Analisa Algoritma K-Means untuk Segmentasi Pelanggan Berbasis Data Transaksi dalam Sistem Insight Dashboard E-Commerce
DOI:
https://doi.org/10.22441/fifo.2026.v18i1.004Abstract
Peningkatan volume dan kompleksitas data transaksi pada e-commerce berbasis Print-on-Demand menimbulkan tantangan dalam mengekstraksi insight pelanggan yang dapat ditindaklanjuti menggunakan pendekatan analitik konvensional. Meskipun algoritma K-Means telah banyak digunakan untuk segmentasi pelanggan, sebagian besar penelitian sebelumnya masih memiliki keterbatasan pada aspek validasi multi-metrik yang komprehensif serta minimnya integrasi dengan sistem pendukung keputusan yang aplikatif. Untuk mengatasi kesenjangan tersebut, penelitian ini mengusulkan kerangka segmentasi pelanggan berbasis K-Means yang dilengkapi dengan validasi cluster multi-metrik dan integrasi visualisasi analitik. Penentuan jumlah cluster optimal dilakukan melalui kombinasi metode Elbow dan metrik evaluasi internal, yaitu Silhouette Score, Calinski-Harabasz Index, dan Davies-Bouldin Index, guna memastikan keseimbangan antara ketahanan statistik dan interpretabilitas hasil. Hasil penelitian menunjukkan bahwa konfigurasi tiga cluster memberikan struktur segmentasi yang paling seimbang, serta mengungkap adanya ketimpangan signifikan dalam distribusi nilai pelanggan, di mana sebagian kecil pelanggan memberikan kontribusi dominan terhadap profit perusahaan. Untuk mengevaluasi aspek aplikatif, hasil clustering diimplementasikan ke dalam sistem Insight Dashboard dan dibandingkan dengan metode analisis manual berbasis spreadsheet menggunakan indikator kinerja efisiensi. Hasil evaluasi menunjukkan adanya peningkatan efisiensi analisis yang signifikan serta percepatan dalam identifikasi pelanggan bernilai tinggi. Kontribusi utama penelitian ini terletak pada integrasi validasi multi-metrik dalam penentuan cluster yang robust serta operasionalisasi hasil clustering ke dalam sistem dashboard sebagai pendukung pengambilan keputusan berbasis data pada lingkungan e-commerce Print-on-Demand.
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