Predicting Students Mathematical Decision Making Using k-Nearest Neighbor Technique
DOI:
https://doi.org/10.24256/jpmipa.v12i1.3669Keywords:
Educational Data Mining, K Parameter Value, Mathematical Decision Making, Nearest Neighbor, Prediction Method.Abstract
Abstract:
Mathematical decision-making abilities are mathematical information processing through risk evaluation and investigation of various possibilities and perspectives. However, the evaluation of mathematical decision-making abilities is still limited to high, medium, and a low level based on test scores and is not predictive naturally. The purpose of this study is to identify the parameter k in the k-nearest neighbor technique, which serves as the nearest-neighbor value determining the mathematical decision-making abilities of students to be predicted. The process of data exploration to prediction is employed by the data mining approach with the k-Nearest Neighbor method. A total of 65 first-year students taking Calculus I included as research samples. The research results show that a parameter value of k=15 is better at predicting the closeness of the mathematical decision-making with an accuracy of 93.33%, associated with the excellent category. The parameter value representing the closeness of the decision-making abilities level among students serves as a reference for teacher predictions to categorize students and create diversified teaching materials.
Abstrak:
Kemampuan pengambilan keputusan matematis merupakan pemrosesan informasi secara matematis melalui evaluasi risiko dan penyelidikan berbagai kemungkinan dan sudut pandang. Namun, evaluasi kemampuan pengambilan keputusan matematis masih terbatas pada level tinggi, sedang dan rendah berdasarkan nilai tes, tidak bersifat prediktif. Penelitian ini bertujuan mengidentifikasi nilai parameter k dengan teknik k-nearest neighbor yang berperan sebagai nilai ketetanggaan terdekat dari kemampuan pengambilan keputusan matematis. Proses eksplorasi data hingga prediksi dilakukan menggunakan pendekatan data mining dengan metode k-Nearest Neighbor. Sebanyak 65 mahasiswa tahun pertama yang mengikuti Kalkulus I sebagai sampel penelitian. Hasil penelitian menunjukkan nilai parameter k=15 yang lebih mampu memprediksi kedekatan tingkat kemampuan pengambilan keputusan matematis dengan akurasi 93,33%, termasuk pada kategori excellent. Nilai parameter tersebut menjadi acuan prediksi pengajar untuk mengkategorikan mahasiswa dan membuat diversifikasi bahan ajar.
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