Product Segmentation for Apparel MSMEs Using K-Means and CRISP-DM Approach - Rahmat Hidayat (JCEIT: Journal of Computer Engineering and Information Technology, 2026)
Apparel Micro, Small, and Medium Enterprises (MSMEs) frequently provide significant sales data that is inadequately leveraged for strategic business advancement. This study intends to examine sales data utilising the K-Means Clustering technique within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The dataset consists of 360 sales records during the period 2021–2023, including factors such as amount sold, unit price, and total turnover. The analysis adheres to the systematic CRISP-DM phases: business understanding, data understanding, data preparation, modelling, assessment, and deployment. The assessment outcomes, quantified by the Silhouette Score, determined two ideal clusters for the dataset. Cluster 1 denotes well-performing products distinguished by elevated sales volume, comparatively high unit prices, and substantial turnover. Conversely, Cluster 2 comprises underperforming products characterised by diminished sales volume, reduced unit pricing, and negligible turnover. These findings offer a data-driven basis for MSMEs to develop more efficient marketing strategies and inventory management procedures. By categorising products according to performance, business owners can prioritise high-value commodities and optimise inventory for less productive categories. This study indicates that employing K-Means clustering inside the CRISP-DM framework effectively converts raw sales data into meaningful business intelligence for the garment sector. Subsequent study may enhance this technique by integrating external variables, including seasonal trends or customer demographics, to improve clustering precision. REFERENCES Abdul-Azeez, O., Ihechere, A. O., & Idemudia, C. (2024). Enhancing business performance: The role of data-driven analytics in strategic decision-making. International Journal of Management and Entrepreneurship Research, 6(7), 2066–2081. https://doi.org/10.51594/ijmer.v6i7.1257 Afari, I. S. (2023). K-medoids customer segmentation algorithm by utilizing customer relationship management. Journal of Computer Science and Information Technology. https://doi.org/10.35134/jcsitech.v9i2.69 Agustin, E. W., Uthami, K., Ulfa, A. I., Efrizoni, L., & Rahmaddeni. (2025). Optimization of customer segmentation in the retail industry using the K-medoid algorithm. MALCOM: Indonesian Journal of Machine Learning and Computer Science. https://doi.org/10.57152/malcom.v5i3.1977 Aslantaş, G., Gençgül, M., Rumelli̇, M., Özsaraç, M., & Bakirli, G. (2023). Customer segmentation using K-means clustering algorithm and RFM model. Deu Muhendislik Fakultesi Fen ve Muhendislik. https://doi.org/10.21205/deufmd.2023257418 Azzaria, C., Daniati, E., & Ristyawan, A. (2025). Peningkatan akurasi deteksi liver disease melalui hyperparameter tuning pada algoritma random forest. Indonesian Journal of Computer Science Research, 4(2), 139–147. Barrera, F., Segura, M., & Maroto, C. (2023). Multicriteria sorting method based on global and local search for supplier segmentation. International Transactions in Operational Research, 31, 3108–3134. https://doi.org/10.1111/itor.13288 Daniati, E., & Utama, H. (2019). Clustering K-Means for criteria weighting with improvement result of alternative decisions using SAW and TOPSIS. In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 73–78). IEEE. https://doi.org/10.1109/ICITISEE48480.2019.9003858 Dewata, E., Sari, Y., & Jauhari, H. (2020). Penyusunan laporan keuangan terkomputerisasi berdasarkan SAK EMKM pada UMKM konveksi. Intervensi Komunitas, 2(1), 11–16. https://doi.org/10.32546/ik.v2i1.676 Fahrudin, N. F., & Rindiyani, R. (2024). Comparison of K-medoids and K-means algorithms in segmenting customers based on RFM criteria. E3S Web of Conferences. https://doi.org/10.1051/e3sconf/202448402008 Farisi, I. M., & Supatmi, S. (2024). Implementation of the K-means clustering technique using RFM attributes to enhance donor retention at Masjid Nusantara. In 2024 International Conference on Informatics Engineering, Science & Technology (INCITEST) (pp. 1–6). https://doi.org/10.1109/INCITEST64888.2024.11121472 Hadad, Y., & Keren, B. (2022). A decision-making support system module for customer segmentation and ranking. Expert Systems, 40. https://doi.org/10.1111/exsy.13169 Haris Munandar, M. (2024). Application of data mining in selecting superior products using the K-means and K-medoids algorithm methods. JURTEKSI (Jurnal Teknologi dan Sistem Informasi). https://doi.org/10.33330/jurteksi.v10i4.3196 Harmain, A., Paiman, P., Kurniawan, H., Kusrini, K., & Maulina, D. (2022). Normalisasi data untuk efisiensi K-Means pada pengelompokan wilayah berpotensi kebakaran hutan dan lahan berdasarkan sebaran titik panas. Teknik Teknologi Informasi dan Multimedia, 2(2), 83–89. https://doi.org/10.46764/teknimedia.v2i2.49 He, Y., Xu, Z., & Liu, N. (2022). Research on K-medoids algorithm with probabilistic-based expressions and its applications. Applied Intelligence, 52, 12016–12033. https://doi.org/10.1007/s10489-021-02937-8 Henderi, H., Fitriana, L., Iskandar, I., Astuti, R., Arifandy, M. I., Hayadi, B., Mesran, M., Chin, J., & Kurniawan, A. (2024). Optimization of Davies-Bouldin index with K-medoids algorithm. AIP Conference Proceedings. https://doi.org/10.1063/5.0225220 Hidayati, R., Zubair, A., Pratama, A. H., & Indana, L. (2021). Analisis silhouette coefficient pada 6 perhitungan jarak K-Means clustering. Techno.Com, 20(2), 186–197. https://doi.org/10.33633/tc.v20i2.4556 Huang, Y., Zhang, M., & He, Y. (2020). Research on improved RFM customer segmentation model based on K-means algorithm. In 2020 5th International Conference on Computational Intelligence and Applications (ICCIA) (pp. 24–27). https://doi.org/10.1109/ICCIA49625.2020.00012 Hung, P. D., & Dat, D. Q. (2020). Customer behavior clustering based on balance history using dynamic time warping distance. International Journal of Machine Learning and Computing. https://doi.org/10.18178/ijmlc.2020.10.1.903 Indonesia, R. (2008). Undang-Undang Republik Indonesia Nomor 20 Tahun 2008 tentang Usaha Mikro, Kecil, dan Menengah. https://peraturan.bpk.go.id/Details/39653/uu-no-20-tahun-2008 Janardhanan, S., & Muthalagu, R. (2020). Market segmentation for profit maximization using machine learning algorithms. Journal of Physics: Conference Series, 1706. https://doi.org/10.1088/1742-6596/1706/1/012160 Jordy, M., Triayudi, A., & Sholihati, I. D. (2023). Analisis segmentasi recency dan customer value pada AVANA Indonesia dengan algoritma K-means dan model RFM. Journal of Information System Research (JOSH). https://doi.org/10.47065/josh.v4i2.2950 Kurnia, O. D., et al. (2024). Analisis perbandingan algoritma Naïve Bayes dengan K-Nearest Neighbor (KNN) pada dataset mobile price classification. Prosiding Seminar Nasional Inovasi Teknologi (SEMNAS INOTEK), 8, 2549–7952. Ma, M. (2024). The application of K-means algorithm-based data mining in optimizing marketing strategies of tobacco companies. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2024.0151186 Maniyara, K., Shah, K., Gaidhane, V. H., & Wanjari, R. (2024). An effective customer segmentation using RFM ranking techniques. In 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) (pp. 592–597). https://doi.org/10.1109/MoSICom63082.2024.10882067 Maulana, A., Ristyawan, A., Ndun, A. R., & Ristyawan, A. (2025). Prediksi volume sampah perkotaan berbasis data spasial menggunakan random forest di DKI Jakarta. Prosiding SEMNAS INOTEK, 9, 1667–1672. Hidayat, R., & Aminulhaq, F. (2025). A Comparative Analysis of Decision Tree, Logistic Regression, and Support Vector Machine Algorithms in Sentiment Analysis of Threads App Reviews. Intechno Journal: Information Technology Journal, 7(2), 45-55. Hidayat, R., & Ratnaningsih, D. J. (2025). Analisis Sentimen Program Mbg Menggunakan Algoritma Random Forest Dan Naive Bayes. Journal of Computing and Informatics Research, 5(1), 395-400. Hidayat, R. (2026). The Effect of Ability and Career Development on Employee Performance: Evidence from the Indonesian Automotive Industry. Jurnal Prima Manajemen, 1(3), 535-547. Mirantika, N., & Rijanto, E. (2024). Implementasi metode clustering partisi dalam menentukan segmentasi pelanggan. Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi. https://doi.org/10.34010/jtk3ti.v10i1.11320 Mirantika, N., & Rijanto, E. (n.d.). Comparative analysis of K-means and K-medoids algorithms in determining customer segmentation using RFM model. Retrieved from https://www.semanticscholar.org/paper/2053b7b55a15bd710794443298214c8305d29458 Nasution, M. Z., & Hasibuan, M. S. (2020). Pendekatan initial centroid search untuk meningkatkan efisiensi iterasi klustering K-Means. Techno.Com, 19(4), 341–352. https://doi.org/10.33633/tc.v19i4.3875 Niu, J. (2021). Intelligent evaluation model of e-commerce transaction volume based on the combination of K-means and SOM algorithms. International Journal of Information and Communication Technology, 18, 189–206. https://doi.org/10.1504/ijict.2021.10034321 Nugroho, B. I., Rafhina, A., Ananda, P. S., & Gunawan, G. (2024). Customer segmentation in sales transaction data using K-means clustering algorithm. Journal of Intelligent Decision Support System (IDSS). https://doi.org/10.35335/idss.v7i2.236 Nursahid, W., Nugroho, B. I., & Syefudin, S. (2025). Optimalisasi preprocessing data menggunakan pendekatan CRISP-DM untuk meningkatkan kualitas klasifikasi penyakit jantung. Jurnal Artificial Intelligence dan Digital Business, 4(3), 3621–3626. https://doi.org/10.31004/riggs.v4i3.2514 Putri, A. Z., Afdal, M., Monalisa, S., & Permana, I. (2023). Penerapan algoritma fuzzy C-means pada segmentasi pelanggan B2B dengan model LRFM. Jurnal Media Informatika Budidarma. https://doi.org/10.30865/mib.v7i3.6150 Ramadani, S., Ramadhani, D. A., Ikrom, M., & Harahap, L. M. (2025). Peran strategis UMKM dalam mendorong pertumbuhan ekonomi berkelanjutan di Indonesia. Jurnal Ekonomi, Bisnis dan Manajemen, 4(1), 158–166. https://doi.org/10.58192/ebismen.v4i1.3183 Salsabila, N., Aulisari, K., & Zahro, H. Z. (2025). Penerapan algoritma K-Means untuk klasterisasi produktivitas tanaman jahe. Infotek: Jurnal Informatika dan Teknologi, 8(1), 228–238. https://doi.org/10.29408/jit.v8i1.28195 Serwah, A. M. A., Khaw, K., Yeng, C. S. P., & Alnoor, A. (2023). Customer analytics for online retailers using weighted K-means and RFM analysis. Data Analytics and Applied Mathematics (DAAM). https://doi.org/10.15282/daam.v4i1.9171 Siagian, R., Sirait, P. S. P., & Halima, A. (2021). E-commerce customer segmentation using K-Means algorithm and length, recency, frequency, monetary model. Jurnal Informatics and Telecommunication Engineering, 5(1), 21–30. https://doi.org/10.31289/jite.v5i1.5182 Syamfithriani, T. S., Mirantika, N., & Trisudarmo, R. (2023). Perbandingan algoritma K-means dan K-medoids untuk pemetaan daerah penanganan diare pada balita di Kabupaten Kuningan. Jurnal Sistem Informasi Bisnis. https://doi.org/10.21456/vol12iss2pp132-139 Wani, A., Priyanka, M., & Prasath, R. (2023). Unleashing customer insights: Segmentation through machine learning. In 2023 World Conference on Communication & Computing (WCONF) (pp. 1–5). https://doi.org/10.1109/WCONF58270.2023.10235136 Wei, X., Zhang, Z., Huang, H., & Zhou, Y. (2024). An overview on deep clustering. Neurocomputing, 590, Article 127761. https://doi.org/10.1016/j.neucom.2024.127761 Windjaya, P. A., & Siregar, B. (2024). Analisis segmentasi pelanggan toko online marketplace berdasarkan RFMTS menggunakan algoritma K-medoids clustering. Mutiara: Multidisciplinary Scientific Journal. https://doi.org/10.57185/mutiara.v2i2.144 Yu, Y. (2022). Real-time sales forecasting algorithm of electronic commerce products in a smart city based on weighted naive Bayes. Journal of Testing and Evaluation. https://doi.org/10.1520/jte20220074 Yunita, I., Ali, P. R., Kartawidjaja, M. A., & Sukwadi, R. (2025). Segmentasi pelanggan menggunakan K-Means clustering: Menganalisis metrik RFM untuk strategi pemasaran. Jurnal Media Teknik dan Sistem Industri, 9(1), 58. https://doi.org/10.35194/jmtsi.v9i1.4452