JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi and in collaboration with the Indonesian Artificial Intelligent Ecosystem (IAIE) aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” (Vol 5,No 1, June 2022 - Vol 9 No 2, December 2026) by The Ministry of Research-Technology and Higher Education Republic of Indonesia (SK No. 230/M/KPT/2022), Article Processing Charge will be applied when the article has been declared accepted before payment is made by the author. JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year). This Journal accepts research articles in these following fields: 1. Software Engineering: Web Development, Mobile Apps Development, Database Management System 2. Multimedia: Augmented Reality, Virtual Reality, Game Development 3. Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing 4. Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System Authors are invited to submit articles that have not been published previously and should follow the Author Guideline [here] [Make a Submission] [Download Author Guideline] [Download Article Template] SCOPUS Citation Analysis [Click Here] Research Field from Dimensions [Click Here]
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Virtual Geocultural Tour of the Baduy Tribe at Bayah Dome Geopark Toward an Inclusive Global Geopark
The Bayah Dome Geopark in Lebak Regency possesses significant geological and cultural heritage; however, it currently holds the status of an "Aspiring Geopark." A primary challenge in optimizing its geotourism potential—particularly for the Baduy tribe—is the physical accessibility constraint caused by adverse weather conditions and rugged terrain. This study aims to develop and evaluate a mobile-based 360-degree virtual tour website as an inclusive digital geocultural and educational medium. The research follows a Research and Development (R&D) approach using the Multimedia Development Life Cycle (MDLC) framework, encompassing concept, design, material collection, assembly, testing, and distribution stages. The system integrates 360-degree panoramic visualizations with interactive navigation and informational hotspots to represent Baduy’s local wisdom. Empirical results from black-box testing confirmed 100% functional validity across all system features. Furthermore, a User Acceptance Test (UAT) conducted using a Likert scale yielded an average acceptance index of 82.8%, categorizing the platform as "Very Good." This virtual tour serves as a strategic digital solution for cultural preservation and sustainable geotourism promotion, effectively surmounting geographical and temporal barriers for a global audience.
Copyright (c) 2026 Dentik Karyaningsih, Ahmad Sugiyarta, Donny Fernando, Reza Pramudita, Irfan Hadi, Muhammad Aris Febriawan https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence Adoption in Learning Systems: Systematic Literature Review
The integration of Artificial Intelligence (AI) in education has accelerated rapidly, reshaping teaching and learning processes through intelligent, adaptive, and data-driven systems. Despite its widespread adoption, a consolidated understanding of implementation trends, benefits, and challenges across educational contexts remains limited. This study aims to examine the current state of AI adoption in education by identifying key applications, success factors, and implementation barriers. A systematic literature review (SLR) was conducted by analyzing 32 peer-reviewed journal articles published within the last five years and indexed in reputable academic databases. The reviewed studies focus on adaptive learning systems, intelligent tutoring systems, and learning analytics. The findings demonstrate that AI contributes significantly to personalized learning, real-time performance assessment, and improved learner engagement. However, effective implementation is strongly influenced by institutional readiness, educator digital literacy, data quality, and ethical governance. Major challenges identified include data privacy concerns, lack of standardization, and unequal access to technological infrastructure. This study concludes that AI has substantial potential to support more adaptive and inclusive educational systems, but sustainable integration requires coordinated efforts among educators, policymakers, and technology developers.
Copyright (c) 2026 Hendi Sama, Aisyah Nurkayla, Stefanus Eko Prasetyo https://creativecommons.org/licenses/by-sa/4.0
Classification of High-Risk Provinces for Fintech Lending Based on TWP90 from LPBBTI OJK Using Interpretable Model for Artificial Intelligence Ethical Transparency
The growth of the fintech lending industry (LPBBTI) in Indonesia has expanded access to financing but has also increased credit risk, as reflected in the 90-day Default Rate (TWP90). This condition demands proactive regional risk monitoring, while existing approaches are still dominated by historical descriptive analysis. This study aims to develop a high-risk provincial classification as a transparent and accountable early warning system. The research methodology uses multi-sheet data integration from the Financial Services Authority (OJK) LPBBTI Statistics to create a provincial-monthly panel dataset covering supply, demand, and transaction activity. The Logistic Regression model was used as the baseline model due to its interpretability and support for decision auditability. Model evaluation using a time-based split approach during the May–July 2025 test period demonstrated good performance, with an ROC-AUC of 0.8598, an accuracy of 0.8462, and a precision of 0.8000. The results of feature analysis indicate that scale and activity indicators, particularly the outstanding amount, the number of active borrowers, and the value of disbursed funds, contribute significantly to risk probability. Although detection sensitivity (recall: 0.5333) still needs improvement, this study provides a measurable, relevant regional risk-ranking framework for regulatory decision-making.
Copyright (c) 2026 Adi Rizky Pratama, Ayu Ratna Juwita, Antika Zahrotul Kamalia, Jan Everhard Riwurohi https://creativecommons.org/licenses/by-sa/4.0
Short-Term and Long-Term Forecasting of Global Gold Prices Using LSTM and GRU Models
Global gold prices exhibit high volatility and complex temporal patterns, making accurate forecasting a challenging task. This study aims to compare the performance of deep learning models for short-term and long-term gold price prediction using daily historical data. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were selected because both models can capture temporal dependencies in financial time-series data, while having different architectural complexities and learning characteristics. Comparing these models is important to identify the most suitable approach for different forecasting horizons. The dataset consists of daily global gold prices denominated in USD obtained from an open financial data source covering the period from 2010 to 2024. The models were evaluated under two forecasting horizons, namely short-term prediction (1 day ahead) and long-term prediction (30 days ahead). Model performance was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Experimental results indicate that the GRU model outperforms LSTM in short-term forecasting by producing lower prediction errors, while LSTM demonstrates slightly better stability in long-term forecasting. These findings suggest that the effectiveness of deep learning models for gold price prediction is highly dependent on the forecasting horizon.
Copyright (c) 2026 Fitria Fitria, Muhammad Syahid Pebriadi, Nitami Lestari Putri https://creativecommons.org/licenses/by-sa/4.0
Development of a Dairy Cattle Facial Recognition System Based on CNN for Digital Livestock Data Management
Dairy cattle data management plays a crucial role in supporting operational efficiency and the sustainability of the livestock sector. However, conventional practices at Mamad Jaya Farm in animal identification still present severe operational and animal welfare challenges. The existing traditional marking methods carry high physical risks for the livestock, frequently leading to torn ears from ear-tagging, severe skin irritation from branding, and high human-error risks in manual paper-based record-keeping. To mitigate these invasive drawbacks, this study aims to develop an artificial intelligence-based facial recognition system for dairy cattle as a non-invasive solution to support digital and integrated livestock data management. A Convolutional Neural Network (CNN) architecture was uniquely implemented for the identification process due to its superior capacity to automatically and hierarchically extract complex spatial biometric features from facial images without manual feature engineering. The research methodology involved collecting a multi-angle facial dataset from a herd of 15 dairy cattle at Mamad Jaya Farm, Karangpawitan, Garut, which was then processed using Roboflow. The developed model was integrated into a web-based livestock platform named BonvaLink. Empirical testing on 15 distinct cattle facial images demonstrated that the system achieved an individual identification accuracy of 93.33%, with the majority of correct predictions yielding robust confidence scores. These results indicate that the CNN-based biometric approach is highly effective and reliable in recognizing individual dairy cattle identities under practical barn environments
Copyright (c) 2026 Andika Muhammad Nur Kholiq, Arief Suryadi Satyawan, Mokh Mirza Etnisa Haqiqi, Arief Abdillah, R Helkhan Sultan Fajar, Yusril Kamil https://creativecommons.org/licenses/by-sa/4.0
A Data-Driven Machine Learning Framework for Cybersecurity Risk Prediction Using Behavioral and Temporal Features from Email Server Logs
This study presents a data-driven machine learning framework for cybersecurity risk prediction using behavioral and temporal features extracted from email server logs. The dataset consists of 955 authentication records, including protocol types, login outcomes, error classifications, timestamps, and spam scores. Initial statistical analysis, comprising descriptive statistics, correlation analysis, and chi-square tests, was conducted to examine relationships among variables and feature relevance. Subsequently, supervised machine learning models, logistic regression, decision trees, and random forests, were implemented to classify cybersecurity risk events. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results indicate that the Random Forest model outperformed other models, achieving the highest AUC of 0.84, compared to 0.723 for logistic regression. The findings demonstrate that integrating behavioral and temporal features significantly enhances the detection of cybersecurity threats. This study highlights the effectiveness of ensemble learning methods in capturing complex patterns within log data and provides a robust framework for developing intelligent intrusion detection systems. The proposed approach offers practical implications for improving cybersecurity monitoring and risk prediction in real-world email-based communication environments.
Copyright (c) 2026 Frowin Rabanus Kifaru https://creativecommons.org/licenses/by-sa/4.0
Development of a School Profile Website as a Digital Information Medium Using the Waterfall Method
The development of information technology encourages educational institutions to utilize digital media in delivering information more effectively. However, SMPN 4 Praya Tengah still uses conventional methods in disseminating information, resulting in limited reach and inefficiency. This study aims to design and implement a school profile website as a digital information medium that can be widely accessed by students, parents, and the community. The method in this study uses the Waterfall model, which was selected due to its systematic and structured development stages that are well-suited for projects with clearly defined and stable requirements. The Waterfall model includes the stages of analysis, design, implementation, and testing. This research integrates PHP and JavaScript programming languages with a MySQL database. The resulting system provides comprehensive features ranging from school profile, vision and mission, news, announcements, gallery, to online admission services (PPDB). The website is designed with a simple and user-friendly interface so it can be well managed by the school. Through Black Box Testing, this website has been proven to function optimally in presenting information accurately and in a structured manner. System validation was conducted through two methods: Black Box Testing, which confirmed all features operate according to specifications, and User Acceptance Testing (UAT) involving 50 respondents with 16 evaluation items, yielding an average score of 78.50% (categorized as 'Agree'). These results empirically validate that the implementation of this website provides measurable benefits in improving the effectiveness of information delivery, expanding access, and supporting digital transformation within the school environment. This system is expected not only to facilitate digital transformation in schools but also to serve as a reference for the development of other school websites.
Copyright (c) 2026 muhammad mustakim, sofiansyah fadli, ahmad tantoni https://creativecommons.org/licenses/by-sa/4.0
Evaluation of Computer-Based Testing Website Quality Using the DeLone and McLean Information System Success Model
The rapid development of information technology has significantly impacted the education sector, particularly in the implementation of computer-based examination systems. This study aims to analyze and evaluate the quality of a Computer-Based Testing (CBT) website at SMK Bangun Bangsa using the DeLone and McLean Information System Success Model. A quantitative approach was employed by collecting primary data through questionnaires distributed to 260 students from grades X and XI of SMK Bangun Bangsa who had experience using the CBT system. The respondents represented various demographic backgrounds in terms of gender, age, and academic majors. The variables analyzed include system quality, information quality, service quality, user satisfaction, and net benefits. Data analysis was conducted using descriptive statistical methods, supported by validity and reliability testing to ensure the accuracy and consistency of the research instrument. The results indicate that the CBT system has generally performed well. System quality obtained a score of 75.4% (good category), indicating that the system is easy to use despite minor technical issues. Information quality achieved 77.7% (very good), reflecting that the system provides accurate, relevant, and understandable information. Service quality recorded the lowest score of 72.2% (good), suggesting the need for improvement in security and technical support. Meanwhile, user satisfaction reached 81.6% and net benefits 80%, both categorized as very good, indicating that the system enhances efficiency and provides a positive user experience. This study contributes to evaluating the effectiveness of CBT systems in vocational education environments and highlights the importance of improving service quality and system reliability to achieve optimal system performance. Future research is recommended to expand the study scope and integrate additional models for deeper analysis.
Copyright (c) 2026 Muhamad Sakinul Wadi, Sofiansyah Fadli, Mohammad Taufan Asri Zaen https://creativecommons.org/licenses/by-sa/4.0
Evaluation of Fresnel Zone in Ubiquiti Wireless Simulation and Implementation Using 802.11ac
The development of wireless communication technology is increasing along with the need for high-speed internet access, both in urban and rural areas. Point-to-Point (PtP) technology based on the IEEE 802.11ac standard is one of the main solutions for providing long-distance connectivity, particularly in backhaul networks, ISPs, and inter-building communication. However, the network implementation process often relies on link simulations using software such as ISP Design Center or AirLink, which do not always accurately represent field conditions. Wave propagation factors, terrain contours, and obstacles in the Fresnel Zone significantly affect signal quality. The Fresnel Zone is a three-dimensional propagation area that must be at least 60% free of obstacles for optimal transmission. Previous research has shown limitations in studies related to evaluating the accuracy of Fresnel Zone simulations on Ubiquiti 802.11ac devices. Therefore, this research evaluates and compares the parameters of RX Signal Strength, throughput, channel width, and Fresnel Zone clearance between simulation results and direct field testing on a PtP connection. The research results are expected to serve as a technical reference for network practitioners in validating simulation results and providing recommendations for more accurate and efficient PtP connection design. This research also emphasizes the importance of Line of Sight (LOS) and Fresnel Zone optimization in maintaining the stability of outdoor wireless networks based on IEEE 802.11ac
Copyright (c) 2026 Ahmad Tantoni, Mohammad Taufan Asri Zaen https://creativecommons.org/licenses/by-sa/4.0
Accuracy Comparison of Support Vector Machine, Random Forest, and K-Nearest Neighbors for Sundanese Speech Classification
To support the preservation of the Sundanese language, speech recognition systems based on machine learning canbe developed. This study aims to evaluate and compare the classification performance of Support Vector Machine, Random Forest, and K-Nearest Neighbors which represent margin-based, ensemble-based, and distance-based classification approaches that have been widely applied in speech classification tasks. A secondary dataset consisting of 100 voice recordings was utilized in this research. The study followed the Knowledge Discovery in Database framework, which encompasses data selection, preprocesing, transformation, data mining, and evaluation phases. Feature extraction was performed using the Mel-Frequecy Cepstral Coefficients method. Experimental result demonstrate that the Random Foret algorithm achieved superior performance, reaching 100% accuracy and an Area Under Curve (AUC) of 100%. Meanwhile, K-Nearest Neighbors achieved 87% accuracy with an AUC of 100%, and Support Vector Machine yielded the lowest performance with 67% accuracy and an AUC of 72.89%. Although Random Forest achieved the highest metrics, futher research is required as a perfect 100% score raises concerns regarding model overfitting. To address this issue, utilizing a large dataset is recommended for future studies. Consequently, K-Nearest Neighbors can be considered a more reliable choice in this study, demonstrating robust and stable performance for MFCC based speech classification on smaller dataset.
Copyright (c) 2026 Laela Nur Rohmah, Abdul Halim Anshor, Wahyu Hadikristanto https://creativecommons.org/licenses/by-sa/4.0
Usability Evaluation of the Yellow Cart Feature in the TikTok Shop Application Using the System Usability Scale (SUS)
TikTok Shop is a rapidly growing e-commerce platform in Indonesia, particularly through its Yellow Basket feature, which allows content creators to embed product links directly in videos. This feature has become essential within Indonesia's social commerce ecosystem. However, limited research specifically evaluates the usability of this feature from a general user perspective. This study aims to measure and evaluate the usability of TikTok Shop's Yellow Basket feature using the System Usability Scale (SUS) method. This method was selected because it is a widely validated and reliable instrument for assessing the perceived usability of interactive systems, providing a standardized measure that facilitates comparison across studies. Testing involved 50 active TikTok Shop users from various age groups, within four predefined age categories (17–21, 22–35, 36–45, and above 45 years). Data were collected through an online questionnaire consisting of 10 statements with a Likert scale and then analyzed using the standard SUS scoring formula. The evaluation yielded a mean SUS score of 71.30, categorized as “Good” according to the adjective rating scale. Dimension-level analysis identified efficiency and errors as the weakest dimensions, attributed to cognitive load induced by a concurrently active background video during catalog navigation. Thematic analysis of open-ended responses revealed three dominant concerns: pricing transparency issues, a rigid and inflexible user interface, and a perceived lack of practicality. Additionally, some of respondents reported usability difficulties suggesting an age-related usability gap not captured by the aggregate SUS score. Design recommendations are proposed to address the identified usability gaps and improve the overall user experience.
Copyright (c) 2026 Hikmah Sani Nadia, Evy Nurmiati, Nuryasin nuryasin https://creativecommons.org/licenses/by-sa/4.0
An AHP–TOPSIS-Based Decision Support System for Objective Pencak Silat Athlete Selection at KONI Lamongan
Sports achievement development requires an objective and measurable athlete selection mechanism, particularly in regional sports organizations preparing for competitive events. However, the athlete selection process conducted by Komite Olahraga Nasional Indonesia (KONI) Kabupaten Lamongan for the Pencak Silat branch in preparation for PORPROV Jawa Timur IX 2025 still relied on conventional weighting based on subjective estimation, namely 40% physical test and 60% achievement certificate assessment. This mechanism potentially generated decision bias, especially in evaluating athlete candidates with diverse physical capabilities and competition backgrounds. Therefore, this study aimed to develop a Decision Support System (DSS) using the integration of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to improve the objectivity and consistency of athlete selection. The study involved 26 athlete candidates evaluated using nine physical performance criteria and seven expert respondents consisting of KONI management representatives, coaches, and Monitoring and Evaluation (MONEV) personnel. The AHP method was applied to determine criterion weights through pairwise comparison and consistency testing, while TOPSIS was utilized to rank athlete candidates based on their proximity to ideal solutions. The results showed that Sprint 30m, Illinois Agility, Stork Stand, Standing Board Jump, and Beep Test obtained the highest priority weights of 14.94%. The consistency test produced a Consistency Ratio (CR) value of 0.0018, indicating reliable expert judgment consistency. Furthermore, the TOPSIS ranking process identified Sastro Hadi Saputro as the athlete with the highest preference value (Ci = 0.8954). The comparison between system-generated rankings and manual coach evaluations also demonstrated a high agreement level, with Spearman and Pearson correlation values of 0.9945 and 0.9863, respectively. These findings indicate that the integrated AHP–TOPSIS framework is capable of improving transparency, reducing subjectivity, and supporting more adaptive athlete selection decisions in regional sports organizations.
Copyright (c) 2026 Ahmat Rendi Saputra, Brina Miftahurrohmah2 https://creativecommons.org/licenses/by-sa/4.0
Comparison of LDA and BERTopic in Identifying Public Issues in the MBG Program
The Free Nutritious Food Program (MBG) is a government policy that has generated various public responses and opinions on social media. The large amount of unstructured text data. This study aims to compare the performance of the Latent Dirichlet Allocation (LDA) and BERTopic methods in identifying public issues related to the MBG program on TikTok data. The dataset used amounted to 13,538 data obtained through a scraping process based on keywords related to MBG. The research stages include text preprocessing, bigram and trigram formation, text representation using TF-IDF and embedding, topic modeling, and evaluation using coherence score and topic diversity. The results showed that the LDA method produced better evaluation performance with a coherence score of 0.5098 and a topic diversity of 0.9000. Meanwhile, BERTopic produced a coherence score of 0.4133 and a topic diversity of 0.7667, but was able to produce topics that were more contextual and semantically representative. Based on these results, LDA is superior in terms of the stability and quality of word associations between topics, while BERTopic is more effective in understanding the context of issues in short and unstructured social media data.
Copyright (c) 2026 Nur Hayati, Saikin Saikin, Hairul Fahmi https://creativecommons.org/licenses/by-sa/4.0
Deployment of a Machine Learning Based Indoor Air Quality Predictor on NodeMCU ESP32 Using EloquentTinyML
Indoor air quality monitoring increasingly requires local intelligence because indoor exposure conditions can change faster than centralized systems can respond. However, many machine learning based indoor air quality predictors remain difficult to deploy on microcontrollers due to memory limits, computation constraints, network dependence, and the lack of a reproducible edge deployment workflow. This study develops an end-to-end TinyML framework for deploying a lightweight indoor air quality predictor on a NodeMCU ESP32 using EloquentTinyML and TensorFlow Lite Micro. A synthetic IAQ dataset was generated from eight environmental variables, namely temperature, humidity, PM2.5, PM10, NO2, SO2, CO, and room type, and the indoor air quality score was derived from pollutant weighted features before balancing comfort labels using SMOTE. The proposed compact MLP contains eight inputs, one hidden layer with twelve neurons, ReLU activation, dropout regularization, and a single regression output. Five-fold validation produced an average root mean square error of 5.794, mean absolute error of 4.394, and R2 of 0.877, while the converted model required only 121 trainable parameters. These results indicate that compact TinyML deployment can provide a feasible proof-of-concept for local indoor air quality estimation, although physical sensor validation remains necessary.
Copyright (c) 2026 Yaddarabullah Yaddarabullah, Umar Al Faruq https://creativecommons.org/licenses/by-sa/4.0
Development of a Marker-Based Augmented Reality Application for Computer Hardware Learning Using the Multimedia Development Life Cycle
Understanding computer hardware is a fundamental competency for vocational students. However, limited access to physical hardware components often restricts practical learning activities in vocatioal schools. This study aims to develop and evaluate VirtuComp, a marker-based Augmented Reality (AR) application for computer hardware learning using the Multimedia Development Life Cycle (MDLC) methodology. The application visualizes seven essential computer hardware components as interactive three-dimensional (3D) objects to support students in recognizing and understanding hardware structures through an immersive learning experience. The development process followed the six phases of the MDLC framework. The application was evaluated through functional performance testing and usability assessment involving 68 tenth-grade students from SMK Muhammadiyah 02 Cileungsi using the User Experience Questionnaire (UEQ). The functional evaluation demonstrated that the marker-based tracking system operated reliably under adequate lighting conditions with an optimal detection distance of 4–12 cm. The usability evaluation produced positive results across all six UEQ dimensions. Efficiency achieved a score of 1.89, which was classified as Excellent. Attractiveness (1.74), Perspicuity (1.78), and Stimulation (1.38) were rated as Good, while Dependability (1.40) and Novelty (0.96) were categorized as Above Average. These findings indicate that the proposed application provides a reliable and user-friendly learning medium that supports computer hardware education, particularly in vocational schools with limited laboratory resources.
Copyright (c) 2026 Muhamad Adila Syahputra,Ade Syahputra, Silvester Dian Handy Permana https://creativecommons.org/licenses/by-sa/4.0