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Journal of Information Technology
Published by Universitas Bhinneka Nusantara
ISSN : 23031425     EISSN : 2580720X     DOI : https://doi.org/10.32664/j-intech
Core Subject : Science,
Computer Science & IT
Journal of Information and Technology is a journal published by Bhinneka Nusantara University, Malang. The scope of this journal includes IT Governance, IS Strategic Planning, IS Theory and Practices, Management Information System, IT Project Management, Distance Learning, E-Government, Information Security and IT Risk Management, E-Business / E-Commerce, Big Data Research, and other related topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 347 Documents
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Clarifying Organizational Requirements through Rapid Application Development: Evidence from a Performance Appraisal Information System Handy Setiawan; Koko Wahyu Prasetyo
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2249

Abstract

This study analyzes the iterative application of Rapid Application Development (RAD) in the engineering of an employee performance appraisal information system within a higher education context. The system was developed to replace a spreadsheet-based process that involved manual score aggregation and fragmented data storage. Using a software engineering case study approach, this research examines how system requirements evolved across development iterations. Design artifacts produced during successive RAD cycles were analyzed as evidence of requirement refinement. The findings show that early development focused on functional centralization, while later iterations introduced configurable modules for evaluation criteria, evaluator assignment, and score normalization. These interface and module revisions reflect progressive clarification of organizational needs and stabilization of business logic. The study demonstrates that RAD supports requirement refinement through iterative prototyping, especially in systems involving multiple roles and configurable structures. The results provide process-oriented insight into requirement evolution in internal organizational information systems.
Hybrid PSO Feature Selection Correlation and Support Vector Machine Model for Heart Disease Detection Sarina Safitri; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2251

Abstract

Heart disease remains a major health problem worldwide. The World Health Organization (WHO) reports that in 2022, approximately 19.8 million people died from heart disease, highlighting the need for the implementation of an appropriate early detection model. This study proposes a hybrid SVM–PSO model with correlation-based feature selection, duplicate data handling, and a multi-metric fitness function to enhance classification performance. PSO is employed to optimize the C parameter and RBF kernel of SVM, producing a more robust and balanced model compared to existing approaches. This study uses a heart disease dataset consisting of 1,025 rows with 13 attributes and 1 target variable obtained from the Kaggle repository and republished on the Zenodo platform in 2024. The research stages include Pre-Processing, Standardization, Feature Selection based on Correlation, and evaluation using the 10-Fold Cross Validation technique with Accuracy, precision, recall, and F1-score metrics. The results show that Support Vector Machine (SVM) achieved an Accuracy of 82.80%, Precision of 79.31%, Recall of 91.70%, and an F1-score of 84.88%. After optimization using PSO, the performance improved to an accuracy of 84.46%, precision of 80.54%, recall of 92.72%, and an F1-score of 86.04%. The experimental results indicate performance improvements of 2.00% in accuracy, 1.55% in precision, 1.11% in recall, and 1.37% in F1-score after PSO optimization. These results prove that the applied hybrid approach successfully improved the ability to detect heart disease. Therefore, this study contributes by demonstrating that PSO-based hyperparameter optimization can effectively enhance SVM classification performance for heart disease detection. The proposed model also has practical implications as a decision support tool for early heart disease detection that can assist medical practitioners in improving diagnostic accuracy and supporting preventive treatment strategies.
Development of a Teacher Administrative Information System Digitally-Based Inside the Classroom Sandi Rohmatullah Alifa; Firman Jaya; Nur Azizah
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2253

Abstract

The development of information technology encourages educational institutions to undergo digital transformation in administrative management, including student attendance recording. MA Syamsul Jinan is still using a manual attendance system that causes various problems, such as low efficiency, a high potential for recording errors, difficulties in data storage, and the risk of attendance data manipulation. This study aims to develop a web-based digital attendance system as part of a classroom administrative information system to improve the efficiency, accuracy, and transparency of managing student attendance data. The research method used is Research and Development (R&D) with the Waterfall development model which includes requirements analysis, design, implementation, testing, and maintenance. Data were collected through observation, questionnaires, and documentation. System quality evaluation refers to the ISO/IEC 25010 standard with a focus on six aspects, namely functionality suitability, usability, reliability, user satisfaction, maintainability, and portability. The results show that all system features function well, with a usability level of 87.6% which falls into the very good category. The system has reliability, ease of maintenance, and can run optimally on various devices and browsers. Thus, the web-based digital attendance system is declared feasible to implement and capable of improving the quality of educational administration at MA Syamsul Jinan.
Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance Eka Yuniar; Sherin Ramadhania; Pascawati Savitri Universitasari; Mas'ud Hermansyah; Akas Bagus Setiawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2256

Abstract

Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. The increasing number of sellers on the platform intensifies competition and requires data-driven approaches to understand store performance patterns. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach that integrates clustering, spatial analysis, and feature importance evaluation. The dataset consists of 655 Shopee stores collected on February 18, 2026, including attributes such as number of products, chat response rate, follower count, store rating, store tenure, promotional activity, and seller address. K-Means clustering is applied to segment store performance, while spatial analysis examines the geographic distribution of clusters across Indonesian provinces. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features affecting store competitiveness. The clustering results reveal three distinct store performance groups representing low, medium, and high activity levels. Spatial analysis indicates that provinces with stronger digital ecosystems, particularly West Java and Jakarta, contain a higher concentration of active stores. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. The findings contribute to the development of hybrid data mining frameworks for marketplace analysis and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
Design of an Enterprise Architecture for Monitoring IT Services and Infrastructure Using TOGAF ADM at PT Fratama Kencana Gemilang Karina; April Lia Hananto; Bayu Priyatna; Agustia Hananto
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2275

Abstract

The management of information technology infrastructure at PT Fratama Kencana Gemilang currently faces significant operational challenges in its day-to-day operations. This is due to the device monitoring mechanisms currently in place, which remain manual and fragmented across units, resulting in the IT team often only becoming aware of technical issues after receiving user complaints. This reactive approach inevitably hinders company productivity, particularly regarding server services that form the core of the business. Therefore, this study aims to design a more proactive, automated, and integrated enterprise system monitoring architecture using the TOGAF ADM (The Open Group Architecture Framework Architecture Development Method) framework. Through this approach, it is expected that all of the company’s technology assets can be centrally monitored and aligned with long-term strategic business objectives. This research employs a qualitative descriptive approach conducted through direct observation of the existing system infrastructure and in-depth architectural modeling. This design process covers various key domains in a structured manner, ranging from the vision domain, business architecture, information system architecture, to the supporting technology infrastructure. The research results indicate that the proposed open-source-based monitoring system design has successfully met the company’s functional and technical requirements comprehensively. This is evidenced by the results of the expert validation process (expert review), which yielded an average score of 4.5 out of 5.0. These results confirm that the designed system is highly effective in providing real-time and accurate visibility into infrastructure performance. This study concludes that the proposed architecture and resulting blueprint are highly suitable to serve as the primary reference for company management in enhancing the reliability of their IT services. The implementation of this design is expected to accelerate the troubleshooting process and minimize the risk of future system failures.
Explainable and Fair Credit Risk Scoring with Counterfactual Explanations: A Reproducible Evaluation on the German Credit Dataset (HELOC-Motivated) Qi Xin
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2228

Abstract

Credit risk scoring requires models that are accurate, fair, and able to provide actionable explanations for adverse decisions. Motivated by the HELOC explainable lending benchmark, but using the publicly downloadable Statlog German Credit dataset as a fully reproducible proxy when direct HELOC access is constrained, this study evaluates explainable and fair credit scoring on 1,000 applicants. We train five common models—logistic regression (LR), decision tree (DT), random forest (RF), XGBoost (XGB), and LightGBM (LGBM)—on a fixed 600/200/200 train/validation/test split with a consistent preprocessing pipeline. Thresholds are selected on the validation set to maximize F1 for bad-risk detection. LR achieves the best test AUC (0.7888), LGBM the highest test accuracy (0.6550), and RF the best calibration (ECE=0.0473), showing that discrimination, thresholded accuracy, and calibration do not align. Fairness is audited by a derived sex attribute using demographic parity and equalized odds. Baseline LGBM shows an approval-rate difference of −0.0846 (female minus male) and an equalized-odds gap of 0.1000. Reweighing reduces the approval-rate difference to −0.0642 while preserving AUC (0.7804), and equal-opportunity thresholding reduces the equalized-odds gap to 0.0750. For individual explainability, we generate counterfactual recourse for rejected applicants using six actionable features. Feasible recourse is defined as the existence of at least one action-constrained counterfactual that changes the decision from reject to approve; 78.64% of rejected applicants receive such recourse, with mean cost 1.5298 measured as standardized numeric change plus categorical steps. Across five retraining seeds, LGBM AUC is stable (mean 0.7742, std 0.0022), but fairness gaps vary. The study provides a reproducible template for jointly evaluating performance, calibration, fairness, mitigation, and recourse in credit scoring
From Small Data to MMM-Style Budget Decisions: Reproducible Conjugate Bayesian Log-Response Regression with ElasticNet Benchmarks and Explainable Budget Scenario Rollouts Yifei Lu; Jinyi Mu; Arthur Lefebvre
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2262

Abstract

Marketing mix modeling (MMM) is often treated as a large-sample, time-series task, but many organizations must make near-term budget decisions with limited historical data. This study develops a reproducible small-sample MMM-style workflow using the public ISLR Advertising.csv dataset (n=200 markets; TV, radio, newspaper spend; sales). Reproducibility is ensured by using a public dataset, fixed random seeds, explicitly stated preprocessing, analysis scripts supplied with the manuscript, and specified Python libraries. The workflow combines predictive benchmarking and prescriptive decision support. First, OLS, Ridge, Lasso, and ElasticNet are evaluated under 5-fold cross-validation and a fixed 80/20 hold-out split. Second, a conjugate Bayesian regression on log(1+spend) features models diminishing returns and yields closed-form posterior and Student-t predictive distributions. Third, for each total budget B, the allocation that maximizes the posterior mean prediction is solved under non-negativity, budget-balance, and observed-maximum channel caps; posterior samples are then evaluated at each optimized allocation to form 90% credible bands for the budget-sales curve. On the fixed split, test RMSE ranges from 1.829 to 1.871, while 100 repeated splits indicate that raw-feature OLS is most accurate on average (mean RMSE 1.671). At B=200, the bounded optimum allocates $147.25k to TV, $49.60k to radio, and $3.15k to newspaper, predicting 18.149 sales. The results suggest that, in small samples, regularization and log-response modeling mainly support stable, interpretable budget recommendations rather than improving point prediction alone.
Implementation of a Plant Disease Identification System using the CNN Algorithm and the Web-Based Django Framework Hendri Ardiansyah; Syifa Ilafiah
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2277

Abstract

This study addresses the need for efficient and accessible plant disease identification systems in the era of Agriculture 4.0, where advances in artificial intelligence (AI) and machine learning (ML) support data-driven agricultural practices. The increasing popularity of home gardening highlights challenges faced by users in identifying plant diseases due to limited knowledge and diagnostic tools. Therefore, this research aims to develop a web plant disease detection system using the Django framework and convolutional neural networks (CNNs). The model was trained on a controlled dataset consisting of 57,320 leaf images collected from the PlantVillage and Turmeric Plant Disease datasets. Image preprocessing was applied, including resizing, normalization, and data augmentation such as image rotation, zooming, image inversion and brightness adjustmen. Class imbalance during training was handled using class weighting. The dataset is divided into a training set and a validation set for model development and evaluation. The CNN model achieved an accuracy of 92% on the labeled validation dataset, with a mean F1 score of 0.79 and a weighted mean F1 score of 0.92. For generalization testing, an uncontrolled (wild) dataset consisting of 223 images collected from online sources was used, resulting in an accuracy of 11%, indicating limited real-world generalization due to domain differences. Despite this limitation, the proposed system demonstrates the feasibility of CNN-based plant disease classification in a web application.
Web-Based 360 Degree Virtual Tour Video for Promoting Cultural Tourism at Pura Puseh and Pura Desa Batuan Gede Wirya; Putu Gede Maha Vivaldi Pradnyana
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2282

Abstract

Pura Puseh and Pura Desa Batuan in Gianyar Regency are prestigious National Cultural Heritage sites characterized by ancient Balinese architecture and deep historical roots dating back to 1020 AD. Despite their significance, a reliance on conventional promotional media has resulted in a lack of immersive experiences for tourists, often causing the site to be overlooked as a mere transit point. This study addresses this gap by designing and developing an immersive, web-based Virtual Tour 360 Video platform using the systematic ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The production utilized 360-degree 4K panoramic videos, edited with Adobe Premiere and 3DVista, integrating hotspot navigation and AI-animated characters to enhance storytelling. Technical validation via Black Box Testing on 6 functional test cases (BB-01 to BB-06) resulted in a 100% "Succeed" status, confirming that all features operate seamlessly. Furthermore, usability testing conducted with 30 respondents using the System Usability Scale (SUS) yielded an average score of 83.1, placing the platform in the "Excellent" category. The findings confirm that 360-degree virtual tour technology is an effective and accessible medium for digital cultural heritage promotion, with implications for the development of immersive tourism platforms for other heritage sites.
Rotten Apple Detection Using YOLOv12 for Postharvest Quality Sorting Bradika Almandin Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2290

Abstract

Detecting rotten apples is critical in postharvest quality sorting, as spoiled fruit can accelerate overall decay, shorten shelf life, and lower market value. This study introduces a real-time, edge-deployable object detection method using YOLOv12 to differentiate between fresh and rotten apples in RGB images. The dataset included 2,312 annotated images, with 1,011 fresh apples and 1,301 rotten apples, split into training, validation, and testing sets with an 80:10:10 stratified ratio. To enhance model generalization, data augmentation techniques such as mosaic augmentation, horizontal flipping, rotation, scaling, HSV color jitter, and mixup were applied. The YOLOv12s model was trained with an input resolution of 640 × 640 and evaluated using accuracy, precision, recall, and F1-score. The results from the confusion matrix showed that the model achieved an accuracy of 0.93, precision of 0.91, recall of 0.89, and F1-score of 0.90, indicating that YOLOv12 offers a lightweight and effective framework for rapid apple quality assessment. The primary contribution of this work lies in integrating an attention-focused YOLOv12 detector into a postharvest apple sorting workflow, accompanied by quantitative performance evaluation and robustness analysis under challenging visual conditions.

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