Customer Segmentation Analysis Using the K-Means Method on Order Data from Aisyah Catering MSME
DOI:
https://doi.org/10.35870/ijsecs.v6i3.8324Keywords:
Customer Segmentation, K-Means, Clustering, Catering MSME, Data MiningAbstract
Aisyah Catering is a Micro, Small, and Medium Enterprise (MSME) in the catering service sector that has conventionally recorded order transactions manually. Consequently, the business owner faces difficulties in comprehensively understanding customer order patterns to formulate targeted marketing strategies. This study aims to segment Aisyah Catering's customers based on order volume using the K-Means algorithm. The research methodology follows the CRISP-DM framework, initiating with the collection of 197 transaction records digitized from physical receipts, followed by data preprocessing using Altair AI Studio to yield 187 valid records. In the modeling phase, K-Means was applied to the order quantity (number of portions) attribute as the sole quantitative variable in the clustering process. The determination of the optimal number of clusters was conducted through the evaluation of values from the Elbow Method and the Davies-Bouldin Index (DBI). The results demonstrate that k=3k=3 was selected as the optimal number of clusters with a DBI value of 0.449. The three resulting segments are: the small-scale segment (49.2% of customers, averaging 360.11 portions), the medium-scale segment (33.7% of customers, averaging 717.78 portions), and the large-scale segment (17.1% of customers, averaging 1,045.31 portions). A notable finding emerges regarding volume contribution: the large-scale segment, despite having the fewest members, accounts for 29.9% of the total order volume—nearly equal to the contribution of the small-scale segment which has nearly three times as many members. Thus, the results of this segmentation serve as a foundational basis for Aisyah Catering in designing marketing strategies, raw material procurement planning, and tailored service delivery aligned with the characteristics of each customer segment.
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Al-Fahmi, B. M., Rahmawati, E., & Sagirani, T. (2023). Penerapan K-Means Clustering pada Pariwisata Kabupaten Bojonegoro untuk Mendukung Keputusan Strategi Pemasaran. Jurnal Nasional Teknologi dan Sistem Informasi, 9(2), 141-149. https://doi.org/10.25077/teknosi.v9i2.2023.141-149
Andrian, Y., & Susilawati, S. (2025). Penerapan Algoritma K-Means dalam Segmentasi Pelanggan untuk Meningkatkan Strategi Pemasaran di E-Commerce. INCODING: Journal of Informatics and Computer Science Engineering, 5(1), 95-108. https://doi.org/10.34007/incoding.v5i1.825
Ardi, R. B., Nastiti, F. E., & Sumarlinda, S. (2023). Algoritma K-Means Clustering untuk Segmentasi Pelanggan (Studi Kasus: Fashion Viral Solo). INFOTECH Journal, 9(1), 124-131. https://doi.org/10.31949/infotech.v9i1.5214
Ariska, S., Puspita, D., & Anggraini, I. (2024). Comparison of K-Means and K-Medoids Algorithm for Clustering Data UMKM in Pagar Alam City. Jurnal Sisfokom (Sistem Informasi dan Komputer), 13(2), 193-199. https://doi.org/10.32736/sisfokom.v13i2.2090
Ashari, I. F., Banjarnahor, R., Farida, D. R., Aisyah, S. P., Dewi, A. P., & Humaya, N. (2022). Application of data mining with the K-means clustering method and Davies Bouldin index for grouping IMDB movies. Journal of Applied Informatics and Computing, 6(1), 07-15. https://doi.org/10.30871/jaic.v6i1.3485
As Shoib, A., Dewi, A. P., Prista, D., Wahyu, F. D., & Sudarmiatin. (2024). Analisis Strategi Pemasaran STP (Segmenting, Targeting, Positioning) pada UMKM Pengrajin Keramik. Jurnal Bintang Manajemen, 2(4), 91-98. https://doi.org/10.55606/jubima.v2i4.3419
Syam, F. A. (2017). Implementasi metode klastering k-means untuk mengelompokan hasil evaluasi mahasiswa. Jurnal Ilmu Komputer dan Bisnis, 8(1), 1857-1864.
Danuwinata, E. R., & Tjen, J. (2023). Segmentasi Pelanggan Menggunakan Metode K-Means dalam Data Mining untuk Strategi Promosi UMKM Lily Cakes Pontianak. INFORMASI: Jurnal Informatika dan Sistem Informasi. https://doi.org/10.37424/informasi.v17i2.438
Dewobroto, W. S., & Shania, W. (2023). Faktor Pendorong dan Dampak dari Peralihan Usaha Mikro ke Pemasaran Digital sebagai Solusi Meningkatkan Penjualan Pasca Pandemi Covid-19. Jurnal Penelitian Pendidikan Indonesia, 9(1), 172-180. https://doi.org/10.29210/020221823
Djun, S. F., Gunadi, I. G. A., & Sariyasa, S. (2024). Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM. JTIM: Jurnal Teknologi Informasi dan Multimedia, 5(4), 354-364. https://doi.org/10.35746/jtim.v5i4.434
Haryandini, N. A. P., & Prihartono, W. (2025). Analisis Segmentasi Pelanggan untuk Menyusun Strategi Promosi Menggunakan K-Means: Studi Kasus di PT XYZ. Jurnal Informatika dan Teknik Elektro Terapan, 13(1). https://doi.org/10.23960/jitet.v13i1.5697
Kementerian Koperasi dan Usaha Kecil dan Menengah Republik Indonesia. (2024). Kontribusi UMKM terhadap PDB Indonesia periode 2020-2024. Databoks Katadata.
Kristanti, B. T., Junaidi, A., & Mandyartha, E. P. (2024). Implementasi K-Means Clustering dalam Segmentasi Pelanggan Berdasarkan Usia, Pendapatan, dan Model RFM (Studi Kasus: Lantikya Store Jombang). Jurnal Informatika dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4677
Manggu, B., & Beni, S. (2021). Analisis Penerapan Segmentasi, Targeting, Positioning (STP) dan Promosi Pemasaran sebagai Solusi Meningkatkan Perkembangan UMKM Kota Bengkayang. Sebatik, 25(1), 27-34. https://doi.org/10.46984/sebatik.v25i1.1146
Marcelina, D., Kurnia, A., & Terttiaavini. (2023). Analisis Klaster Kinerja Usaha Kecil dan Menengah Menggunakan Algoritma K-Means Clustering. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 293-301. https://doi.org/10.57152/malcom.v3i2.952
Nurlathifa, A., & Sucahyati, D. (2025). Segmentasi Pelanggan menggunakan Metode K-Means Clustering Berdasarkan Model RFM pada Bisnis Food and Beverage (Studi Kasus: Coffee Shop X). Jurnal Nasional Teknologi dan Sistem Informasi, 11(3), 218-225. https://doi.org/10.25077/teknosi.v11i3.2025.218-225
Rohman, N., & Wibowo, A. (2020). Perbandingan Metode K-Medoids dan Metode K-Means dalam Analisis Segmentasi Pelanggan Mall. https://doi.org/10.31598/sintechjournal.v7i1.1507
Setyaningtyas, S., Nugroho, B. I., & Arif, Z. (2022). Tinjauan Pustaka Sistematis: Penerapan Data Mining Teknik Clustering Algoritma K-Means. Jurnal Teknoif Teknik Informatika Institut Teknologi Padang, 10(2), 52-61. https://doi.org/10.21063/jtif.2022.V10.2.52-61
Widiyanto, A. T., & Witanti, A. (2021). Segmentasi Pelanggan Berdasarkan Analisis RFM Menggunakan Algoritma K-Means sebagai Dasar Strategi Pemasaran (Studi Kasus PT Coversuper Indonesia Global). KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 1(1), 204-215. https://doi.org/10.24002/konstelasi.v1i1.4293
Zebua, O. K., dkk. (2026). Eksplorasi Peran Strategi Segmentasi, Targeting, dan Positioning (STP) dalam Membentuk Perilaku Pembelian Konsumen pada UMKM Kuliner. https://doi.org/10.37481/sjr.v9i1.1345
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