Perfume Product Segmentation on Marketplace Using Web Scraping and K-Means Algorithm
Article Sidebar
Main Article Content
Abstract
The rapid development of e-commerce in Indonesia, particularly Tokopedia, has significantly shifted consumer consumption patterns towards secondary products such as perfume. However, Micro, Small, and Medium Enterprises (MSMEs) often struggle to understand competitive market characteristics due to limited access to clear, usable analytical data. This study aims to address this issue by developing an automated data collection system and performing perfume product market segmentation using a data-driven approach. The methodology used in this study combines Web Scraping techniques and Machine Learning algorithms. Product data was automatically collected from Tokopedia using Node.js and Playwright scripts, followed by a data preprocessing stage that included price cleaning, sales volume conversion, and attribute normalization. Segmentation analysis was performed using the K-Means Clustering algorithm, with the optimal number of clusters determined using the Elbow Method. The results from 995 successfully collected product data points indicate that the perfume product market on Tokopedia is divided into three main segments (k=3). Cluster 0 is the dominant segment (98% of the population), representing the mass market with affordable prices and high sales volume. Cluster 1 was identified as anomalous data or outliers with extreme prices but no sales, while Cluster 2 represents a niche market (premium) with high prices and limited sales volume. The conclusion of this study suggests that combining web scraping with K-Means is effective for mapping product positions in the digital market. The output of this research is expected to assist MSMEs in formulating more targeted pricing and promotional strategies based on real data rather than mere assumptions.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work and grant Jurnal Ilmiah Teknologi Informasi Asia the right of first publication. The work is simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
While the editorial board endeavors to ensure accuracy, they accept no responsibility for the content of articles. Liability rests solely with the respective authors.
This is an open-access journal. All articles are immediately available to read and reuse upon publication under the CC BY 4.0 license. Users may share and adapt the material for any purpose, including commercial use, provided they comply with the license terms.
References
Aldi, M. A., & Fatah, Z. (2025). Implementasi K-means clustering dalam pengelompokan data kunjungan wisatawan asing di Indonesia. Jurnal Ilmiah Multidisiplin Ilmu, 2(1), 13–19. https://doi.org/10.69714/3hhfj353 DOI: https://doi.org/10.69714/3hhfj353
Alfi Rahmawati, Tasya Kamila Hamdani, & Wahyu Budi Priatna. (2025). Ecommerce Adoption and MSME Business Performance in Indonesia: Systematic Literature Review. Journal Scientific of Mandalika (JSM) E-ISSN 2745-5955 | P-ISSN 2809-0543, 6(9), 3512-3520. https://doi.org/10.36312/10.36312/vol6iss9pp3512-3520
Analisis performa algoritma K-means dan DBSCAN dalam segmentasi pelanggan dengan pendekatan model RFM. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 8(7). https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/13962
Chairunnita, C., Handayanto, A., & Dewanto, F. (2025). Penerapan Algoritma K-Means Clustering dalam Analisis Pengelompokan Produk Toko Oleh-Oleh Berdasarkan Data Penjualan. Journal of Information System Research (JOSH), 6(4), 1816-1825. https://doi.org/10.47065/josh.v6i4.7832 DOI: https://doi.org/10.47065/josh.v6i4.7832
Chrisinta, D., & Simarmata, J. E. (2024). Eksplorasi teknik web scraping pada data mining: Pendekatan pencarian data berbasis Python. Faktor Exacta, 17(1). http://dx.doi.org/10.30998/faktorexacta.v17i1.22393 DOI: https://doi.org/10.30998/faktorexacta.v17i1.22393
Dewi, R. K. (2025). The influence of e-commerce on consumer buying behavior in rural Indonesia. Aegaeum Journal, 13(2), 45–56.
Fatonah, S. ., Hidayat, A. ., & Mirnayani. (2024). Exploring the impact of consumer behavior and innovation orientation on business strategy and firm performance: Insights from MSMEs in Indonesia . International Journal of Management and Sustainability, 13(4), 818–831. https://doi.org/10.18488/11.v13i4.3900 DOI: https://doi.org/10.18488/11.v13i4.3900
Laila Ali Putri, Mazayah Tsaqofah, Dea Syahfira Hasibuan, Hasti Fadillah, Maria Ulfa, & Mhd.Furqan. (2025). Application of K-Means Clustering Algorithm for E-Commerce Data Analysis. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(3), 2364–2367. https://doi.org/10.59934/jaiea.v4i3.1170 DOI: https://doi.org/10.59934/jaiea.v4i3.1170
Legito, L., Wattimena, F. Y., Rofi’i, Y. U., & Munawir, M. (2023). E-commerce product recommendation system using case-based reasoning (CBR) and K-means clustering. International Journal Software Engineering and Computer Science, 3(2), 162–173. DOI: https://doi.org/10.35870/ijsecs.v3i2.1527
Mubarok, R., Syahputra, A. A., Permana, A. T., Sholiah, L., & Tarwoto. (2025). Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(2), 703-712. https://doi.org/10.35870/jtik.v9i2.3438 DOI: https://doi.org/10.35870/jtik.v9i2.3438
Nugroho, A. Y. (2025). Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis: Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis. JURNAL ILMIAH MATEMATIKA DAN TERAPAN, 22(1), 21 - 30. https://doi.org/10.22487/2540766X.2025.v22.i1.17884
Nurhayati, I. P., Helmayana, Tiani, A., Maruenci, K., Harahap, Y. S., & Fansyuri, M. (2025). Utilization of Data Mining for Credit Card Customer Segmentation Using the K-Means Method. Journal of Information Technology and Informatics Engineering, 1(1), 62-66. https://journal.jci.co.id/jitie/article/view/162
P. Rajapandian, A. Karunamurthy, V. Vasanth, & M. Meganathan. (2025). E-Commerce Customer Segmentation: A Clustering Approach in A Web-Based Platform. Journal of Engineering Technology and Applied Physics, 7(1), 71–79. https://doi.org/10.33093/jetap.2025.7.1.12 DOI: https://doi.org/10.33093/jetap.2025.7.1.12
Pranata, F. M., Wijoyo, S. H., & Setiawan, N. Y. (2024).
Sabrina, A. E., & Yasin, M. (2025). Clustering data konsumen e-commerce menggunakan algoritma K-Means dan dataset Kaggle. Journal of Computer Science and Informatics Engineering, 4(2), 96–109. https://doi.org/10.55537/cosie.v4i2.1123 DOI: https://doi.org/10.55537/cosie.v4i2.1123
Sakinah, A., & Awaliyah, D. S. (2025). Optimization of e-commerce consumer segmentation based on K-means clustering and machine learning. Journal of Mathematics, Computations and Statistics, 8(2). https://doi.org/10.35580/jmathcos.v8i2.9548 DOI: https://doi.org/10.35580/jmathcos.v8i2.9548
Wardhana, A., Pradana, M., Shabira, H., Buana, D. M. A., Nugraha, D. W., & Sandi, K. (2021). The influence of consumer behavior on purchasing decision process of Tokopedia e-commerce customers in Indonesia. In Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management (pp.—). IEOM Society International. https://doi.org/10.46254/AN11.2021099 DOI: https://doi.org/10.46254/AN11.20210998
Wulandari, A., Purnomo, & Rustandy, A. . (2024). The Role of Market Segmentation and Competitive Positioning in Increasing Brand Awareness in the Indonesian Retail Industry. Ilomata International Journal of Management, 5(4), 1390–1412. https://doi.org/10.61194/ijjm.v5i4.1293 DOI: https://doi.org/10.61194/ijjm.v5i4.1293
Yu, L. (2024). The application of K-means clustering algorithm in the evaluation of e-commerce websites. Journal of Electrical Systems, 20(6s). https://doi.org/10.52783/jes.2738 DOI: https://doi.org/10.52783/jes.2738
Zhang, W., & Wu, Z. (2024). E-commerce recommender system based on improved K-means commodity information management model. Heliyon, 10(9), e29045. https://doi.org/10.1016/j.heliyon.2024.e29045 DOI: https://doi.org/10.1016/j.heliyon.2024.e29045
Zhao, Y., et al. (2024). Research on e-commerce user segmentation and customized marketing strategy based on cluster analysis. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-2668 DOI: https://doi.org/10.2478/amns-2024-2668