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All Journal IKRAM: Jurnal Ilmu Komputer Al Muslim ALMUISY: Journal of Al Muslim Information System
Casto Uripto
Politeknik Bisnis Digital Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Analisis Pola Pembelian Produk Digital Menggunakan Metode FP-Growth untuk Optimalisasi Strategi Bundling pada Marketplace Online Casto Uripto; Putri Mayang
IKRAM: Jurnal Ilmu Komputer Al Muslim Vol. 5 No. 1 (2026): IKRAM: Jurnal Ilmu Komputer Al Muslim
Publisher : STMIK Al Muslim

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Abstract

The rapid growth of online marketplaces has generated massive transaction data, which is often underutilized in supporting marketing strategies, particularly in product bundling. This study aims to analyze digital product purchasing patterns using the FP-Growth algorithm to optimize bundling strategies in online marketplaces. The dataset used is the Online Retail dataset from the UCI Machine Learning Repository, which has undergone preprocessing, transformation, and analysis stages. The FP-Growth algorithm is applied to extract frequent itemsets and generate association rules based on support, confidence, and lift ratio metrics. The results indicate that FP-Growth effectively identifies relationships between frequently co-purchased products in an efficient manner. The generated association rules can serve as a foundation for developing bundling strategies and product recommendations. Therefore, the application of FP-Growth proves to be effective in enhancing the utilization of transaction data for business decision-making in online marketplaces.
Analisis Segmentasi Pelanggan E-Commerce Menggunakan Metode Clustering Berbasis RFM Casto Uripto; Putri Mayang; Adelia Alvi Yana
ALMUISY: Journal of Al Muslim Information System Vol. 5 No. 1 (2026): ALMUISY: Journal of Al Muslim Information System
Publisher : STMIK Al Muslim

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Abstract

Pertumbuhan pesat industri e-commerce menghasilkan volume data transaksi pelanggan yang besar dan kompleks. Pemanfaatan data tersebut secara optimal menjadi tantangan bagi perusahaan dalam menyusun strategi pemasaran berbasis perilaku pelanggan. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan e-commerce menggunakan pendekatan Recency, Frequency, Monetary (RFM) yang dikombinasikan dengan algoritma K-Means clustering. Dataset yang digunakan adalah Brazilian E-Commerce Public Dataset (Olist) yang diperoleh dari Kaggle. Tahapan penelitian meliputi preprocessing data, perhitungan nilai RFM, normalisasi menggunakan Min-Max Scaling, penentuan jumlah cluster menggunakan metode Elbow, serta evaluasi model menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa segmentasi berbasis RFM mampu mengelompokkan pelanggan ke dalam beberapa cluster dengan karakteristik berbeda. Evaluasi model menghasilkan nilai Silhouette Score sebesar 0,278 yang menunjukkan kualitas cluster cukup baik. Segmentasi yang dihasilkan dapat digunakan sebagai dasar penyusunan strategi retensi pelanggan, reaktivasi pelanggan berisiko churn, serta optimalisasi pemasaran berbasis data.
Co-Authors - STMIK Nusa Mandiri, Adelia Alvi Yana Putri Mayang Putri Mayang