Implementasi Algoritma C4.5 Untuk Klasifikasi Pengenalan Warna Dasar di Taman Kanak-Kanak - Anandaya Difi Dzulardi Kalimanti; Vilianty Rafida; Aisyah Fajriantini (Bulletin of Computer Science Research, 2026)
Differences in early childhood ability to recognize basic colors at TK Negeri 01 Barong Tongkok indicate the need for a structured evaluation system to ensure objective assessment. The classification of these abilities is carried out by applying the C4.5 algorithm within a quantitative experimental framework. Data are collected through observations and color recognition tests involving 35 children as respondents, then processed using predefined attributes to construct a classification model. The analysis results group children’s abilities into three categories: Sangat Mengenal (High), Mengenal (Moderate), and Cukup Mengenal (Low). The experimental results indicate that the C4.5 algorithm is highly effective and stable, achieving an average classification accuracy of 85.71% through 5-Fold Cross-Validation. Furthermore, the resulting decision tree provides an intuitive and transparent structure that assists educators in interpreting evaluation outcomes and understanding the dominant variables that determine student learning success more clearly than black-box models. The primary contribution of this study lies in the provision of a data-driven evaluation model that generates empirically measurable decision rules (if-then rules), while simultaneously serving as a methodological bridge to create differentiated learning strategies at the early childhood education (PAUD) level. Consequently, the implementation of the C4.5 algorithm represents a strategic, efficient, and scientifically accountable alternative for enhancing pedagogical effectiveness and cognitive monitoring in early childhood education.