Algoritma C4.5 sebagai Penerapan Decision Tree-Based Classification Model untuk Mengklasifikasikan Tingkat Omzet UMKM Berdasarkan Profil Bisnis

Abstract

Micro, Small, and Medium Enterprises (MSMEs) significantly contribute to the national economy. However, many of them experience stagnant revenue due to limited business profiles and the lack of data-driven development strategies. This study aims to build a classification model for MSME revenue levels based on business profile attributes using the Decision Tree C4.5 algorithm. The dataset consists of over 13,000 publicly available records, which were preprocessed and categorized into three revenue classes: low, medium, and high, based on quartile distribution. The results show that the C4.5 model achieves a classification accuracy of 48.53%, with a dominant prediction in the medium revenue category. The resulting decision tree structure generates interpretable and logical rules, such as: “If the business type is services, not legally registered, and has assets less than IDR 7 million, then the revenue tends to be medium.” Further analysis reveals that attributes such as business type, legal status, assets, and production capacity are key predictors of MSME revenue classification. Although the model's accuracy is still limited, this approach provides a solid foundation for developing decision support systems for MSME development agencies. The study recommends exploring additional features and implementing ensemble methods to improve model performance in future research