Implementasi Metode Cluster Preferensi Pelanggan Teh Kemasan dengan K-Means
Keywords:
Cluster, preference, packaged tea, K-MeansAbstract
Tea is a product widely consumed by the Indonesian population. Amid increasing market competition, an appropriate marketing strategy is essential. One approach to optimizing marketing efforts is to conduct market segmentation through customer preference clustering. This study utilized a packaged tea sales dataset (secondary data) obtained from Kaggle.com and processed it using the K-Means clustering method through the WEKA tool. The clustering results produced five cluster patterns, with the most dominant patterns found in two clusters: JAVANA tea and Sosro bottled tea. Based on the RFM model analysis, JAVANA tea was identified as the most preferred product, particularly among young consumers, with a time span of 15:46. Meanwhile, Sosro bottled tea ranked second, preferred by consumers across all age groups, with a time span ranging from 09:58 to 16:05.
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References
Aditya, Tubagus. 2002. Optimalisasi Distribusi Teh Botol Sosro di PT Sasana Caraka Mekarjaya Unit Cakung Tugu. Skripsi. Jurusan Ilmu-ilmu Sosial Ekonomi Pertanian. Fakultas
D. Birant, Data Mining Using RFM Analysis, in Knowledge-Oriented Applications in Data Mining, Turkey: KImito Funatso, 2011, pp. 91–108
Hidayat, N., D. P. Gevano, A. Putra, and R. Ilahi. 2025. Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi 6(5):3914–3924.
J. Zhao, W. Zhang, and Y. Liu 2010. Improved K-Means cluster algorithm in telecommunications enterprises customer segmentation, Proc. 2010 IEEE Int. Conf. Inf. Tehory Inf. Secur. ICITIS 2010, pp. 167–169
Kapti dan Dwi Astuti. 2025. Implementasi Algoritma K - Means Clustering untuk Segmentasi Pelanggan Implementation of K - Means Clustering Algorithm for 14:2751–2767.
Kodinariya, Trupti M. & Makwana, Prashant R.,(2013). Review on determining number of cluster in K- Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, I(6), pp. 90-95
Lathifah, S. N., and Z. F. Azzahra. 2025. AI-Driven Customers Segmentation Using K-Means Clustering. G-Tech: Jurnal Teknologi Terapan 9(1):320–329.
Miao, H. 2025. Consumer Preference and Product Differentiation in the Chinese Tea Market : A Conjoint Analysis Approach 2(3):158–161.
Pertanian. Institut Pertanian Bogor.
R. J. Kuo, S. H. Lin, and Z.-Y. Chen 2015. Integration of Particle Swarm Optimization and Immune Genetic Algorithm- Based Dynamic Clustering for Customer Clustering, Int. J. Artif. Intell. Tools, vol. 24, no. 5, p. 1550019
Soraya, N. 2007. Sehat dan Cantik Berkat Teh Hijau. Penebar Swadaya, Jakarta.
Sumarwan, Ujang. 2002. Analisis Hubungan antara Persepsi Popularitas, Persepsi Kualitas, Persepsi Harga dengan Merek Teh Celup yang Dikonsumsi. Staff Pengajar Jurusan Gizi Masyarakat dan Sumberdaya Keluarga dan Magister Management Agribusiness. Fakultas Pertanian. Institut Pertanian Bogor.
W. Li. 2008. Modified K-Means Clustering Algorithm, 2008 Congr. Image Signal Process., pp.618–621.
