Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi
DOI:
https://doi.org/10.52436/1.jutif.2025.6.5.5140Keywords:
Customer Behavior, Customer Segmentation, Data Mining, K-Means Clustering, Personalized MarketingAbstract
In today's competitive business landscape, a deep understanding of customer behavior and preferences is crucial for strategic success. Customer segmentation emerges as a vital approach to identify distinct customer subgroups, enabling personalized and efficient marketing strategies. However, many companies still struggle to achieve this understanding due to suboptimal data utilization and inaccurate manual grouping methods. To address these challenges, this research proposes and implements a data mining approach using the K-Means Clustering algorithm for automated and measurable customer segmentation. Leveraging the "Customer Personality Analysis" dataset from Kaggle, this study aims to uncover hidden patterns in customer demographics (age, income, marital status, number of children) and purchasing behavior (number and frequency of transactions). A comprehensive data pre-processing pipeline, including handling missing values, feature engineering, irrelevant column removal, categorical transformation, and numerical scaling, ensures data quality and readiness. Using the Elbow Method, four optimal clusters were identified: "Balanced Spenders with Teenagers" (Cluster 0), "Budget-Conscious Families" (Cluster 1), "High-Value Engaged Buyers" (Cluster 2), and "Active Mature Buyers" (Cluster 3). Visualization using Principal Component Analysis (PCA) further confirms significant characteristic differences between these segments. Cluster 2, being the most valuable and responsive segment, requires premium marketing strategies, while Cluster 1, the largest segment, demands a value-oriented approach. The results of this segmentation provide deep strategic insights, enabling companies to allocate marketing resources more efficiently, craft more relevant messages, and ultimately enhance customer satisfaction and business profitability. These findings demonstrate the potential of unsupervised learning in enhancing data-driven customer profiling systems in marketing and business informatics.
Downloads
References
D. Müllensiefen, C. Hennig, and H. Howells, “Using clustering of rankings to explain brand preferences with personality and socio-demographic variables,” Apr. 2017, [Online]. Available: http://arxiv.org/abs/1704.00959
J. Salminen, M. Mustak, M. Sufyan, and B. J. Jansen, “How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation,” Journal of Marketing Analytics, vol. 11, no. 4, pp. 677–692, Dec. 2023, doi: 10.1057/s41270-023-00235-5.
J. M. John, O. Shobayo, and B. Ogunleye, “An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market,” Analytics, vol. 2, no. 4, pp. 809–823, Dec. 2023, doi: 10.3390/analytics2040042.
L. A. S. Kristiyowati, F. M. Hana, and W. C. Wahyudin, “Penerapan Algoritma Naive Bayes pada Sistem Chatbot Persewaan Kos,” Sainteks, vol. 22, no. 1, pp. 53–62, Apr. 2025, doi: 10.30595/sainteks.v22i1.25962.
D. Chen, S. L. Sain, and K. Guo, “Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining,” Journal of Database Marketing and Customer Strategy Management, vol. 19, no. 3, pp. 197–208, Sep. 2012, doi: 10.1057/dbm.2012.17.
W. G. Mangold and D. J. Faulds, “Social media: The new hybrid element of the promotion mix,” Bus Horiz, vol. 52, no. 4, pp. 357–365, Jul. 2009, doi: 10.1016/j.bushor.2009.03.002.
M. S. E. Kasem, M. Hamada, and I. Taj-Eddin, “Customer profiling, segmentation, and sales prediction using AI in direct marketing,” Neural Comput Appl, vol. 36, no. 9, pp. 4995–5005, Mar. 2024, doi: 10.1007/s00521-023-09339-6.
M. Alves Gomes and T. Meisen, “A review on customer segmentation methods for personalized customer targeting in e-commerce use cases,” Information Systems and e-Business Management, vol. 21, no. 3, pp. 527–570, Sep. 2023, doi: 10.1007/s10257-023-00640-4.
D. Paundra Gevano, “Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi: Studi Kasus pada Dataset Customer Personality Analysis H1D023075 H1D023089,” 2025.
S. Prisilia, N. R. Fitrya, A. Febriana, and N. N. A. Putri, “Perbandingan Kinerja Keuangan Perusahaan Menggunakan Analisis Rasio,” GEMILANG: Jurnal Manajemen dan Akuntansi, vol. 3, Jan. 2023.
R. S. Fams and A. W. Lubis, “Konsep Penguat Usaha Ekonomi Rakyat Dengan Menggunakan Cloud Computing Literature Study,” Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah, vol. 6, no. 2, pp. 685–697, Feb. 2023, doi: 10.47467/alkharaj.v6i2.3227.
F. Sutomo et al., “OPTIMIZATION OF THE K-NEAREST NEIGHBORS ALGORITHM USING THE ELBOW METHOD ON STROKE PREDICTION,” Jurnal Teknik Informatika (JUTIF), vol. 4, no. 1, 2023, doi: 10.20884/1.jutif.2023.4.1.839.
G. Wang, “Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering,” PLoS One, vol. 20, no. 2 February, Feb. 2025, doi: 10.1371/journal.pone.0318519.
K. Tabianan, S. Velu, and V. Ravi, “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data,” Sustainability (Switzerland), vol. 14, no. 12, Jun. 2022, doi: 10.3390/su14127243.
B. Benaissa, M. Kobayashi, and H. Takenouchi, “Enhancing Consumer Agent Modeling Through Openness-Based Consumer Traits and Inverse Clustering,” Mach Learn Knowl Extr, vol. 7, no. 1, Mar. 2025, doi: 10.3390/make7010009.
S. N. Lathifah and Z. F. Azzahra, “AI-Driven Customers Segmentation Using K-Means Clustering,” G-Tech: Jurnal Teknologi Terapan, vol. 9, no. 1, pp. 320–329, Jan. 2025, doi: 10.70609/gtech.v9i1.6202.
N. Jain and V. Ahuja, “Segmenting online consumers using K-means cluster analysis,” 2014.
M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Aug. 01, 2020, MDPI AG. doi: 10.3390/electronics9081295.
A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit Lett, vol. 31, no. 8, pp. 651–666, Jun. 2010, doi: 10.1016/j.patrec.2009.09.011.
L. Afuan, N. Hidayat, H. Hamdani, H. Ismanto, B. C. Purnama, and D. I. Ramdhani, “Optimizing BERT Models with Fine-Tuning for Indonesian Twitter Sentiment Analysis,” J Wirel Mob Netw Ubiquitous Comput Dependable Appl, vol. 16, no. 2, pp. 248–267, Jun. 2025, doi: 10.58346/JOWUA.2025.I2.016.
N. Miftahul Janna and D. Pembimbing, “KONSEP UJI VALIDITAS DAN RELIABILITAS DENGAN MENGGUNAKAN SPSS.”
S. N. Agni, M. I. Djomiy, R. Fernando, and C. Apriono, “Evaluasi Penerapan Smart Mobility di Jakarta (Evaluation of Smart Mobility Implementation in Jakarta),” 2021.
M. Shutaywi and N. N. Kachouie, “Silhouette analysis for performance evaluation in machine learning with applications to clustering,” Entropy, vol. 23, no. 6, Jun. 2021, doi: 10.3390/e23060759.
F. P. Rachman, H. Santoso, and A. Djajadi, “Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation.” [Online]. Available: www.ijacsa.thesai.org
H. M. Tenkam, J. C. Mba, and S. M. Mwambi, “Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach,” Applied Sciences (Switzerland), vol. 12, no. 13, Jul. 2022, doi: 10.3390/app12136408.
E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, “Comprehensive Review of K-Means Clustering Algorithms,” International Journal of Advances in Scientific Research and Engineering, vol. 07, no. 08, pp. 64–69, 2021, doi: 10.31695/ijasre.2021.34050.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Iqbal, Nurul Hidayat, Daiva Paundra Gevano, Andhika Putra Restu Ilahi
This work is licensed under a Creative Commons Attribution 4.0 International License.
