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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Karimah Agustin
Universitas Nusa Putra, Sukabumi
Computer Science & IT Decision Sciences, Operations Research & Management

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Prediksi Minat Belajar Siswa Berdasarkan Nilai Dan Intensitas Bermain Game Menggunakan Algoritma K-Nearest Neighbor Karimah Agustin; Gina Purnama Insany
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.949

Abstract

This study aims to predict students' learning interest based on Islamic creed scores and their frequency of playing the Free Fire game using the KNN algorithm. The dataset used includes Islamic creed assignment scores, final exam (UAS) scores, game play time, frequency of play, and learning interest categories. The analysis process was carried out through several stages: data exploration, data cleaning and processing, selecting the best values, modeling, and disseminating the model results. The model was tested using the 5-fold cross validation method and confusion matrix to ensure that the prediction performance was adequate. The results showed that academic scores, especially Islamic creed final exam scores, had a greater influence on learning interest than the intensity of playing the Free Fire game. The KNN model with a k value of 6 produced an accuracy of 94.73% and a very small prediction error, as seen in the Confusion Matrix and Classification Report. These results indicate that the model is capable of working well in classifying students' learning interest into medium or high categories. This prediction model is expected to be used as a tool to understand student learning interest patterns in the school environment. The contribution of this research is the application of KNN-based machine learning methods in analyzing student learning interests, which were previously dominated by conventional approaches such as regression and questionnaires.
Integrasi Analisis Butir Soal dan Klasifikasi Kognitif Untuk Personalisasi Rekomendasi Remedial Adaptif Menggunakan K-Nearest Neighbor Karimah Agustin; Alun Sujjada; Muhammad Ikhsan Thohir
Journal of Information System Research (JOSH) Vol 8 No 1 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v8i1.11066

Abstract

In many secondary schools, classroom assessments still use tests that are written by hand, which makes it hard for teachers to ensure each question matches the right level of thinking from Bloom's taxonomy or to check if the questions are statistically reliable. This study tackles that issue by creating a web-based testing system for a madrasah tsanawiyah, which automatically sorts multiple-choice questions, checks their quality using psychometric methods, and offers custom learning suggestions to help students improve. Cognitive-level tags (C1 to C6) are assigned by matching keywords that describe actions, and a kNearest Neighbor classifier uses TF-IDF features to sort items into groups that show more or less complex thinking. Item quality is checked using Classical Test Theory module automatically computes the difficulty index and discrimination index of each item from students answer patterns, and materials that help improve learning are chosen based on each student's weak areas through content-based filtering. The system was developed using the Waterfall model and was tested on 600 items taken from three different subjects. The classifier achieved 86.67% accuracy when using k=11, which was verified through 10- fold cross-validation showing 81.83% ± 5.19%, and all 23 black-box test cases were successfully passed. The results show that using automatic item analysis along with adaptive remediation is possible to implement in assessment practices at the madrasah level. The main contribution of this study is the integration of automatic cognitive classification, CTT-based psychometric analysis, and content-based filtering remedial recommendation into a single real-time examination platform, a combination that has not been found in previous similar studies, which generally address only one of these three aspects separately.
Co-Authors Alun Sujjada Gina Purnama Insany Muhammad Ikhsan Thohir