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Vilianty Rafida
STMIK Widya Cipta Dharma, Samarinda
Computer Science & IT

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Klasifikasi Tingkat Pemahaman Siswa Kelas VI Sekolah Dasar terhadap Perangkat Keras Komputer Menggunakan Metode Decision Tree Suchi Azzahro Syarif; Vilianty Rafida; Rizky Zakariyya Rasyad
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1095

Abstract

The development of information technology in education requires students to have a basic understanding of computer hardware from an early age. However, the level of students’ understanding of computer hardware still varies, especially at the elementary school level. This condition can affect students’ ability to understand the use of technology more effectively in computer-based learning processes. This study aims to classify the level of understanding of sixth grade elementary school students regarding computer hardware using the Decision Tree method. The research data were obtained through a questionnaire consisting of 25 questions related to computer hardware. Each student’s answer was assigned points based on its correctness level, then the total score was calculated and converted into a 0–100 scale before being categorized into three classes, namely High Understanding, Moderate Understanding, and Low Understanding based on score ranges adjusted to the distribution of the research data. The data show that there are 30 students in the High Understanding category, 18 students in the Moderate Understanding category, and 6 students in the Low Understanding category. The classification process was carried out using the Decision Tree method with 80% training data and 20% testing data. The model achieved an accuracy of 45% on the test data. The result indicates that the model is not yet optimal in performing balanced classification across all categories of student understanding. The findings of this study contribute to the application of the Decision Tree classification method in elementary education, particularly in identifying students’ understanding of computer hardware based on questionnaire data.
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 Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1102

Abstract

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.
Co-Authors Aisyah Fajriantini Anandaya Difi Dzulardi Kalimanti Rizky Zakariyya Rasyad Suchi Azzahro Syarif