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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
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 aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). 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: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 202 Documents
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Comparison of LDA and BERTopic in Identifying Public Issues in the MBG Program Nur Hayati; Saikin Saikin; Hairul Fahmi
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2782

Abstract

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.
Short-Term and Long-Term Forecasting of Global Gold Prices Using LSTM and GRU Models Fitria Fitria; Muhammad Syahid Pebriadi; Nitami Lestari Putri
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2723

Abstract

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.
Evaluation of Fresnel Zone in Ubiquiti Wireless Simulation and Implementation Using 802.11ac Ahmad Tantoni; Mohammad Taufan Asri Zaen
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2752

Abstract

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
Deployment of a Machine Learning Based Indoor Air Quality Predictor on NodeMCU ESP32 Using EloquentTinyML Yaddarabullah Yaddarabullah; Umar Al Faruq
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2847

Abstract

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.
Development of a Dairy Cattle Facial Recognition System Based on CNN for Digital Livestock Data Management Andika Muhammad Nur Kholiq; Arief Suryadi Satyawan; Mokh Mirza Etnisa Haqiqi; Arief Abdillah; R Helkhan Sultan Fajar; Yusril Kamil
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2741

Abstract

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
Accuracy Comparison of Support Vector Machine, Random Forest, and K-Nearest Neighbors for Sundanese Speech Classification Laela Nur Rohmah; Abdul Halim Anshor; Wahyu Hadikristanto
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2757

Abstract

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.
Virtual Geocultural Tour of the Baduy Tribe at Bayah Dome Geopark Toward an Inclusive Global Geopark Dentik Karyaningsih; Ahmad Sugiyarta; Donny Fernando; Reza Pramudita; Irfan Hadi; Muhammad Aris Febriawan
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2681

Abstract

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.
Development of a Marker-Based Augmented Reality Application for Computer Hardware Learning Using the Multimedia Development Life Cycle Muhamad Adila Syahputra; Ade Syahputra; Silvester Dian Handy Permana
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2857

Abstract

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.
A Data-Driven Machine Learning Framework for Cybersecurity Risk Prediction Using Behavioral and Temporal Features from Email Server Logs Frowin Rabanus Kifaru
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2742

Abstract

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.
Usability Evaluation of the Yellow Cart Feature in the TikTok Shop Application Using the System Usability Scale (SUS) Hikmah Sani Nadia; Evy Nurmiati; Nuryasin nuryasin
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2762

Abstract

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.

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