DATA MINING ANALYSIS OF SHELL OIL SALES USING THE C4.5 ALGORITHM AT CV. HARAPAN KARYA MANDIRI

Seci Monika; Muhammad Husni Rifqo

doi:10.21063/jtif.2025.V13.2.48-56

Authors

Seci Monika University of Muhammadiyah Bengkulu
Muhammad Husni Rifqo

DOI:

https://doi.org/10.21063/jtif.2025.V13.2.48-56

Keywords:

Analysis, Data Mining, Sales, C4.5 Algorithm

Abstract

This study focuses on the analysis of Shell oil sales at CV. Harapan Karya Mandiri (HKM) Bengkulu, which faces challenges in predicting consumer demand and managing stock efficiently. CV. HKM Bengkulu is an official distributor of PT. Shell Indonesia, competing in the vehicle lubricant industry. To address the challenges of competition and demand uncertainty, this study applies data mining methods, particularly the C4.5 algorithm, to analyze historical sales data and uncover significant patterns and trends. Data mining is a technique that helps identify hidden patterns and insights in large datasets to support decision-making. The C4.5 algorithm is employed to build a predictive model through a decision tree, which classifies data based on certain variables such as oil type, sales region, or time period. This model is expected to assist CV. HKM in predicting customer demand, optimizing sales strategies, and improving stock planning efficiency. Additionally, the results from the C4.5 algorithm provide practical benefits by enabling CV. HKM to optimize inventory management, target marketing efforts more effectively, and enhance operational efficiency. The insights derived from the model support data-driven decisions, improve business performance, and maximize profits by aligning stock levels with demand trends, thereby reducing wastage and improving profitability.

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