Classification of Post-School Tendencies of Madrasah Aliyah Students Using the K-Nearest Neighbor Algorithm
DOI:
https://doi.org/10.30871/jaic.v10i2.12354Keywords:
Data Mining, K-Nearest Neighbor, Classification, Madrasah Aliyah, Graduation PredictionAbstract
This research develops a classification model to identify post-graduation tendencies of Madrasah Aliyah students using the K-Nearest Neighbor (KNN) algorithm with academic report card scores as input features. The dataset includes 76 students, using average scores from semesters 1 to 5 as predictors and students’ post-school tendencies as the target variable. Data preprocessing involved normalization and splitting the dataset into training and testing subsets, while similarity between instances was measured using Euclidean Distance with k = 5. The experimental results achieved an accuracy of 87.50%, indicating that KNN performs well on small-scale academic datasets. This study contributes by specifically applying KNN to classify post-school tendencies in a Madrasah Aliyah context using limited academic features, an area that has not been extensively explored in previous educational data mining studies. The proposed model can assist schools in providing data-driven academic counseling and decision support within the Madrasah Aliyah environment.
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