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All Journal IC Tech: Majalah Ilmiah IC Tech: Majalah Ilmiah
Chrinstian Yulianto Rusli
Institut Widya Pratama
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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ANALISIS KLASTERISASI DATA TRANSAKSI PENYEWAAN PAKAIAN MENGGUNAKAN ALGORITMA K-MEANS UNTUK IDENTIFIKASI KARAKTERISTIK PELANGGAN SEBAGAI DASAR STRATEGI INOVASI DAN STOK Much Rifqi Maulana; Arochman; Chrinstian Yulianto Rusli
IC Tech: Majalah Ilmiah Vol 21 No 1 (2026): IC Tech: Majalah Ilmiah Volume XXI No. 1 April 2026
Publisher : P3M Institut Widya Pratama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47775/ictech.v21i1.402

Abstract

This study aims to analyze clothing rental patterns and identify customer segmentation of Nani Collaction Clothing Rental, as an effort to support strategic decision-making in the rental business. The method used is the K-Means algorithm with the determination of the optimal number of clusters using the Elbow Method. The data analyzed include clothing categories, costs, rental time, clothing types (men/women), and sizes (children/adults). The results show that the data can be grouped into four main clusters, namely the profession-based children's segment, the culture-based children's segment, the adult segment with high transaction value, and the niche youth segment. The children's segment has the largest transaction volume and is therefore the main market, while the adult segment contributes more to revenue. Meanwhile, the youth segment has specific and seasonal demand characteristics. The analysis results show that the clustering approach is effective in identifying customer patterns and can be used as a basis for developing business strategies, particularly in stock management, service innovation, and market segmentation. This study also recommends the implementation of segmentation-based strategies, the utilization of reservation technology, and the development of further research by adding more diverse variables and clustering methods.
ANALISIS KLASTERISASI DATA TRANSAKSI PENYEWAAN PAKAIAN MENGGUNAKAN ALGORITMA K-MEANS UNTUK IDENTIFIKASI KARAKTERISTIK PELANGGAN SEBAGAI DASAR STRATEGI INOVASI DAN STOK Much Rifqi Maulana; Arochman; Chrinstian Yulianto Rusli
IC Tech: Majalah Ilmiah Vol 21 No 1 (2026): IC Tech: Majalah Ilmiah Volume XXI No. 1 April 2026
Publisher : P3M Institut Widya Pratama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47775/ictech.v21i1.402

Abstract

This study aims to analyze clothing rental patterns and identify customer segmentation of Nani Collaction Clothing Rental, as an effort to support strategic decision-making in the rental business. The method used is the K-Means algorithm with the determination of the optimal number of clusters using the Elbow Method. The data analyzed include clothing categories, costs, rental time, clothing types (men/women), and sizes (children/adults). The results show that the data can be grouped into four main clusters, namely the profession-based children's segment, the culture-based children's segment, the adult segment with high transaction value, and the niche youth segment. The children's segment has the largest transaction volume and is therefore the main market, while the adult segment contributes more to revenue. Meanwhile, the youth segment has specific and seasonal demand characteristics. The analysis results show that the clustering approach is effective in identifying customer patterns and can be used as a basis for developing business strategies, particularly in stock management, service innovation, and market segmentation. This study also recommends the implementation of segmentation-based strategies, the utilization of reservation technology, and the development of further research by adding more diverse variables and clustering methods.
Co-Authors Arochman Much Rifqi Maulana