Analisis Karakteristik Pelanggan Menggunakan Metode RFM-DL dan Algoritma DBSCAN
Kata Kunci:
segmentasi pelanggan, modifikasi RFM, diversity, length, DBSCANAbstrak
Naskah ini akan diterbitkan di Knowledge Engineering dan Data Science (KEDS)
Referensi
Brahmana, R. W. S., Mohammed, F. A., & Chairuang, K. (2020). Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 11(1), 32. https://doi.org/10.24843/lkjiti.2020.v11.i01.p04
Deng, D. (2020). DBSCAN Clustering Algorithm Based on Density. Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020, 949–953. https://doi.org/10.1109/IFEEA51475.2020.00199
Ernawati, E., Baharin, S. S. K., & Kasmin, F. (2021). A review of data mining methods in RFM-based customer segmentation. Journal of Physics: Conference Series, 1869(1). https://doi.org/10.1088/1742-6596/1869/1/012085
Essayem, W., Bachtiar, F. A., & Priharsari, D. (2022). Customer Clustering Based on RFM Features Using K-Means Algorithm. Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022, 23–27. https://doi.org/10.1109/CyberneticsCom55287.2022.9865572
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. www.aaai.org
Khade, A. A. (2016). Performing Customer Behavior Analysis using Big Data Analytics. Procedia Computer Science, 79, 986–992. https://doi.org/10.1016/j.procs.2016.03.125
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327–1332. https://doi.org/10.1016/j.procs.2011.01.011
Monalisa, S., Juniarti, Y., Saputra, E., Muttakin, F., & Ahsyar, T. K. (2023). Customer segmentation with RFM models and demographic variable using DBSCAN algorithm. Telkomnika (Telecommunication Computing Electronics and Control), 21(4), 742–749. https://doi.org/10.12928/TELKOMNIKA.v21i4.22759
Raykov, Y. P., Boukouvalas, A., Baig, F., & Little, M. A. (2016). What to do when K-means clustering fails: A simple yet principled alternative algorithm. PLoS ONE, 11(9). https://doi.org/10.1371/journal.pone.0162259
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
Sander, J. ¨ O., & Ester, M. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. In Data Mining and Knowledge Discovery (Vol. 2).
Sari, J. N., Nugroho, L. E., Ferdiana, R., & Santosa, P. I. (2016). Review on Customer Segmentation Technique on Ecommerce. Advanced Science Letters, 22(10), 3018–3022. https://doi.org/10.1166/asl.2016.7985
Smaili, M. Y., & Hachimi, H. (2023). New RFM-D classification model for improving customer analysis and response prediction. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2023.102254
Wedel, M., & Kamakura, W. A. (2002). Introduction to the Special Issue on Market Segmentation. http://ssrn.com/abstract=2395277
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52. https://doi.org/10.1016/0169-7439(87)80084-9
Zhang, X., Zhao, D., Li, Y., Liu, Y., & Hu, G. (2022). Customer Portrait for Metrology Institutions Based on the Machine Learning Clustering Algorithm and the RFM Model. ICSAI 2022 - 8th International Conference on Systems and Informatics. https://doi.org/10.1109/ICSAI57119.2022.10005470
Brahmana, R. W. S., Mohammed, F. A., & Chairuang, K. (2020). Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 11(1), 32. https://doi.org/10.24843/lkjiti.2020.v11.i01.p04
Deng, D. (2020). DBSCAN Clustering Algorithm Based on Density. Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020, 949–953. https://doi.org/10.1109/IFEEA51475.2020.00199
Ernawati, E., Baharin, S. S. K., & Kasmin, F. (2021). A review of data mining methods in RFM-based customer segmentation. Journal of Physics: Conference Series, 1869(1). https://doi.org/10.1088/1742-6596/1869/1/012085
Essayem, W., Bachtiar, F. A., & Priharsari, D. (2022). Customer Clustering Based on RFM Features Using K-Means Algorithm. Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022, 23–27. https://doi.org/10.1109/CyberneticsCom55287.2022.9865572
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. www.aaai.org
Khade, A. A. (2016). Performing Customer Behavior Analysis using Big Data Analytics. Procedia Computer Science, 79, 986–992. https://doi.org/10.1016/j.procs.2016.03.125
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327–1332. https://doi.org/10.1016/j.procs.2011.01.011
Monalisa, S., Juniarti, Y., Saputra, E., Muttakin, F., & Ahsyar, T. K. (2023). Customer segmentation with RFM models and demographic variable using DBSCAN algorithm. Telkomnika (Telecommunication Computing Electronics and Control), 21(4), 742–749. https://doi.org/10.12928/TELKOMNIKA.v21i4.22759
Raykov, Y. P., Boukouvalas, A., Baig, F., & Little, M. A. (2016). What to do when K-means clustering fails: A simple yet principled alternative algorithm. PLoS ONE, 11(9). https://doi.org/10.1371/journal.pone.0162259
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
Sander, J. ¨ O., & Ester, M. (1998). Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. In Data Mining and Knowledge Discovery (Vol. 2).
Sari, J. N., Nugroho, L. E., Ferdiana, R., & Santosa, P. I. (2016). Review on Customer Segmentation Technique on Ecommerce. Advanced Science Letters, 22(10), 3018–3022. https://doi.org/10.1166/asl.2016.7985
Smaili, M. Y., & Hachimi, H. (2023). New RFM-D classification model for improving customer analysis and response prediction. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2023.102254
Wedel, M., & Kamakura, W. A. (2002). Introduction to the Special Issue on Market Segmentation. http://ssrn.com/abstract=2395277
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52. https://doi.org/10.1016/0169-7439(87)80084-9
Zhang, X., Zhao, D., Li, Y., Liu, Y., & Hu, G. (2022). Customer Portrait for Metrology Institutions Based on the Machine Learning Clustering Algorithm and the RFM Model. ICSAI 2022 - 8th International Conference on Systems and Informatics. https://doi.org/10.1109/ICSAI57119.2022.10005470
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