, Djuanda Djuanda, Samsuar Samsuar, Anzora Anzora, Irma Aryani, Nelia Susanti https://creativecommons.org/licenses/by-sa/4.0
Sistem Monitoring Inventory Souvenir berbasis AppSheet: Inovasi Manajemen Stok
Technological advances encourage digitalization in various sectors, including inventory management. Information systems have an important role in collecting, storing, and presenting data into useful information. PT PLN (Persero) UP3 Bandung faces obstacles in managing souvenir inventory stocks, such as delays in data access, recording errors, and inefficient manual processes. This research aims to develop an AppSheet-based souvenir inventory monitoring system as an efficient and integrated digital solution. This type of research is software engineering research, with a Waterfall development model consisting of requirements analysis, system design, implementation, and testing. The testing we do uses black box testing which aims to evaluate the functionality of the application according to specifications. The data collection we used was interviews and observations. The result of our findings is a system that provides reporting features to facilitate monitoring of souvenir inventory stock based on AppSheet which is designed to replace the manual recording process. The test results prove that the system runs well as needed and gets a positive response from the owners and employees of PT PLN (Persero) UP3 Bandung. This system can improve efficiency, recording accuracy, reduce the risk of human error, and speed up the monitoring process. With this system, it has the potential to be replicated in other units as part of the digital transformation of the public sector.
Copyright (c) 2025 Aghniya Tsaniya Fauziyyah, Ma’mun Sutisna https://creativecommons.org/licenses/by-sa/4.0/
Digital Banking Satisfaction: A SeaBank Study using The End User Computing Satisfaction (EUCS) Model
SeaBank serves as a digital banking platform that enables users to conduct financial transactions conveniently via their smartphones. Despite its widespread adoption, concerns regarding service quality persist. The objective of this study is to assess user satisfaction with the SeaBank application using the EUCS model. This research employs a quantitative methodology with PLS-SEM as the analytical technique to examine variable relationships. The software used for data analysis is SmartPLS 4. Data were collected through an online survey comprising 23 items, involving 100 active users aged 17 and above. Model evaluation includes both outer model and inner model analyses to ensure construct validity and reliability. The findings indicate that content, accuracy, and ease of use have a significant and positive impact on user satisfaction. Conversely, format and timeliness do not have a significant influence, suggesting that these aspects play a marginal role in shaping users' overall perception of the application. Implications of this study highlight the necessity of improving information quality, data accuracy, and intuitive interface design to enhance user satisfaction. While enhancements to format and timeliness can be considered, they are not a primary focus. This research provides valuable insights for application developers in designing user-centered service improvement strategies.
Copyright (c) 2025 Della Oktoriani, Medyantiwi Rahmawita, Tengku Khairil Ahsyar, Syaifullah Syaifullah https://creativecommons.org/licenses/by-sa/4.0/
Optimasi Kendali PID berbasis IoT pada Oven Listrik untuk Pengeringan Rempah yang Presisi
Unstable spice drying can reduce active compound content by up to 30% and increase the risk of microbial contamination by up to 40%, while conventional temperature control does not ensure thermal stability. This study aims to develop an IoT-based drying system using a PID algorithm to maintain temperature stability and allow remote monitoring and control. The research followed the waterfall model, starting from needs analysis, hardware and software design, PID implementation using a trial-and-error approach, IoT application development with Kodular and Firebase, system integration, and full system performance testing. Key components include the DS18B20 temperature sensor, ESP32, heating element, and IoT platform. Software testing used the black box method, while hardware testing evaluated performance through overshoot, steady-state error, settling time, disturbance simulation, and comparison with the on-off control method. The resulting system automatically regulates temperature with a PID algorithm and enables real-time monitoring via mobile devices. Testing showed the PID system was more stable than the on-off method, with overshoot <4°C, steady-state error <1.5°C, settling time of ±750 seconds, and quick response to disturbances. The mobile application operated reliably without errors, enhancing the quality and precision of the spice drying process.
Copyright (c) 2025 Bambang Pratama, Irma Salamah, Lindawati Lindawati https://creativecommons.org/licenses/by-sa/4.0/
Quality Evaluation of The SITASI Final Project System using Selected McCall Software Quality Factors
The Final Project Information System (SITASI) was developed to support academic administration processes. However, performance and usability issues continue to hinder its effectiveness, particularly during peak usage. This study aims to evaluate the quality of SITASI using the McCall Software Quality Model by focusing on five relevant operational factors: correctness, reliability, efficiency, integrity, and usability. The research employed a descriptive quantitative approach by distributing a validated user perception questionnaire to 72 students with active experience using SITASI. The instrument was tested for validity and reliability, with data analyzed using descriptive statistical techniques to evaluate the quality of the SITASI system. The results show that usability scored the highest at 86%, followed by correctness at 67.6%, reliability at 64.2%, integrity at 50.8%, and efficiency at 43.5%. These findings reveal strong user interface performance but expose technical limitations in speed and data security. The study concludes that while SITASI performs well in terms of usability, it requires substantial improvements in system responsiveness and integrity features. The results offer a structured evaluation of software quality and provide practical recommendations for developers to optimize performance and strengthen data protection. This study contributes a replicable framework for evaluating academic information systems in higher education environments.
Copyright (c) 2025 Suci Nur Adillah, Tengku Khairil Ahsyar, Syaifullah Syaifullah, Anofrizen Anofrizen, Arif Marsal https://creativecommons.org/licenses/by-sa/4.0/
Evaluating User Experience of SITASI System using HEART Metrics and Importance-Performance Analysis (IPA)
SITASI is a web-based system developed to help students manage references and citations for their final projects. Despite its importance, no comprehensive evaluation of the system’s user experience (UX) had been conducted. This study aims to evaluate the UX of SITASI used by students of the Information Systems Study Programme at UIN Suska Riau. Using a quantitative survey method, data were collected from 72 active students from the 2018-2021 cohorts through an online questionnaire. The evaluation was based on the five HEART Metrics dimensions: Happiness, Engagement, Adoption, Retention, and Task Success. Respondents were selected through random sampling using the Slovin formula. Data were analysed using SPSS version 30 for validity, reliability, and Importance-Performance Analysis (IPA). The HEART results indicated high usability levels across all dimensions, with Happiness and Retention scoring highest (73%) and Engagement lowest (70%). IPA showed an average suitability of 92%, with four indicators placed in Quadrant I, signifying urgent areas for improvement. These results serve as a foundation for actionable recommendations to enhance system satisfaction, improve performance, and develop a more user-centered and adaptive SITASI platform.
Copyright (c) 2025 Elsa Harmaniola, Tengku Khairil Ahsyar, Syaifullah Syaifullah, Anofrizen Anofrizen, Arif Marsal https://creativecommons.org/licenses/by-sa/4.0/
Performance Evaluation of Naïve Bayes and SVM in Sentiment Analysis of Illegal Parking Attendants
The increase in the number of vehicles in Indonesia has led to high demand for parking spaces, which has triggered the emergence of illegal parking attendants. This phenomenon has elicited various public responses, particularly on social media platform X. This study analyzes public sentiment toward the presence of illegal parking attendants by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms. The data used consists of 1,484 Indonesian-language tweets collected via crawling techniques. The pre-processing stage included data cleaning, case folding, word normalization, tokenization, stopword removal, and stemming. The data was then labeled with positive or negative sentiment using the InSet (Indonesia Sentiment Lexicon) approach and manually validated, before being divided into training and testing datasets. Feature extraction was performed using the TF-IDF method before being applied to the classification model. The evaluation results show that the SVM algorithm with a linear kernel approach produces the highest accuracy of 82%, outperforming Naïve Bayes: Gaussian 56%, Multinomial 74%, and Bernoulli 77%. These results are expected to contribute to the formulation of more organized and transparent parking policies, as well as demonstrate the importance of sentiment analysis as a tool to support data-driven decision making.
Copyright (c) 2025 Sandra Saputra, Paradise Paradise, Novanda Alim Setya Nugraha https://creativecommons.org/licenses/by-sa/4.0/
Sistem Pendukung Keputusan dalam Menentukan Jurusan Siswa di Sekolah Menengah menggunakan Metode Fuzzy Tahani
The major selection process at MAS Plus Al Ulum, Medan, is still conducted manually, making it prone to subjectivity, time-consuming, and potentially misaligned with students’ potential. Our research aims to develop a web-based decision support system for major recommendations using the Fuzzy Tahani method. This development research follows the Waterfall model, encompassing requirements analysis, design, implementation, and testing. Requirements were gathered through observation and interviews, followed by designing data processing logic and implementing a web-based system. Testing was performed using the black-box method on features such as login, homepage, student data input, fuzzification process, recommendation results, front page, dark mode, and logout. The analysis technique employed fuzzification and fuzzy queries to calculate students’ suitability for Science, Social Studies, or Religious Studies based on academic scores, interests, talents, and learning styles. The study involved 151 tenth-grade students. The resulting system automatically recommends Science, Social Studies, or Religious Studies based on the aforementioned criteria. Analysis of the 151 students’ data showed 72 students suited for Social Studies, 40 for Science, and 39 for Religious Studies. Testing confirmed all features functioned correctly without errors. This research provides a foundation for schools to create a more targeted, time-efficient, and student-aligned major selection strategy.
Copyright (c) 2025 Alvin Sany Lubis, Sriani Sriani https://creativecommons.org/licenses/by-sa/4.0/
Model Hybird Fuzzy Logic dan Deep Learning untuk Prediksi Harga Saham
Stock price prediction is a major challenge in the financial sector due to nonlinear factors and data uncertainty. This study aims to develop a predictive model by integrating fuzzy logic into deep learning algorithms to improve accuracy and robustness against noise. This is a quantitative experimental study using 1,000 daily historical stock price data of BBCA (Bank Central Asia), collected via web scraping from public sources. The data were analyzed using three types of neural networks: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), both before and after fuzzy integration. Fuzzification was applied to the price data to generate linguistic features, which were added as input to the neural network models. The models were evaluated using Train Cost, Test Cost, and the number of epochs, and a t-test was conducted to assess the statistical significance of performance differences. Our findings show that the LSTM model with fuzzy input achieved the best performance, with a Train Cost of 0.0002 and a Test Cost of 0.0052, and demonstrated superior capability in handling long-term dependencies. In contrast, RNN and GRU models showed decreased accuracy after fuzzy integration. The combining fuzzy and LSTM model shows promise for broader applications in time-series forecasting under uncertainty.
Copyright (c) 2025 Asep Muhidin, Elkin Rilvani, Candra Naya https://creativecommons.org/licenses/by-sa/4.0/
Klasifikasi Sentimen Ulasan E-Wallet menggunakan TF-IDF dan Random Forest dengan Penyeimbangan Data SMOTE
The increasing use of e-wallets in Indonesia highlights the need to understand user perceptions automatically and efficiently. One valuable data source is user reviews from the Google Play Store. This study aims to classify sentiment toward three major e-wallets, such as GoPay, OVO, and DANA to support service improvement. A quantitative approach is used with a machine learning-based classification method. A total of 30,000 reviews (10,000 per application) were collected using the google-play-scraper library. The data were processed through several stages: preprocessing (labeling, stopword removal, tokenization, and stemming), feature extraction using TF-IDF, data balancing with SMOTE, and classification with the Random Forest algorithm. Our findings show that the combination of Random Forest and SMOTE significantly improves model performance. Accuracy reached 90% (GoPay), 90% (OVO), and 87% (DANA). Precision, recall, and weighted f1-score were 90%, 89%, and 89% for GoPay; 90%, 90%, and 90% for OVO; and 88%, 88%, and 88% for DANA. WordCloud visualizations further support the findings by highlighting dominant words in each sentiment, such as “good,” “help,” and “lost.” Overall, the integration of TF-IDF, SMOTE, and Random Forest is proven effective and reliable for sentiment classification across the three e-wallet platforms.
Copyright (c) 2025 Muhammad Rioardian Syaputa, Muhammad Arifin, Diana Laily Fithri https://creativecommons.org/licenses/by-sa/4.0/
Aplikasi Deteksi Otomatis Hukum Tajwid Utama pada Ayat Al-Qur’an menggunakan YOLOv8
Tajwid learning faces challenges in visually recognizing recitation rules from Arabic script, thus requiring an interactive and accurate digital medium. This study aims to develop a web-based application to automatically detect seven core tajwid rules using YOLOv8. This research follows a Research and Development approach adopting the ADDIE model, which consists of five systematic stages: analysis, design, development, implementation, and evaluation. The YOLOv8 model was trained using 200 annotated images of Qur’anic verses, with a data split of 70% for training, 20% for validation, and 10% for testing. Data augmentation was applied through rotation, flipping, and brightness adjustment, with training facilitated using Roboflow. Our main finding is an interactive web application capable of automatically detecting seven tajwid rules from Qur’anic verse images. The application allows users to upload images, which are then analyzed and displayed with colored bounding boxes and interactive captions. Testing results showed accurate and responsive detection performance, achieving a mAP@50 of 89.88% with high accuracy across several tajwid classes. These findings highlight the potential of Artificial Intelligence (AI) to support more interactive, independent, and adaptive tajwid learning, while also promoting the digitization of Islamic manuscripts.
Copyright (c) 2025 Arfiansah Arfiansah, Mohammad Nasucha https://creativecommons.org/licenses/by-sa/4.0/
Enhanced Data Security in Video Media using RSA-LSB Hybrid Technique
Securing structured documents in dynamic digital media such as video remains a challenge, especially under threats like unauthorized access, format conversion, and compression. This research aims to build a secure and hidden digital document insertion system into video media using a hybrid approach between RSA cryptography algorithm and Least Significant Bit (LSB) steganography. This research developed a steganography system using the prototype method to embed complex documents into video media securely. It uses Python with libraries like OpenCV and PyCryptodome, embedding data into the blue channel's LSB bit to maintain visual quality. The system was tested using various video samples and documents to ensure error-free embedding and 100% accurate extraction, with no file corruption. Robustness against compression and format conversion was also evaluated using metrics like PSNR, SSIM, and BER. The study successfully created a secure system for embedding complex digital documents into video media. Evaluation confirmed high visual quality (PSNR 45.3-63.2 dB), 100% data recovery, and resilience to post-processing, a significant advance over methods that handle only simple payloads.
Copyright (c) 2025 Nafis Artaruna Putra, Muhammad Fajar Sidiq, Arif Amrulloh https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Sistem Pakar Forward Chaining dan Decision Tree untuk Deteksi Hama berbasis WhatsApp
The Indonesian agricultural sector faces challenges in early detection of land disturbances such as pests, diseases, floods, and droughts. Low technology adoption and digital literacy among farmers lead to slow responses to early symptoms. The research aims to develop a web-based decision support system that integrates the decision tree method, forward chaining inference, and sending automatic classification results via WhatsApp. The research used a Waterfall model, encompassing needs analysis, design, implementation, testing, and maintenance. The system is built based on 24 input symptoms that generate five classification categories. This application allows farmers to enter land condition data and receive classification results directly through the system and WhatsApp notifications. Testing demonstrated 80% accuracy compared to expert diagnoses, with a 95% message delivery success rate and an average response time of under five seconds. These results demonstrate that a rules-based approach combined with real-time communication can improve the speed and effectiveness of decision-making at the farmer level. This solution has the potential to be applied in other regions as part of accelerating the digital transformation of agriculture.
Copyright (c) 2025 Zaki Maulana Iskhak, Eko Darmanto, Syafiul Muzid https://creativecommons.org/licenses/by-sa/4.0/
Sleeption: Aplikasi Deteksi Gerakan Video Real-time berbasis Mobile untuk Pemantauan Gangguan Tidur Bayi
A baby's quality sleep is very important for his or her growth and development. Infant sleep disturbances have a negative impact on their physical health. Monitoring baby's sleep becomes very important to identify potential sleep disorders and ensure optimal sleep quality for babies. This research aims to build an android application called Sleeption using the waterfall model, a mobile-based baby sleep pattern monitoring application connected to a Raspberry Pi 3 device as an edge computing device. The development phase includes the stages of analysing the needs of parents in monitoring activities, application design, implementation of application design, verification of the success of features in the application, application and application testing along with periodic maintenance on the application. Data collection is done by observation with the development team on the needs of parents in conducting baby sleep monitoring activities. The result of our findings is a mobile application that helps parents in the process of monitoring their baby's sleep activities. The results of black box testing show that the Sleeption application is able to provide accurate and real-time information with a latency of 41 ms about the baby's sleep activity. The application of the Sleeption application helps parents in monitoring their baby's sleep patterns, along with the automation of reports on changes in baby sleep patterns helping parents to identify potential sleep disorders more accurately.
Copyright (c) 2025 Dodik Dwi Andreanto Andreanto, Muis Muhtadi, Haffas Zikri Ariyandi https://creativecommons.org/licenses/by-sa/4.0/
Community Detection in Scientific Collaboration Networks using the Louvain Method
This research aims to identify the collaboration of authors in scientific publication networks, which is increasingly important due to the rapid growth of research output using the Louvain method. Understanding the structure of collaboration can help reveal the formation of research communities and the key actors within them. To achieve this, we use three centrality metrics, each of which has a function to help understand collaboration between authors. Authors with ID P107 in degree centrality, authors with ID 177 in betweenness centrality and authors with ID P174 closeness centrality. In applying the Louvain method, the results show a fairly high modularity score of 0.7689, which indicates optimal separation between clusters. This finding shows that although Louvain is quite efficient in separating communities.
Copyright (c) 2025 Ghina Briliana Fatin Octariana, Heri Wijayanto, I Gede Darmawan Adi Pratama Putra https://creativecommons.org/licenses/by-sa/4.0/
Sistem Klasifikasi Kematangan Apel Fuji berdasarkan Warna menggunakan KNN untuk Sortasi Otomatis
Manual fruit sorting typically relies on workers' visual observation to assess ripeness. This assessment is heavily influenced by individual experience and lighting conditions, often leading to inaccuracies. Furthermore, manual methods are time-consuming, increase the risk of misclassification, and reduce operational efficiency. Our research aims to develop a color-based Fuji apple ripeness classification application using the K-Nearest Neighbor algorithm that combines RGB and HSV features. Our research is developmental research using the Waterfall model, consisting of requirements analysis, design, implementation, testing, and maintenance. We used 240 fuji apple images sourced from images taken in the Kudus area. Our findings are an automatic classification application capable of classifying apple images into three ripeness levels: unripe, semi-ripe, and ripe. The evaluation results showed an accuracy of 93.75% with balanced precision, recall, and f1-score across all classes, confirming the system's stable performance without any indication of bias. Testing results using the black-box method in three scenarios opening the application, uploading an image, and reclassifying proved that all features performed as expected. The implication is that this application is ready for use in camera-based sorting in horticultural production lines and can be developed for other fruit classifications, supporting widespread post-harvest digitalization.
Copyright (c) 2025 Ahmad Inzul Maula, Wiwit Agus Triyanto, Pratomo Setiaji https://creativecommons.org/licenses/by-sa/4.0/
Sistem Informasi Pengelolaan Persediaan berbasis Safety Stock pada Industri Konveksi Seragam Polisi
Manual stock management often leads to various problems, such as delayed information, recording errors, and low operational efficiency, highlighting the need for a more integrated and efficient inventory information system. This study aims to develop a web-based inventory management information system that can automatically implement the safety stock and reorder point methods. The system is designed to determine the minimum inventory level, calculate reorder timing, and provide alerts when stock reaches the minimum threshold to support more accurate restocking decisions. This is applied research that uses the Waterfall model to develop a web-based inventory system for a garment manufacturing business. The stages include needs analysis through observation and interviews, system design using flowcharts and use case diagrams, implementation using PHP and MySQL, and testing using the black-box method. Our findings resulted in an information system equipped with safety stock and reorder point features to automatically determine the minimum stock level and reorder timing. The test results showed that all features functioned properly, increasing data entry speed, improving data accuracy, and supporting better restocking decisions. This system can be used by small businesses that require efficient, real-time, and structured inventory management.
Copyright (c) 2025 Faris Ahmad Farhan, Raden Rhoedy Setiawan, Yudie Irawan https://creativecommons.org/licenses/by-sa/4.0/
Aplikasi Chatbot Interaktif Pembelajaran Bahasa Pemrograman PHP dengan Algoritma NLP berbasis BERT
The digitalization of education facilitates access to information; however, beginners still face challenges in understanding programming languages such as PHP. This study aims to develop a chatbot based on Natural Language Processing (NLP) using the Sentence-BERT model (all-MiniLM-L6-v2) to understand user questions in natural language contextually. The research follows a prototyping development method, consisting of several stages: needs identification to determine relevant features for users; interface design to create an intuitive and user-friendly layout; web-based system implementation to realize system functions; and testing using the black-box method to ensure each feature works as specified, along with usability evaluation using the System Usability Scale (SUS) to assess user comfort and ease of use. The result is a chatbot application capable of matching user questions with a Q&A database using semantic similarity. All testing scenarios ran as expected. The SUS evaluation yielded a score of 89.58, indicating a very high level of user satisfaction. This research demonstrates that the integration of NLP and BERT can enhance the effectiveness and convenience of independent programming learning and has the potential to be applied to other educational platforms.
Copyright (c) 2025 Rangga Febrio Waleska, Hadi Asnal, Rahmiati Rahmiati, Gunadi Gunadi https://creativecommons.org/licenses/by-sa/4.0/
Pengembangan Video Pembelajaran Teknik Vokal sebagai Media Audio Visual Interaktif
Learning vocal techniques requires an understanding of physical, technical, and musical aspects, but many students have difficulty mastering diaphragmatic breathing, articulation, resonance, and tone control due to the limitations of media that are only oral or text-based. Therefore, teachers need more flexible and interesting learning media. This research aims to develop vocal technique learning videos as interactive audio-visual media. This research uses the Research and Development (R&D) method with the ADDIE development model which consists of analysis, design, development, implementation, and evaluation stages. The research subjects were 11th grade students of SMA N 1 Masbagik. The results of our research are in the form of vocal technique learning videos as interactive audio-visual media. The results of testing by media experts show that this media is very feasible (85.75%) and material media experts give an assessment that this media is also very feasible (86.2%). Meanwhile, the results of user responses to this media are very high (85.6%). These results indicate that the learning media is effective and very feasible as a medium for learning vocal techniques.
Copyright (c) 2025 Wahyu Maulana Alhafizi, Alwan Hafiz https://creativecommons.org/licenses/by-sa/4.0/
Studi Eksperimen Keamanan Jaringan Wi-Fi Kampus: Analisis Kerentanan terhadap Serangan Evil Twin dan Deauthentication
The increasing use of Wi-Fi in higher education also brings security risks, such as Evil Twin Attacks that trick users into connecting to fake access points. This study aims to assess the vulnerability of Universitas Mataram’s Wi-Fi network to such attacks using a multihop model and to propose technical improvements. An ethical penetration testing method was applied using a NodeMCU ESP8266 with Deauther firmware, tested across 13 campus locations. Observed variables included the number of connected devices, user interaction with phishing pages, deauthentication success, and captured credentials. The results reveal that five out of 13 locations (38.46%) were vulnerable, where users were redirected to fake SSIDs and entered credentials, even though most deauthentication attempts failed. These findings highlight that attack success depends not only on deauthentication but also on firmware variation and AP configuration. The study implies the need for network security audits, firmware standardization, stronger authentication with full encryption, and enhanced user awareness to reduce phishing risks.
Copyright (c) 2025 Kia Putri Asiana, Raphael Bianco Huwae, Andy Hidayat Jatmika https://creativecommons.org/licenses/by-sa/4.0/
Segmentasi Pelanggan Menggunakan Kerangka LRFMV dan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran
In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means algorithm to identify customer behavior characteristics and determine high-value segments. The combination of these five dimensions has rarely been used in previous studies, thus providing a new contribution to data-based customer behavior analysis. This study adopts an exploratory descriptive quantitative approach. The data used consists of 2,098 transactions from 452 customers, sourced from a public GitHub dataset. The data analysis process includes preprocessing, determining LRFMV values, and segmentation using K-Means Clustering. The Silhouette Coefficient is used to evaluate cluster quality and determine the optimal number of clusters. The results show that the best configuration is obtained at k=5 with a Silhouette value of 0.842. The findings show five customer segments with different characteristics and Customer Lifetime Value (CLV) values. Clusters 0 and 2 are categorized as Loyal Customers (L↑R↓F↑M↑V↑) with the highest CLV. Clusters 3 and 1 are Inactive New Customers (L↓R↑F↓M↓V↓) with low contribution. Cluster 4 consists of Inactive Customers (L↓R↓F↓M↓V↓), indicating overall inactivity. These segmentation results are used to develop more targeted strategies, such as loyalty programs or reactivation campaigns, to optimize marketing strategies based on customer value.
Copyright (c) 2025 A. Nolly Sandra Wawagalang, Syahrullah Syahrullah, Rizka Ardiyansyah, Dwi Shinta Angreni, Septiano Anggun Pratama, Deny Wiria Nugraha https://creativecommons.org/licenses/by-sa/4.0/