Penerapan Machine Learning dalam Pengelompokan Pelanggan Menggunakan K-Means Clustering untuk Meningkatkan Strategi Pemasaran PT Maspion
Keywords:
K-Means Clustering, customer segmentation, machine learning, marketing strategy, PT Maspion
Abstract
This study aims to enhance PT Maspion's marketing strategy effectiveness by clustering customers using the K-Means Clustering algorithm. By leveraging customer transaction data, this research successfully grouped customers into four clusters based on their purchasing patterns. Each cluster was analyzed to identify key characteristics and provide relevant marketing strategy recommendations, such as volume-based discounts, personalized services, and loyalty programs. The results indicate that implementing K-Means Clustering helps PT Maspion better understand customer needs, increase loyalty, and optimize company revenue. This study offers practical contributions to the company and enriches academic literature on the application of machine learning in marketing.
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References
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Tabianan, Kayalvily, Shubashini Velu, and Vinayakumar Ravi. 2022. “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data.” Sustainability (Switzerland) 14 (12). https://doi.org/10.3390/su14127243.
Vanneschi, Leonardo, and Sara Silva. 2023. “Artificial Neural Networks.” In Natural Computing Series. https://doi.org/10.1007/978-3-031-17922-8_7.
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Wijoyo A, Saputra A, Ristanti S, Sya’ban S, Amalia M, and Febriansyah R. 2024. “Pembelajaran Machine Learning.” OKTAL (Jurnal Ilmu Komputer Dan Science) 3 (2): 375–80. https://journal.mediapublikasi.id/index.php/oktal/article/view/2305.
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Alves Gomes, Miguel, and Tobias Meisen. 2023. “A Review on Customer Segmentation Methods for Personalized Customer Targeting in E-Commerce Use Cases.” Information Systems and E-Business Management 21 (3). https://doi.org/10.1007/s10257-023-00640-4.
Alvianatinova, Via, Irfan Ali, Nining Rahaningsih, and Agus Bahtiar. 2024. “Penerapan Algoritma K-Means Clustering Dalam Pengelompokan Data Penjualan Supermarket Berdasarkan Cabang (Branch).” JATI (Jurnal Mahasiswa Teknik Informatika) 8 (2): 1529–35. https://doi.org/10.36040/jati.v8i2.8993.
Atira, Afifa, and Betha Nurina Sari. 2023. “Penerapan Silhouette Coefficient, Elbow Method Dan Gap Statistics Untuk Penentuan Cluster Optimum Dalam Pengelompokkan Provinsi Di Indonesia Berdasarkan Indeks Kebahagiaan.” Jurnal Ilmiah Wahana Pendidikan 9 (17): 76–86. https://doi.org/10.5281/zenodo.8282638.
Awalina, Eriskiannisa Febrianty Luchia, and Woro Isti Rahayu. 2023. “Optimalisasi Strategi Pemasaran Dengan Segmentasi Pelanggan Menggunakan Penerapan K-Means Clustering Pada Transaksi Online Retail.” Jurnal Teknologi Dan Informasi 13 (2): 122–37. https://doi.org/10.34010/jati.v13i2.10090.
Butsianto, Sufajar, and Nindi Tya Mayangwulan. 2020. “Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering.” Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI) 3 (3): 187–201. https://doi.org/10.32672/jnkti.v3i3.2428.
D, Kaja Mohaideen, Baskaran S, and Mohammed Ibrahim D. 2021. “Application of K-Means Clustering in Spoken English Training Process.” Malaya Journal of Matematik 9 (1): 163–68. https://doi.org/10.26637/mjm0901/0027.
Gul, Marina, and M. Abdul Rehman. 2023. “Big Data: An Optimized Approach for Cluster Initialization.” Journal of Big Data 10 (1): 1–19. https://doi.org/10.1186/s40537-023-00798-1.
Hadi, Fakhri, Dini Octari Rahmadia, Ferdian Hadi Nugraha, Nada Putri Bulan, Mustakin, and Siti Monalisa. 2017. “Penerapan K-Means Clustering Berdasarkan RFM Mofek Sebagai Pemetaan Dan Pendukung Strategi Pengelolaan Pelanggan (Studi Kasus: PT. Herbal Penawar Alwahidah Indonesia Pekanbaru).” SITEKIN: Jurnal Sains, Teknologi Dan Industri 15 (1): 69–76. http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/4575.
Ikotun, Abiodun M., Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, and Jia Heming. 2023. “K-Means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data.” Information Sciences 622. https://doi.org/10.1016/j.ins.2022.11.139.
Junaedy, Nur Avia Aminia, and Nurul Asfiah. 2024. “Hubungan Antara AI, Machine Learning, Dan Implikasinya Terhadap Responsivitas Bisnis.” Jurnal Bisnis Inovatif Dan Digital 1 (3): 81–91. https://doi.org/10.61132/jubid.v1i3.189.
Kamila, Cahya. 2021. “Systematic Literature Review: Penggunaan Algoritma K-Means Untuk Clustering Di Indonesia Dalam Bidang Pendidikan.” Intech 2 (1): 19–24. https://doi.org/10.54895/intech.v2i1.866.
Kotler, Philip, Kevin Lane Keller, and Alexander Chernev. 2022. Marketing Management 16/E. Global Edition. Pearson Practice Hall.
Liu, Fei, and L. Billard. 2022. “Partition of Interval-Valued Observations Using Regression.” Journal of Classification 39 (1). https://doi.org/10.1007/s00357-021-09394-5.
Mahesh, Batta. 2020. “Machine Learning Algorithms - A Review.” International Journal of Science and Research (IJSR) 9 (1): 381–86. https://doi.org/10.21275/art20203995.
Mitchell, Tom M. 1999. “Machine Learning and Data Mining.” Communications of the ACM 42 (11). https://doi.org/10.1145/319382.319388.
Norshahlan, Muhammad, Hendra Jaya, and Rini Kustini. 2023. “Penerapan Metode Clustering Dengan Algoritma K-Means Pada Pengelompokan Data Calon Siswa Baru.” Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) 2 (6): 1042. https://doi.org/10.53513/jursi.v2i6.9148.
Pratama, Rizki Rino. 2020. “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia.” MATRIK?: Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer 19 (2): 302–11. https://doi.org/10.30812/matrik.v19i2.688.
Sarkar, Malay, Aisharyja Roy Puja, and Faiaz Rahat Chowdhury. 2024. “Optimizing Marketing Strategies with RFM Method and K-Means Clustering-Based AI Customer Segmentation Analysis.” Journal of Business and Management Studies 6 (2): 54–60. https://doi.org/10.32996/jbms.2024.6.2.5.
Seshia, Sanjit A., Dorsa Sadigh, and S. Shankar Sastry. 2022. “Toward Verified Artificial Intelligence.” Communications of the ACM 65 (7). https://doi.org/10.1145/3503914.
Sirunyan, A. M., A. Tumasyan, W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Erö, et al. 2020. “Identification of Heavy, Energetic, Hadronically Decaying Particles Using Machine-Learning Techniques.” Journal of Instrumentation 15 (6). https://doi.org/10.1088/1748-0221/15/06/P06005.
Suyal, Manish, and Sanjay Sharma. 2024. “A Review on Analysis of K-Means Clustering Machine Learning Algorithm Based on Unsupervised Learning.” Journal of Artificial Intelligence and Systems 6 (1): 85–95. https://doi.org/10.33969/ais.2024060106.
Tabianan, Kayalvily, Shubashini Velu, and Vinayakumar Ravi. 2022. “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data.” Sustainability (Switzerland) 14 (12). https://doi.org/10.3390/su14127243.
Vanneschi, Leonardo, and Sara Silva. 2023. “Artificial Neural Networks.” In Natural Computing Series. https://doi.org/10.1007/978-3-031-17922-8_7.
Watters, F. L., and K. F. MacQueen. 1967. “Effectiveness of Gamma Irradiation for Control of Five Species of Stored-Product Insects.” Journal of Stored Products Research 3 (3). https://doi.org/10.1016/0022-474X(67)90049-5.
Wijoyo A, Saputra A, Ristanti S, Sya’ban S, Amalia M, and Febriansyah R. 2024. “Pembelajaran Machine Learning.” OKTAL (Jurnal Ilmu Komputer Dan Science) 3 (2): 375–80. https://journal.mediapublikasi.id/index.php/oktal/article/view/2305.
Wu, Shuli, Wei Chuen Yau, Thian Song Ong, and Siew Chin Chong. 2021. “Integrated Churn Prediction and Customer Segmentation Framework for Telco Business.” IEEE Access 9. https://doi.org/10.1109/ACCESS.2021.3073776.
Published
2025-04-03
Section
Original Articles
Copyright (c) 2025 Bimas Ihsan Pratama, Verdi Yasin, Irfan Junaedi, Anton Zulkarnain Sianipar, Zulhalim
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