Segmentasi Konsumen Zero Waste Menggunakan Metode Gaussian Mixture Model dan Fuzzy C-Means Berdasarkan Preferensi dan Perilaku Pembelian
DOI:
https://doi.org/10.31004/joecy.v5i2.1319Keywords:
Clustering, Customer Segmentation, FCM, GMM, Zero-wasteAbstract
The increasing concern for the environmental problems has led to the growth of zero-waste stores as a form of sustainable consumption. This study aims to identify consumer segmentation in zero waste stores in North Kuta, Bali, based on preferences and purchasing behavior. Data was gathered through reliable and valid questionnaires involving 80 respondents who had shopped at three zero waste stores. The analysis was conducted using GMM (Gaussian Mixture Model) and FCM (Fuzzy C-Means) algorithms, with evaluation using silhouette coefficient and ICD Rate. The results showed that GMM was more optimal in forming homogeneous clusters (ICD Rate: 0.715). Three main clusters were identified: Eco-Engaged Advocates (respondents who are loyal and environmentally conscious), Value-Conscious Supporters (respondents who focus on product value), and Occasional Shoppers (pragmatic respondents who are responsive to promotions). The findings provide strategic implications for businesses in developing a more adaptive and data-driven marketing approach.
References
Cebeci, Z., & Yildiz, F. (2015). Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures. Journal of Agricultural Informatics, 6(3), Article 3. https://doi.org/10.17700/jai.2015.6.3.196
Chusna, H. A., & Rumiati, A. T. (2021). Penerapan Metode K-Means dan Fuzzy C-Means untuk Pengelompoan Sekolah Menengah Pertama (SMP) di Indonesia Berdasarkan Standar Nasional Pendidikan (SNP). Jurnal Sains Dan Seni ITS, 9(2), D216–D223.
Cohen, L., Manion, L., & Morrison, K. (2017). Research Methods in Education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32–57. https://doi.org/10.1080/01969727308546046
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Cengage.
Irfiyanda, C., Andreswari, R., & Hamami, F. (2022). Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry. 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA), 1–4. https://doi.org/10.1109/ICSINTESA56431.2022.10041585
Kementerian Lingkungan Hidup dan Kehutanan RI. (2024). https://sipsn.menlhk.go.id/sipsn/
Korolevska, I. (2023). Support for Infrastructure Investmentsin Indonesia (S4I) Consulting Services for Project Implementation.
Krasnov, D., Davis, D., Malott, K., Chen, Y., Shi, X., & Wong, A. (2023). Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection. Entropy, 25(7), Article 7. https://doi.org/10.3390/e25071021
Kurniawan, D. (2019). FKP Unud bersama tim Bali Partnership release Data Kebocoran Sampah ke laut Di prov. Bali. https://fkp.unud.ac.id/posts/fkp-unud-bersama-tim-bali-partnership-release-data-kebocoran-sampah-ke-laut-di-prov-bali
Laksana, E. A., & Fahrezi, M. M. (2024). Customer Segmentation and Analysis Based on Gaussian Mixture Model Algorithm. 67–75. https://doi.org/10.2991/978-94-6463-618-5_8
Patel, E., & Kushwaha, D. S. (2020). Clustering cloud workloads: K-means vs gaussian mixture model. Procedia Computer Science, 171, 158–167.
Rizqi, U. (2023). Gaussian Mixture Model dengan Algoritme Expectation Maximization untuk Pengelompokan Data Distribusi Air Bersih di Jawa Barat. 6, 745–750.
Syaifudin, A., Purwanto, P., Himawan, H., & Soeleman, M. A. (2023). Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 239–248. https://doi.org/10.30812/matrik.v22i2.2408
Tabianan, K., Velu, S., & Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability, 14(12), Article 12. https://doi.org/10.3390/su14127243
Weber, C. M., Ray, D., Valverde, A. A., Clark, J. A., & Sharma, K. S. (2022). Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1027, 166299. https://doi.org/10.1016/j.nima.2021.166299
Wierzchon, S., & Kłopotek, M. (2018). Modern Algorithms of Cluster Analysis. 34. https://doi.org/10.1007/978-3-319-69308-8
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