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All Journal JUSIFOR : Jurnal Sistem Informasi dan Informatika
Anis Zubair
Universitas Merdeka Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Segmentasi Pelanggan E-Commerce Berdasarkan Pola Pembatalan dan Pengembalian Pesanan Menggunakan K-Means Yulia Natasya Farah Diba Arifin; Anis Zubair
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8887

Abstract

This study examines e-commerce customer segmentation based on cancellation and return behaviors using K-Means clustering as a proof-of-concept. Using the Pakistan E-Commerce Dataset (2017), we performed preprocessing, behavioral feature engineering (Cancellation Rate, Return Rate, Average Order Value, Discount Sensitivity, Preferred Payment Method, and Total Orders), Min–Max normalization, and K-Means modeling. Cluster number validation relied on the Elbow Method, Silhouette Score, and PCA visualization. Results indicate K = 3 stable clusters: Price-Sensitive Customers (69.48%) high per-order value but price-sensitive; Loyal Customers (13.55%), high frequency and low CR/RR; and High-Risk Customers (16.97%), high return rate with low value contribution. The findings demonstrate K-Means’ effectiveness in identifying cancellation/return patterns and provide a conceptual basis for risk management and further analysis.
Perbandingan Double Exponential Smoothing, Single Exponential Smoothing dan MA terhadap Peramalan Jumlah Pelanggan Di Gendis Jowo Aryadhiva Soetedja; Rahmatina Hidayati; Anis Zubair; Luthfi Indana
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8896

Abstract

Gendis Jowo experiences fluctuations in the number of nasi box customers, which lead to suboptimal stock management and operational inefficiency, thereby requiring a forecasting approach to predict customer numbers more accurately. This study applies three forecasting methods Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Moving Average (MA)—with the aim of determining the most accurate method for forecasting the next period’s customer count. Historical data from January 2022 to August 2025 were analyzed, with SES and DES parameters optimized using the Optimal ARIMA approach, and accuracy evaluated through MAPE, MAD, and MSD. The results show that the Moving Average method with a length of 4 (MA4) provides the highest accuracy with the lowest error values, making it the best-performing model. Based on the MA4 method, the number of customers for the next period is predicted to be 1,065.88, and this result can be used to plan stock requirements, packaging needs, and operational activities more effectively.
Co-Authors Aryadhiva Soetedja Luthfi Indana Rahmatina Hidayati Yulia Natasya Farah Diba Arifin