Penerapan Metode Clustering Dalam Segmentasi  Pelanggan Perusahaan Logistik

Hanan Kishendrian; Nisa Hanum; Cahyo  Prianto; Woro Isti  Rahayu

doi:10.31598/sintechjournal.v6i3.1432

Authors

Hanan Kishendrian Mahasiswa
Nisa Hanum
Cahyo Prianto Universitas Logistik & Bisnis Internasional
Woro Isti Rahayu Universitas Logistik & Bisnis Internasional

DOI:

https://doi.org/10.31598/sintechjournal.v6i3.1432

Keywords:

clustering, customer segmentation, RFM, data mining, K-means, CRISP-DM

Abstract

Marketing is important in business to compete and maintain market share. The development of technology brings major changes in the industry. In addition to product development as well as required services, and customer segmentation becomes a factor to consider in marketing strategies. Clustering, such as the K-Means method, is used in customer segmentation to divide data into groups based on similarities. This technique helps in useful pattern recognition and customer segmentation. By applying Clustering techniques in Data mining, companies can understand customer behavior, recognize similar customer groups, and plan marketing strategies accordingly. The results showed that the best cluster was generated with a k value of 4, and the data was normalized using the Min-Max Normalization method. Grouping customers in the form of clusters can enable the identification of consumer profiles to guide companies in decision making.

Downloads

Download data is not yet available.

References

Adiana, B. E., Soesanti, I., & Permanasari, A. E. (2018). Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering. Jurnal Terapan Teknologi Informasi, 2(1), 23–32.

Agustin, F. E. M., & Fitria, A. (2015). Implementasi algoritma k-means untuk menentukan kelompok pengayaan materi mata pelajaran ujian nasional (Studi kasus: SMP Negeri 101 Jakarta).

Baker, S. R., Baugh, B., & Sammon, M. C. (2020). NBER WORKING PAPER SERIES MEASURING CUSTOMER CHURN AND INTERCONNECTEDNESS. http://www.nber.org/papers/w27707

Chen, K., Hu, Y. H., & Hsieh, Y. C. (2015). Predicting customer churn from valuable B2B customers in the logistics industry: a case study. Information Systems and E-Business Management, 13(3), 475–494. https://doi.org/10.1007/s10257-014-0264-1

Chen, X., Fang, Y., Yang, M., Nie, F., Zhao, Z., & Huang, J. Z. (2018). PurTreeClust: A clustering algorithm for customer segmentation from massive customer transaction data. IEEE Transactions on Knowledge and Data Engineering, 30(3), 559–572. https://doi.org/10.1109/TKDE.2017.2763620

Daniele, R., Bate, G., & Quezada, I. (2017). Adventure Alternative and Moving Mountains Trust: A Hybrid Business Model for Social Entrepreneurship in Tourism (pp. 265–277). https://doi.org/10.1007/978-3-319-46518-0_16

Hamel, G., & Prahalad, C. K. (1991). Corporate imagination and expeditionary marketing. Harvard Business Review, 69(4), 81–92.

Hosseini, M., & Shabani, M. (2015). New approach to customer segmentation based on changes in customer value. Journal of Marketing Analytics, 3, 110–121.

Khairati, A. F., Adlina, A. A., Hertono, G. F., & Handari, B. D. (2019). Kajian Indeks Validitas pada Algoritma K-Means Enhanced dan K-Means MMCA. PRISMA, Prosiding Seminar Nasional Matematika, 2, 161–170.

Kim, S. Y., Jung, T. S., Suh, E. H., & Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1), 101–107.https://doi.org/10.1016/j.eswa.2005.09.004

Krause, M., & Yakel, E. (2007). Interaction in virtual archives: the polar bear expedition digital collections next generation finding aid. The American Archivist, 70(2), 282–314.

Liu, F., & Deng, Y. (2020). Determine the number of unknown targets in open world based on elbow method. IEEE Transactions on Fuzzy Systems, 29(5), 986–995.

Liu, Y., Kiang, M., & Brusco, M. (2012). A unified framework for market segmentation and its applications. Expert Systems with Applications, 39(11), 10292–10302. https://doi.org/10.1016/j.eswa.2012.02.161

Liu, Y., & Zhuang, Y. (2015). Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data. Journal of Computer and Communications, 03(06), 87–93. https://doi.org/10.4236/jcc.2015.36009

Lubis, A. H. (2016). Model segmentasi pelanggan dengan kernel k-means clustering berbasis customer relationship management. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 1(1).

Marbun, I. I., & Ginting, E. (2014). Analisis faktor-faktor yang mempengaruhi perilaku konsumen Kopi Luwak bermerek di Kota Medan. Journal of Agriculture and Agribusiness Socioeconomics, 3(6), 15244.

Marcus, C. (1998). A practical yet meaningful approach to customer segmentation. Journal of Consumer Marketing, 15(5), 494–504.

Neal, W. D., & Wurst, J. (2001). Advances in Market Segmentation. Marketing Research, 13(1).

Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1), 103–119.

Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes.

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.

Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conference Series: Materials Science and Engineering, 336, 012017.

Tripathi, S., Bhardwaj, A., & Poovammal, E. (2018). Approaches to clustering in customer segmentation. International Journal of Engineering & Technology, 7(3.12), 802–807.

Vähä-Karvia, S. E. J. (2021). How to adapt the requirements of an expedition to meet customer competences.

Wang, X., & Xu, Y. (2019). An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index. IOP Conference Series: Materials Science and Engineering, 569(5), 052024.

Wiedmann, K.-P., Buxel, H., & Walsh, G. (2002). Customer profiling in e-commerce: Methodological aspects and challenges. Journal of Database Marketing & Customer Strategy Management, 9, 170–184.

Windarto, A. P. (2017). Penerapan Datamining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering Method. Techno. Com, 16(4), 348–357.

Wulandari, G. F. (2014). Segmantasi Pelanggan Menggunakan Algoritma K-Means Untuk Customer Relationship Management (CRM) Pada Hijab Miulan. Ind. Mark. Manag, 1(7).

Xiao, J., Lu, J., & Li, X. (2017). Davies Bouldin Index based hierarchical initialization K-means. Intelligent Data Analysis, 21(6), 1327–1338.

Yanto, R., & Di Kesuma, H. (2017). Pemanfaatan Data Mining Untuk Penempatan Buku Di Perpustakaan Menggunakan Metode Association Rule. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 4(1), 1–10.

Yoseph, F., Ahamed Hassain Malim, N. H., Heikkilä, M., Brezulianu, A., Geman, O., & Paskhal Rostam, N. A. (2020). The impact of big data market segmentation using data mining and clustering techniques. Journal of Intelligent and Fuzzy Systems, 38(5), 6159–6173. https://doi.org/10.3233/JIFS-179698

Zhang, W., Yue, Z., Ye, J., Xu, H., Wang, Y., Zhang, X., & Xi, L. (2022). Modulation format identification using the Calinski–Harabasz index. Applied Optics, 61(3), 851–857.

Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences, 33(10), 1251–1257. https://doi.org/10.1016/j.jksuci.2018.09.004

Henderi, H. (2021). Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. IJIIS: International Journal of Informatics and Information Systems, 4(1), 13–20. https://doi.org/10.47738/ijiis.v4i1.73

Shihab, S. H., Afroge, S., & Mishu, S. Z. (2019). RFM Based Market Segmentation Approach Using Advanced K-means and Agglomerative Clustering : A Comparative Study. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–4.

Ünlü, R., & Xanthopoulos, P. (2019). Estimating the number of clusters in a dataset via consensus clustering. Expert Systems with Applications, 125, 33–39. https://doi.org/10.1016/j.eswa.2019.01.074

WIBOWO, G. (2022). Segmentasi Pelanggan Berdasarkan Recency FREQUENCY MONETARY (RFM) dan USER EVENT TRACKING MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING (Budi Luhur Tesis).

Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018). Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annual Reviews in Control, 46, 107–133. https://doi.org/10.1016/j.arcontrol.2018.09.003

Penerapan Metode Clustering Dalam Segmentasi Pelanggan Perusahaan Logistik

Authors

DOI:

Keywords:

Abstract

Downloads

References

Downloads

Published

How to Cite

Issue

Section

License