Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal International Journal of Information Technology and Computer Science Applications (IJITCSA)
Wowon Priatna
Universitas Bhyangkara Jakarta Raya, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Application of K-Means Clustering in Grouping Customer Preferences for K-Pop Albums And Merchandise Aditiya Dwi cahyo; Wowon Priatna; Agus Hidayat
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.206

Abstract

The increasing popularity of K-Pop in Indonesia is particularly in the purchase of physical products. THJMINE Store faces challenges in inventory management and promotional strategies due to the lack of product grouping for albums and merchandise. This study applies the K-Means Clustering algorithm to 110 sales transaction data from July 2022 to January 2025. The method used in this study is the CRISP-DM approach, which consists of the following stages: business understanding, data understanding, data preparation, modeling, and evaluation discussion. The result of the study shows that the K-Means algorithm successfully formed three clusters with customer classification: loyal customers (cluster 0), general customers (cluster 1), and premium or collector customers (cluster 2). The model evaluation results in a DBI score of 0.6342, indicating good cluster quality. These clustering results can help THJMINE Store understand customer segmentation, develop more targeted marketing strategies, and improve inventory management efficiency.
Association Pattern Analysis of Production Results Using the Apriori Algorithm Zacky Achmad Sholeh; Wowon Priatna; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.208

Abstract

This study aims to analyze association patterns in production data at CV. Sinar Agung Teknik using the Apriori algorithm. The company faces challenges in identifying co-produced product relationships, which complicates production pattern recognition. The research adopts the Knowledge Discovery in Databases (KDD) approach, comprising data selection from three months of daily production, data cleaning, transformation into transactional format, application of the Apriori algorithm, and result visualization. Key parameters applied in the mining process include support, confidence, and lift. The analysis was conducted from 1-itemset to 5-itemset combinations to determine product co-occurrence frequencies. The results revealed several significant association rules. One notable rule shows that the production of Karet Membran TT, Panel Pressure Destec, and Plat C Starcam is followed by Join Tuas Starcam and Karet Membran COM, with a confidence of 90% and a lift value of 2.25. A lift greater than 1 indicates a strong correlation among the products. These findings are expected to provide data-driven insights that can support decision-making in warehouse management, inventory control, and the strategic arrangement and retrieval of products
Co-Authors Aditiya Dwi cahyo Agus Hidayat Muhammad Yasir Zacky Achmad Sholeh