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All Journal Journal of Informatics and Computer Science (JINACS) Modem : Jurnal Informatika dan Sains Teknologi Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Education Technology and Information System Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
I Kadek Dwi Nuryana
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Modem : Jurnal Informatika dan Sains Teknologi

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Penerapan Klasifikasi Pelanggan Berdasarkan Segmentasi Pelanggan pada UMKM Monex Toys Bekasi Eugenea Chiquita Zahrani Assyarif; I Kadek Dwi Nuryana
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 3 (2025): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i3.533

Abstract

This study aims to conduct customer segmentation and develop a classification model to predict the clusters of new customers at Monex Toys Abadi Bekasi, a micro, small, and medium enterprise (MSME). Segmentation was performed using the K-Means Clustering algorithm, incorporating parameters such as Recency, Frequency, Monetary (RFM), purchased products, payment methods, shipping cost discounts, and the total number of products purchased by customers. The segmentation results revealed two clusters: (1) Discount Hunters and (2) Loyal Customers. Subsequently, a classification process was conducted to predict customer clusters using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. Evaluation results indicated that all models achieved high accuracy exceeding 98%. The best-performing model was obtained with SVM using a 70:30 data split, achieving an accuracy of 98.81%. This classification model was then implemented into a Streamlit-based cluster prediction application, enabling users to identify customer segments in real-time. The findings of this research are expected to assist MSMEs in understanding customer behavior, enhancing service quality, and supporting more effective marketing strategies.
Co-Authors Achmad Mauludi Asror Alfi Azqia Uzzulfa Haq Alfina Dian Febyani Aries Dwi Indriyanti Balqis Nur Aura Shaviradilla Bonda Sisephaputra Eugenea Chiquita Zahrani Assyarif Ghea Sekar Palupi Kistofer, Terdy Kurrotul Uyun M Wildan Kafafi M. Renaldi Wildan F Mawarni, Lintang Iqhtiar Dwi Muhammad Fatkhur Rizal Mujianto, Ahmad Heru Mukhtarul Fata An Nadwi Tanhella Zein Vitadiar Wilda Zakaria Nur Abidin