Metode Hybrid Dalam Pengelompokkan Kemampuan Calistung Siswa Berbasis Machine Learning
DOI:
https://doi.org/10.47065/jimat.v5i2.500Keywords:
Hybrid Method; K-Means Clustering; K-Nearest Neighbors (KNN); Calistung SkillsAbstract
Students reading, writing, and arithmetic abilities (reading, writing, and arithmetic) are an important foundation in the academic development of elementary school students. This study aims to group students' reading, writing, and arithmetic abilities using a hybrid method based on machine learning, with grade data from two Elementary Schools in Lubuklinggau City. The method applied combines the K-Means Clustering algorithm for initial grouping and K-Nearest Neighbors (KNN) for classification. The analysis process includes data preprocessing, application of K-Means, cluster validation using Silhouette Score, and classification with KNN to ensure accuracy. As a result, K-Means successfully grouped students into three clusters: Middle (0), Low (1), and High (2). The KNN model with k = 3 which has the highest accuracy of 95% provides very good accuracy in testing the K-Nearest Neighbors (KNN) classification model with an accuracy of 97%, with very good precision, recall, and F1-score values for all clusters. These findings indicate that this hybrid approach is effective in classifying students' reading, writing and arithmetic abilities, which has implications for the development of more targeted learning strategies based on the characteristics of each group of students.
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