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International Journal of Information Technology and Business
Published by Universitas Kristen Satya Wacana
ISSN : 26559293     EISSN : 2655495X     DOI : 10.24246
Core Subject : Science,
Computer Science & IT
Information Technology Management Information System E-commerce Computational Intelligence Information Infrastructure Cyberspace Enterprise Resource Model Business Intelligence Diffusion and Future IT Network Management IoT Infrastructure
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 49 Documents
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Systematic Literature Review Find Novelty Analysis on Hand Sign Recognition Using Vosviewer Robertos Wijaya
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.01-05

Abstract

This study presents a systematic literature review of Hand Sign Recognition (HSR) technologies, focusing on advancements from 2015 to 2025. Analyzing 500 articles from Google Scholar using VOSViewer, we identify key trends, challenges, and gaps in the field. Findings reveal a predominant focus on static gesture recognition using deep learning models like CNNs and YOLO, with accuracies exceeding 90% in many cases. However, dynamic gesture recognition, robustness to lighting variations, and integration of facial expressions remain understudied. Bibliometric analysis highlights declining publication trends in recent years, signaling a need for innovative approaches, such as hybrid models and interdisciplinary collaboration. This review underscores the importance of addressing real-world deployment challenges to enhance accessibility for individuals with hearing or speech disabilities.
Customer Loyalty Analysis Using RFM Model and K-Means Clustering for Marketing Strategy Optimization Vigo Yano Sahertian; Yessica Nataliani
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.212025.01-07

Abstract

This study aims to segment customers to measure their level of loyalty using the RFM (Recency, Frequency, Monetary) model approach combined with the k-Means clustering algorithm. The dataset used comes from the Kaggle site and contains motor vehicle sales data, both cars and motorbikes, with a total of 2,747 transactions. The RFM method is used to calculate three important indicators of customer behavior, namely the last time to make a purchase (recency), purchase frequency (frequency), and total transaction value (monetary). The data is then normalized and grouped using the k-Means algorithm. Based on the results of the Elbow Method and Silhouette Score tests, the optimal number of clusters obtained is four. The segmentation results show four groups of customers with different characteristics, ranging from very loyal customers with high frequency and large transaction values, to customers who have been inactive for a long time. This segmentation is very useful for companies to design more targeted marketing strategies and increase customer retention. This study shows that the combination of RFM and k-Means clustering is able to provide significant insights in understanding consumer behavior and supporting data-based strategic decision making.
Cost-Sensitive Fraud Detection with Reliability Calibration: A Practical Pipeline with XGBoost and Focal-Proxy Reweighting Danang Danang; Toni Wijanarko Adi Putra
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.13-24

Abstract

Fraud detection on payment transactions is an extremely imbalanced, high-stakes classification task in which deployment decisions depend not only on ranking quality but also on reliable probability estimates. We study credit card fraud detection on a standard real-transaction benchmark (284,807 transactions; 492 frauds) and target two deployment requirements: cost-sensitive thresholding under asymmetric error costs and reliability calibration so model outputs can be interpreted as stable risk scores. We benchmark logistic regression and XGBoost and propose a focal-proxy reweighting scheme for boosted trees via iterative weight updates inspired by focal loss. Probabilities are calibrated on validation using Platt scaling, temperature scaling, and isotonic-style monotone calibration; the best calibrator is selected by minimum validation Brier score. For decision-making, we choose the operating threshold that minimizes expected cost, Cost(t) = 10 · FN(t) + 1 · FP(t), on validation, then evaluate on a held-out test set. On the benchmark split (train 199,364; validation 42,721; test 42,722), the calibrated XGBoost baseline achieves AUROC 0.973, AUPRC 0.812, fraud-class F1 0.767, and expected cost 154 with very low calibration error (ECE = 1.1 × 10⁻⁴). Overall, calibration reduces ECE and improves or maintains the Brier score, while cost-aware thresholding makes the FN/FP trade-off explicit via decision curves. 
Privacy Protection and Trust in the Digital Era: A Systematic Review of Data Breach Impacts on SDG Progress Toni Wijanarko Adi Putra; Danang Danang
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.24-34

Abstract

Objective – In the digital transformation era, the integrity of personal data has become essential for maintaining trust and ensuring the sustainability of digital services. This paper aims to systematically review how data privacy violations affect public trust and progress toward Sustainable Development Goals (SDGs), especially SDG 9 (infrastructure and innovation) and SDG 16 (strong institutions and justice). Methodology—This study adopts the Systematic Literature Review (SLR) approach based on Kitchenham’s framework. Relevant articles from 2021–2025 were retrieved from Scopus, IEEE, Springer, and ScienceDirect using a predefined search string aligned with PICOC. A total of 19,504 records were screened, and 36 high-quality studies were selected after applying inclusion/exclusion criteria and quality assessment tools (e.g., CASP, AMSTAR). Findings—The review reveals that sectors such as education, healthcare, and smart cities are increasingly adopting data protection technologies, including encryption, federated learning, differential privacy, and blockchain. However, many still face regulatory, infrastructural, and human literacy gaps. Breaches in personal data significantly reduce public trust, impair the exercise of digital rights, and pose ethical and operational risks for achieving SDGs. Limitations – The study is limited by the timeframe (2021–2025) and focuses primarily on peer-reviewed literature. Practical insights from developing countries may be underrepresented due to database indexing limitations. Contribution – This review contributes a cross-sectoral synthesis of technological and regulatory practices for data protection, identifies key challenges, and outlines a strategic roadmap for policymakers and technologists to integrate ethical data governance for sustainable digital futures.
Determination of Maintenance Priority Based on Analytical Hierarchy Process for Magnetic Resonance Imaging Nurdono Nurdono; Muhamad Haddin
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.1-7

Abstract

Magnetic Resonance Imaging (MRI) is a high-tech medical diagnostic equipment that plays an important role in healthcare, but in its operation it faces problems in the form of very large maintenance costs. This is due to the complexity of MRI technology, where the main unit of MRI is an imported product, while supporting equipment such as UPS, chiller, and AHU system are domestic products, thus impacting the increase in maintenance costs in hospitals and potentially reducing the quality of service. The solution to reduce maintenance costs, because maintenance cost efficiency can increase hospital profits. This study discusses the determination of maintenance priorities on MRI using a multi-criteria-based decision-making model by considering: the age of the MRI, the number of error logs, the condition of supporting equipment, and the expertise of the operator in operating the MRI. The Analytical Hierarchy Process (AHP) method is used with the stages of forming a decision hierarchy, pairwise comparisons, matrix normalization, priority weight calculation, and consistency testing. Data were obtained through questionnaires given to competent respondents, with the object of research at the Orthopedic Hospital, Surakarta, Indonesia. The results of the study indicate that the AHP method can be used to determine MRI maintenance priorities effectively. This is evidenced by the best alternative results, namely the Medium type (0.3372), followed by All Risk (0.3315), and Labor Only (0.3311). The Medium type is the most optimal choice. The AHP method has been proven to provide objective, structured, and accountable recommendations for hospital management in determining maintenance contracts that are appropriate to the technical condition of the equipment and budget constraints.
Modeling Factors Affecting Vocational School Students’ Perceived Ease of Use of Educational Technology Masruri Hafid; Krismiyati Krismiyati; Andrushia Diana
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.32-39

Abstract

The integration of educational technology has become increasingly important in supporting effective learning processes. Google Drive is one of the widely used cloud platforms for online storage, access, and collaboration in educational activities. This study aims to examine the factors influencing vocational school students’ perceived ease of use (PEoU) of Google Drive as a learning support tool. The research model was adapted from the Technology Acceptance Model (TAM) by incorporating external variables, including Computer Self-Efficacy (CSE), Perceived Enjoyment (PE), Perceptions of External Control (PEC), Technological Complexity (TC), and Facilitating Conditions (FC). Data were collected from 274 students at a public vocational school in Central Java using an online questionnaire. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS. The findings reveal that Computer Self-Efficacy, Perceived Enjoyment, and Perceptions of External Control significantly influence students’ perceived ease of use of Google Drive. In contrast, Technological Complexity and Facilitating Conditions do not significantly affect perceived ease of use. These findings highlight the importance of internal user perceptions in supporting educational technology adoption among vocational school students.
An Adaptive AI-Driven Copywriting Framework: Design, Implementation, and Evaluation of a Web-Based GPT-Integrated Content Generation System April Firman Daru; Febrian Wahyu Christanto; Rastri Prathivi; Dimas Prasetyo; Eryan Ahmad Firdaus
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.8-17

Abstract

The increasing demand for scalable and high-quality digital marketing content has exposed limitations in traditional manual copywriting processes, which are time-intensive and difficult to scale. This research proposes an adaptive AI-driven copywriting framework that integrates a full-stack web architecture with optimized prompt engineering strategies for automated content generation. The system is implemented using React.js for the frontend, Node.js with Express for backend services, and a GPT-based API for language generation. Unlike prior implementations, this research introduces a structured prompt optimization mechanism to enhance content relevance and consistency. Experimental evaluation was conducted using multiple datasets of marketing prompts, with comparisons against baseline GPT usage and manual copywriting. Quantitative results show that the proposed system achieves improvements in BLEU (+18.7%) and ROUGE-L (+21.3%) scores over baseline methods. Human evaluation involving 30 participants indicates a significant increase in perceived content quality, coherence, and persuasiveness (p < 0.05). System performance analysis demonstrates an average response time of 1.8–3.0 seconds and a GTmetrix performance score of 82%. The findings confirm that the proposed framework significantly enhances efficiency, scalability, and content quality, contributing to both applied AI systems and intelligent web-based content production.
Multi-Objective Reinforcement Learning for Supply Chain Optimization in Indonesia’s Free Nutritious Meal Program Budi Yanto; Rusmin Saragih; Adyanata Lubis; Elyandri Prasiwiningrum; Romy Wahyuny
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.18-27

Abstract

Indonesia’s Free Nutritious Meal Program (MBG) requires an efficient and adaptive supply chain system to ensure timely distribution, cost efficiency, and adequate nutritional delivery for a large number of beneficiaries. However, conventional supply chain approaches are generally static and unable to respond effectively to dynamic demand, supply uncertainty, and logistical constraints. This study proposes a Multi-Objective Reinforcement Learning (MORL) model to optimize the MBG supply chain by simultaneously considering distribution cost, delivery timeliness, service level, nutritional adequacy, and food waste reduction. The model is developed using a simulation-based environment representing real-world supply chain conditions, including demand variability, transportation limitations, and kitchen capacity constraints. The results show that the proposed approach achieves cost reductions of 15–22%, improves delivery timeliness by 18–25%, maintains a service level above 90%, increases nutritional fulfillment by 12–18%, and reduces food waste by 10–15% compared to baseline methods. Sensitivity analysis further demonstrates the robustness of the model, with minimal performance degradation under disruption scenarios. These findings indicate that Reinforcement Learning provides a scalable and adaptive solution for optimizing large-scale public food distribution systems. The proposed model contributes both theoretically by integrating multi-objective optimization within an RL framework and practically by supporting data-driven decision-making for improving the effectiveness of the MBG program in Indonesia.
Resilience of Deep Q-Network (DQN) Agent in Mitigating Ethereum Trading Risks Under Bearish Market Conditions Fajri Adha; Tiwuk Widiastuti; Bertha Selvian Djahi
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.28-34

Abstract

The high volatility of cryptocurrency assets, particularly Ethereum, poses a significant challenge for investors during market downturns. Traditional passive strategies often lead to substantial capital erosion in bearish conditions. This study explores the application of Deep Reinforcement Learning (DRL) through the Deep Q-Network (DQN) algorithm to develop an adaptive trading agent. By integrating technical indicators—Relative Strength Index (RSI), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD)—the proposed model aims to optimize decision-making processes. Experimental results using historical data from 2020 to 2026 demonstrate that while the market experienced a significant decline of 19.55%, the DQN agent successfully maintained capital stability with a marginal deviation of only -0.54%. This finding suggests that the DQN-based approach offers superior risk mitigation and capital preservation capabilities compared to conventional buy-and-hold strategies in volatile financial environments.

Page 5 of 5 | Total Record : 49

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