Educational Data Mining: Multiple Choice Question Classification in Vocational School

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification

Teknik Random Forest untuk Meningkatan Akurasi Data Tidak Seimbang

“…Langkah kedua adalah pre-processing. Pre-processing yang dilakukan adalah konversi data dari beberapa atribut [19], [20]. Konversi data yang dilakukan adalah mengubah data dari bentuk teks ke bentuk angka [21].…”