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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Rama Mahendra
Universitas Muria Kudus
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Penerapan Metode RFM Analysis dan K-Means Clustering untuk Manajemen Pelanggan pada Zhe Homewear Muhammad Rama Mahendra; Eko Darmanto; Syafiul Muzid
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9452

Abstract

Abstrak - Zhe Homewear, sebuah usaha mikro di bidang fashion, menghadapi masalah rendahnya tingkat pembelian ulang pelanggan yang hanya mencapai 28%, akibat tidak adanya sistem pengelolaan data pelanggan yang terpusat dan terstruktur. Penelitian ini bertujuan untuk mengembangkan sistem Customer Relationship Management (CRM) berbasis web yang dapat menganalisis perilaku pelanggan menggunakan metode Recency, Frequency, Monetary (RFM) dan melakukan segmentasi melalui algoritma K-Means Clustering. Sistem ini dirancang untuk mengelola histori transaksi, mempermudah analisis segmentasi pelanggan, serta mendukung strategi pemasaran yang lebih personal. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan berhasil mengelompokkan pelanggan menjadi segmen loyal, potensial, dan pasif, serta meningkatkan efektivitas strategi pemasaran berbasis data. Selain itu, pencatatan manual berhasil dieliminasi dan pengelolaan data pelanggan menjadi lebih terpusat dan efisien.Kata Kunci : CRM; RFM Analysis; K-Means Clustering; loyalitas pelanggan; sistem informasi pelanggan; Zhe Homewear; Abstract - Zhe Homewear, a micro-business in the fashion sector, faced a low customer repurchase rate of only 28%, caused by the absence of a centralized and structured customer data management system. This study aimed to develop a web-based Customer Relationship Management (CRM) system capable of analyzing customer behavior using the Recency, Frequency, Monetary (RFM) method and segmenting customers through the K-Means Clustering algorithm. The system was designed to manage transaction histories, facilitate customer segmentation analysis, and support more personalized marketing strategies. The results indicated that the developed system successfully classified customers into loyal, potential, and passive segments, and improved the effectiveness of data-driven marketing. Moreover, manual recording was eliminated, and customer data management became more centralized and efficient.Keywords: CRM; RFM Analysis; K-Means Clustering; customer loyalty; customer information system; Zhe Homewear;
Co-Authors Eko Darmanto Syafiul Muzid