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Journal of Information Technology and Computer Science
Published by Universitas Brawijaya
ISSN : 25409433     EISSN : 25409824     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
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Articles 284 Documents
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Naive Bayes with SMOTE for Predicting the Competitiveness of Vocational School Graduates on Imbalanced Data (Case Study: SMK Negeri 3 Malang) Arsy Kurnia Fitri; Wijoyo, Satrio Hadi; Hariyanti, Uun
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111870

Abstract

Vocational high schools (SMK) aim to produce work-ready graduates. However, the open unemployment rate (TPT) for SMK graduates remains high at 9.01%, indicating a significant competency gap. This study designs a model to predict graduates workforce competitiveness using the Naive Bayes algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE is employed to address the class imbalance between capable and incapable graduates. The study follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, utilizing academic scores and tracer study datasets. Evaluation results demonstrate that applying SMOTE with a 70:30 train-test split successfully increased model accuracy to 97%. Notably, the model effectively detects the minority class with a Recall of 90%. Furthermore, cross-validation yielded an average accuracy of 97.66%, demonstrating stable performance. Finally, the model was implemented as a web-based dashboard to serve as an early warning system for schools.
Designing and Evaluating the User Interface of a Socratic Dialogue Chatbot to Enhance Student Engagement in Online Learning Platforms Widjanarko, Alexander; Haq, Isri Amirul; Mubarok, Berlian Davis Dwi; Purnawirawan, Okta
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111886

Abstract

History education in Phase F is often perceived as monotonous and dominated by rote memorization, with 92.9% of students feeling burdened and 89.3% feeling bored. Standard generative AI tools may worsen this issue by functioning as instant answering machines, fostering cognitive dependency. This study aims to describe the User Interface (UI) and User Experience (UX) design process of “Kak Sarah,” a Learning Management System (LMS) platform featuring a chatbot designed to act as a Socratic facilitator through historical role-play, and to evaluate its usability and impact on student engagement. The UI/UX development employed the Successive Approximation Model (SAM) integrated with the Design Thinking approach, consisting of empathize, define, ideate, prototype, and test stages. The platform’s usability was evaluated using the System Usability Scale (SUS) with 136 respondents, while its impact on engagement was tested on 312 Grade XI students across five high schools. Results indicate that the UI design successfully translated four narrative learning stages into an interactive interface. The usability evaluation produced a SUS score of 86.09 (Excellent / Grade A). Observation data and conversation logs showed that all students engaged in reflective thinking rather than accepting direct answers, supported by a 35.03-point increase in critical thinking scores (p = 0.005). These findings suggest that integrating SAM and Design Thinking can produce an effective Socratic chatbot interface that enhances student engagement in history learning.
Technology Architecture and System Design Patterns of Immersive E-Learning in Building Engineering Education: A Systematic Literature Review Mubarroq, Ahmad Yudi; Muhammad Aris Ichwanto; Luhur Adi Prasetya; Dea Nisfuha Anindya; Tee Tze Kiong
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111901

Abstract

The digital transformation of Industry 4.0 necessitates a fundamental restructuring of Architecture, Engineering, and Construction (AEC) pedagogy through high-fidelity immersive environments. However, current systems often prioritize isolated interaction over the underlying technology architectures required for long-term effectiveness. This research conducted a systematic review of 100 peer-reviewed articles (2018–2025) using the PRISMA 2020 framework and CASP quality appraisal instrument to map functional system configurations. Results indicate a significant paradigm shift toward integrated computational ecosystems, where adaptive intelligent units (30%) and sustainability-oriented processing layers (20%) function as core architectural components. These layers enable dynamic cognitive regulation and real-time environmental metric integration, such as Life Cycle Analysis (LCA), directly within virtual design workflows. Furthermore, findings demonstrate that scalability barriers in vocational settings are increasingly mitigated through cloud-native architectures and IFC-based interoperability. This study concludes that the future of immersive e-learning depends on “Sustainability-by-Design” and scalable architectures, providing a strategic blueprint for intelligent, institution-wide digital infrastructures in engineering education..  
Applying a Constructivist Learning Approach in Designing Educational Games to Enhance Conceptual Understanding of Geometric Shapes Muhammad Rizki Arzi; Putri Ayu Andini; Manda Apriliani Fadzillah; Alya Andini Prasetyo; Ahmad Zulfi'Azwan Nur; Okta Purnawirawan
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111920

Abstract

Mathematics instruction on plane figures still faces challenges in the form of students’ low conceptual understanding and a limited availability of interactive learning media. This study aims to develop MathMind, an educational game based on Game-Based Learning with a constructivist approach, to improve students’ understanding of geometric concepts at SD Negeri 2 Pandanrejo. The study employed the Research and Development method using the ADDIE model. MathMind was developed as a level-based game featuring gamification elements such as rewards, timers, lives, and interactive feedback. Evaluation was conducted through expert validation, users response testing, and pretest posttest analyses using a paired-sample t-test. The results showed a validity rate of 96% from media experts and 98% from content experts, as well as a user response rate of 94%. The pretest and posttest results indicated a significant improvement in students’ conceptual understanding with a p-value < 0.001 and a high effect size. These results demonstrate that MathMind effectively enhances elementary school students’ engagement and understanding of plane figure concepts.
Evaluating the Success Factors of a Tourism Management Information System Using the DeLone and McLean Model: A User Perspective Hidayat Syah, Rizqi Mulyantara; Oky Dwi Nurhayati; Bayu Surarso
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111925

Abstract

This study evaluates the success factors of a tourism management information system implemented by startup XYZ using the DeLone and McLean Information Systems Success Model. Startups in the tourism industry are becoming more dependent on digital platforms, which prompted this research because there has been little user-centered evaluation of tourism information systems. Involving 75 active users of the system, a quantitative explanatory method was used. A systematic questionnaire with 44 questions was used to gather data. The questions covered topics such as system quality, information quality, service quality, use, user satisfaction, and net benefits. Research methods encompassed creating instruments, collecting data, analyzing it statistically, checking reliability using Cronbach's Alpha, and expert validation with Aiken's V. The instrument demonstrated strong validity and reliability with an overall Aiken's V value of 0.8247. Additionally, all constructs attained Cronbach's Alpha values above 0.70, further supporting these findings. Descriptive statistics, construct correlation, multiple regression, coefficient of determination (R²), and F-test analyses were used to examine the data. All descriptive factors had high mean values, suggesting that users had a good impression of the system. System quality and service quality had a substantial impact on user happiness, according to regression analysis, but information quality and system quality had a substantial impact on system usage. Perceived net benefits were also heavily impacted by system utilization and user satisfaction. The results show that the DeLone and McLean model may be used to evaluate TISs. Making the system more responsive, making the information more accurate, and improving the user support services should be the goals of future enhancements.
Combining Inference Results of YOLOv8 and Faster R-CNN using Weighted Boxes Fusion for Car’s Underbody Quality Inspection (Study Case On Automotive Company In Indonesia) Alfons Abilo, Nelson; Indriati; Setya Perdana, Rizal
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101918

Abstract

The automotive industry in Indonesia has gradually experienced rapid development over time, leading to intense competition among automotive companies. In such a competitive environment, the quality inspection process is a critical aspect of the automotive manufacturing industry. One significant issue in the quality inspection process is human error. The Fourth Industrial Revolution offers solutions through computer vision technology to improve production quality. YOLOv8 is a real-time object detection algorithm that offers fast inference and good background reduction, but it is less effective for small and low-contrast objects. Conversely, Faster R-CNN has high confidence scores but slow inference capabilities. Company XYZ has a visual inspection system using YOLOv8, with a mAP50@95 value of 74%. However, the confidence scores produced often do not meet the company's standard threshold, which requires a confidence score above 90%. Therefore, a study was conducted to combine the inference results of YOLOv8 and Faster R-CNN using the weighted boxes fusion method to enhance the inference results of YOLOv8. The study results showed an increase in mAP when combining the inferences of the two models, compared to when each model performed inference individually – there was a 3.8% increase compared to YOLOv8's performance and a 5.5% increase compared to Faster R-CNN's performance.  
Comparative Analysis of Machine Learning Algorithms for Optimal Decision-Making in Data-Driven Applications Fatyanosa, Tirana Noor; Brata, Gede Indra Adi; Reansyah, Javier Aahmes; Athaya, Haikal Thoriq; Adam, Muhammad Herdi; Aranda, Achmad Fauzi
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101921

Abstract

This research examines the effectiveness of machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine, Decision Tree, Logistic Regression, and Gradient Boosting, applied to Kaggle competition datasets. It also investigates essential data preprocessing techniques, such as Standard Scaler, Power Transform, and Simple Imputer, to enhance model performance. The paper provides a comprehensive analysis of best practices in model selection and data preparation to achieve optimal results across different domains. From our experimental results on different datasets, we found that combining Logistic Regression with XGBoost and using ensemble methods like XGBoost and CatBoost achieved the highest scores in several Kaggle competitions. Moreover, using preprocessing such as missing value imputation, data normalization, and feature engineering significantly improved the performance of all models. Our findings suggest that the selection of appropriate machine learning algorithms and data preprocessing techniques is crucial for achieving optimal results in data-driven decision-making. By employing these methodologies, we achieved commendable results across the utilized datasets. For instance, the Predict Failure Keep It Dry dataset yielded a Kaggle score of 0.5901, while the Smoker Status using Bio Signals dataset achieved a score of 0.8675. The Bank Churn Dataset resulted in a Kaggle score of 0.89046, the Spaceship Titanic dataset scored 0.80617, and the health of horses dataset attained a score of 0.76212. These results highlight the effectiveness of the proposed combination of machine learning algorithms and preprocessing techniques in enhancing model performance on real-world datasets.
A Comparative Study: Can Deep Learning Outperform Tree-Based Models in Tabular Data Classification? Fatyanosa, Tirana Noor; Hilmi, Fadhilah; Taqiyassar, Kenzie; Pratama, Naufal Romero Putra; Satrio Condro Kusuma; Hafiz Rizky Nurwachid
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101922

Abstract

The rapid growth of data in various domains has heightened the need for accurate and efficient predictive models, particularly for tabular data. While deep learning has revolutionized fields like computer vision and natural language processing, its effectiveness in tabular data classification remains a topic of debate. This study conducts a comprehensive comparison between deep learning models (NODE, SAINT, TabNet, Tab Transformers) and tree-based models (Random Forest, XGBoost, LightGBM, CatBoost) to determine whether deep learning can outperform traditional methods in this context. The results indicate that tree-based models, particularly LightGBM and CatBoost, consistently achieve the highest accuracy and F1 scores, coupled with efficient execution times, making them more suitable for real-world applications that require quick and accurate predictions. In contrast, deep learning models show varied performance. Although SAINT sometimes achieves comparable accuracy, its processing time makes it less practical. The findings suggest that despite the potential of deep learning, tree-based models remain superior for tabular data classification tasks, particularly when considering a balance of accuracy, speed, and robustness. This study contributes to the ongoing discussion on the role of deep learning in tabular data and highlights the conditions under which traditional models may still be preferred.
Leveraging Convolutional Neural Networks Preprocessing for Accurate Age and Gender Classification in Personalized Nutrition Fadhillah, Muhammad Aulia Nur; Al Hakim, Shidiq; Rodiah, Rodiah; Data, Mahendra; Siagian, Al Hafiz Akbar Maulana; Riyanto, Slamet; Madenda, Sarifuddin; Apriani, Niken Fitria; Maukar, Maukar
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101923

Abstract

Imagine having a personalized nutrition plan that caters to your unique dietary needs based on your age and gender. Such a system could revolutionize the way we approach health and wellness. A key component of this vision is the accurate classification of age and gender from facial images, which can be leveraged to provide tailored nutritional recommendations. In this paper, we explored the use of convolutional neural networks and their preprocessing techniques to enhance the performance of age and gender classification models. Our age classification aimed to identify the age group according to the regulation of the Indonesian Republic's Health Ministry in 2014 about the guidelines for balanced nutrition, which includes the following categories: 10-12 years old, 13-15 years old, 16-18 years old, 19-29 years old, and 30-49 years old. We utilized the ResNet50 and Inception-v3 models, which were fine-tuned on the UTKFace dataset, a collection of more than 20,000 face images with corresponding age and gender labels. However, the UTKFace dataset suffers from a data imbalance problem. To address this issue, we proposed innovative data augmentation methods to create a more balanced dataset. Our experimental results demonstrated that our proposed augmented methods could significantly improve the classification performances of the models, leading to more accurate age and gender predictions. This advancement in facial attribute classification could pave the way for developing personalized nutrition systems that cater to individual needs and preferences, ultimately improving health outcomes and quality of life.
Volitional Fatigue Monitoring System Using Random Forest With Root Mean Square and Integrated Electromyogram Feature Audrian, Nathaniel; Widasari, Edita Rosana
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101924

Abstract

Athletes often hire personal trainers (PTs) for strength training, where training to volitional fatigue maximizes results but increases injury risk. This study proposes a volitional fatigue monitoring system to assist PTs in preventing fatigue-induced injuries. The system utilizes electromyography (EMG) with features derived from RMS and IEMG signals, with Random Forest classification method. Outputs are displayed on an OLED screen, LED lights, and a website via the WebSocket Protocol. EMG signal disturbances were mitigated with a filter, a battery, and a sport band. Testing involved five subjects aged 20-22 with various arm strength and no exercise background. The test results show that the EMG sensor acquires data within the appropriate range. The system achieved a 91.43% accuracy in muscle fatigue detection and a 1.0284 second average computation time, and produced the expected outputs with 100% accuracy. Therefore, the proposed monitoring system is feasible and reliable for volitional fatigue monitoring.

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