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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 42 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.

Page 5 of 5 | Total Record : 42

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