Cosmetic Product Segmentation Analysis Using K-Means Clustering at PT Mandom Bekasi
DOI:
https://doi.org/10.69916/jkbti.v5i3.578Keywords:
K-Means Clustering, Knowledge Discovery Databases, Product Segmentation, Sales Pattern, Inventory TurnoverAbstract
PT Mandom Indonesia Tbk manages a wide range of cosmetic products with varying sales levels and inventory turnover rates, creating challenges in inventory management and marketing strategy formulation. This study aims to segment cosmetic products based on sales patterns and inventory turnover using the K-Means Clustering algorithm within a Knowledge Discovery in Databases (KDD) framework. The research stages include data selection, preprocessing, transformation, clustering, and evaluation. The dataset consists of 436 cosmetic products with attributes including sell in, sell out, stock, and expiration date, sourced from PT Mandom's internal sales report for the year 2025. Feature engineering produced two derived variables, the sell out to sell in ratio and the remaining days until expiration, which were normalized using Min-Max Scaling. The optimal number of clusters, determined using the Elbow Method and validated with the Silhouette Score, was three. The K-Means algorithm successfully grouped the products into three segments: Fast Moving (75 products, 17.2%), Medium Moving (299 products, 68.6%), and Slow Moving (62 products, 14.2%). The Fast Moving cluster exhibited the highest sell in, sell out, and sell-through ratio values, while the Slow Moving cluster showed the lowest ratio, indicating a higher risk of stock accumulation. These segmentation results can serve as a data-driven basis for inventory management, distribution planning, and marketing strategy decisions at PT Mandom.
Downloads
References
K. P. R. Indonesia, “Kemenperin Gadang Potensi Industri Kosmetik Semakin Gemilang,” Direktorat Jenderal Industri Kecil, Menengah dan Aneka, Kementerian Perindustrian Republik Indonesia.
Databoks, “Ini Produk Kecantikan yang Banyak Diburu Konsumen E-Commerce Indonesia,” Katadata.co.id.
M. A. Barata, I. S. Ayuni, A. Y. Kartini, and Z. Alawi, “Algoritma K-Means dalam Clustering Produk Skincare untuk Menentukan Strategi Pemasaran,” J. Inform. Polinema, vol. 10, no. 3, pp. 421–428, 2024.
M. Noval, W. Windarsyah, and F. D. Marleny, “Implementasi Algoritma K-Means Untuk Analisis Pola Penjualan Pada Toko Monisa,” J. Media Inform., 2025.
R. P. Rahmawati and W. Prihartono, “Optimasi Stok dengan Clustering Data Transaksi Penjualan Menggunakan Algoritma K-Means,” J. Inform. dan Tek. Elektro Terap., 2024.
D. Aldo, “Data Mining Sales of Skin Care Products Using the K-Means Method,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 1, pp. 295–304, 2023.
M. Miranda and S. Sriani, “Implementation of K-Means Clustering in Grouping Sales Data at Zura Mart,” J. Appl. Informatics Comput., 2025.
P. J. Rousseeuw, “Silhouettes: A Graphical Aid to the Interpretation of Cluster Analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 2021.
D. Frias and J. Hidalgo, “A High Accuracy Image Hashing and Random Forest Classifier for Crack Detection in Concrete Surface Images,” Arxiv, pp. 1–13, 2021, [Online]. Available: http://arxiv.org/abs/2106.05755
Z. Luo, Hotel Cancellation Rate Prediction: A Machine Learning Based Prediction Model, no. Iciaai. Atlantis Press International BV, 2025. doi: 10.2991/978-94-6463-823-3_31.
A. Herrera, Á. Arroyo, A. Jiménez, and Á. Herrero, “Forecasting hotel cancellations through machine learning,” Expert Syst., vol. 41, no. 9, pp. 1–19, 2024, doi: 10.1111/exsy.13608.
M. Słoński, “A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks,” Comput. Assist. Methods Eng. Sci., vol. 26, no. 2, pp. 105–112, 2019, doi: 10.24423/CAMES.267.
N. Vandeput, Inventory Analytics: Data Science for Inventory Optimization. De Gruyter, 2020.
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. Sebastopol, CA, USA: O’Reilly Media, 2022.
B. Shen, “E-commerce Customer Segmentation via Unsupervised Machine Learning,” in CONFCDS 2021, 2021.
A. G. Costa, D. A. G. De Sousa, J. L. Paes, J. P. B. Cunha, and M. V. M. De Oliveira, “CLASSIFICATION OF ROBUSTA COFFEE FRUITS AT DIFFERENT MATURATION STAGES USING COLORIMETRIC CHARACTERISTICS artificial vision Coffee growers who produce the robusta species ( Conilon ) have sought to increase productivity and drink quality by improving prod,” Eng. Agrícola, vol. 4430, no. 4, pp. 518–525, 2020, [Online]. Available: http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v40n4p518-525/2020%0Ahttp://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000400518&tlng=en
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (Ny)., vol. 622, pp. 178–210, 2023, doi: https://doi.org/10.1016/j.ins.2022.11.139.
A. Azis and S. Sutisna, “Application of Data Mining to Determine Product Stock Availability Using K-Means Clustering,” JIMIK, vol. 5, no. 3, pp. 3099–3106, 2024.
A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” J. Tekno Kompak, vol. 15, no. 2, p. 25, 2021, doi: 10.33365/jtk.v15i2.1162.
N. D. Rahayu, A. H. Anshor, and I. Afriantoro, “Penerapan Data Mining untuk Pemetaan Siswa Berprestasi menggunakan Metode Clustering K-Means,” JUKI J. Komput. dan Inform., vol. 6, no. 1, pp. 71–83, 2024, doi: 10.53842/juki.v6i1.474.
Downloads
Published
Scite Metrics
Altmetric
How to Cite
Issue
Section
License
Copyright (c) 2026 Mona Dewintha Agustine, Adi Muhajirin, Prio Kustanto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Muhammad Rofiq Ubaidillah, R Wisnu Prio Pamungkas, Prio Kustanto, Application of K-Means Clustering Algorithm in Edam Burger Sales Information System for Inventory Control Optimization , Jurnal Kecerdasan Buatan dan Teknologi Informasi: Vol. 5 No. 3 (2026): September 2026 In progress.
menuterbaru
