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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 45 Documents
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Systematic Literature Review Find Novelty Analysis on Hand Sign Recognition Using Vosviewer Wijaya, Robertos
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 Sahertian, Vigo Yano; 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.

Page 5 of 5 | Total Record : 45

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