Klasifikasi Siswa Slow Learner Menggunakan Algoritma C4.5 Dalam Optimalisasi Pembelajaran di Sekolah Menengah Pertama
- Bela Priska Gloria Universitas Tanjungpura, Pontianak, Indonesia
-
Rahmi Hidayati
*
Universitas Tanjungpura, Pontianak,
Indonesia
- Hirzen Hasfani Universitas Tanjungpura, Pontianak, Indonesia (*) Corresponding Author
Abstract
Education is a learning process aimed at enhancing students' abilities in the school environment. SMP Negeri 12 Sungai Ambawang is one of the educational institutions located in Sungai Ambawang District. In the learning process, each student has a different level of understanding of the material being taught. Some students struggle to grasp lessons at the same pace as their peers, which categorizes them as slow learners. Lack of awareness about slow learners can hinder the teaching and learning process, as teachers must repeatedly explain the material. Therefore, this study aims to identify and classify slow learners using the C4.5 machine learning algorithm to help schools design more effective and adaptive learning strategies. The classification of slow learners is divided into four categories: normal, mild, moderate, and severe. The dataset consists of 135 data points, including 81 training samples and 54 testing samples. The attributes used include scores from subjects such as Civics (PKN), Indonesian Language, Mathematics, Natural Sciences (IPA), Social Sciences (IPS), and English. The C4.5 algorithm generates a decision tree with Natural Sciences (IPA) as the root node attribute. Testing using the Confusion Matrix shows an accuracy of 91%, precision of 54%, recall of 68%, and an error rate of 9%. The classification results indicate that 9% of students fall into the normal category, 24% into the mild category, 62% into the moderate category, and 0% into the severe category. These results demonstrate that the C4.5 algorithm is effective in classifying slow learners.
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