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p-Index From 2021 - 2026
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All Journal International Journal of Information Technology and Computer Science Applications (IJITCSA)
Tia Aulia
Universitas Bhayangkara Jakarta Raya
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Clustering and Sales Prediction Using K-Means and Simple Linear Regression Tia Aulia; Wowon Priatna; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.209

Abstract

CV. Cipta Usaha Selaras faces challenges in identifying customer purchasing patterns and accurately projecting sales values. The importance of this research lies in the company’s need for data-driven marketing strategies and efficient operational planning. This study employs the K-Means algorithm to cluster customers based on purchase frequency and total transaction value, as well as Simple Linear Regression to predict total purchases based on transaction frequency. The data analyzed consists of 358 sales transaction entries from the year 2024. The clustering results reveal three customer segments with distinct characteristics, with a Silhouette Score of 0.7913, indicating good segmentation quality. The regression model produced an equation with a coefficient of determination (R²) of 0.6910, a MAE of IDR 213 million, and a MSE of IDR 206 trillion. These results indicate that the applied approach provides a reasonably representative overview of customer purchasing behavior. This research offers a significant contribution to data-driven decision-making within the company, particularly in the development of marketing strategies and estimation of potential revenue.
Co-Authors Muhammad Yasir Priatna , Wowon