Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Andrew Castello Purba; Teny Handhayani

doi:10.34010/komputa.v13i1.11710

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Affiliations

Andrew Castello Purba
Universitas Tarumanagara

Teny Handhayani
Universitas Tarumanagara

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Perbandingan Algoritma K-Means, Affinity Clustering, dan Mini Batch K-Means Untuk Analisis Segmentasi Pasar

Andrew Castello Purba ,
Teny Handhayani

Vol 13 No 1 (2024): Komputa : Jurnal Ilmiah Komputer dan Informatika

DOI 10.34010/komputa.v13i1.11710

Abstract

Segmentasi pasar adalah proses membagi pasar menjadi kelompok-kelompok pembeli yang homogen berdasarkan karakteristik tertentu. Segmentasi pasar penting bagi perusahaan untuk memahami kebutuhan dan perilaku konsumennya sehingga dapat mengembangkan strategi pemasaran yang lebih efektif. Penelitian ini membandingkan tiga metode clustering, yaitu K-Means Clustering, Affinity Propagation Clustering, dan Mini Batch K-Means, dalam konteks analisis segmentasi pasar. Data yang digunakan adalah dataset marketing_campaign.csv yang terdiri dari 29 kolom dan 2240 baris. Eksperimen dilakukan untuk mengevaluasi performa ketiga metode dengan menggunakan metrik siluet. Hasil eksperimen menunjukkan bahwa Affinity Propagation menghasilkan nilai silhouette tertinggi sebesar 0.5861, diikuti oleh K-Means dengan 0.4675, dan Mini Batch K-Means dengan 0.4659. Temuan ini mengindikasikan bahwa Affinity Propagation dapat menjadi pilihan yang baik untuk analisis segmentasi pasar pada dataset tersebut. Hasil perbandingan ini memberikan wawasan tentang keunggulan dan kelemahan masing-masing metode clustering, membantu pemilih dalam memilih pendekatan yang paling sesuai untuk tujuan analisis mereka. Studi ini memberikan kontribusi pada pemahaman praktis penerapan teknik clustering dalam konteks pemasaran.