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Journal : journal of applied informatics and computing

Knowledge Discovery of AI Usage Dependency Patterns in Learning Activities Using Random Forest, XGBoost, Logistic Regression with SHAP-Based Interpretation Fidela Tertia Alfino; Puti Chalisa Wardhana; A. Salwa Aurelya Putri; Athiyyah Nuha Rotifa; Ken Ditha Tania; Ahmad Rifai; Dedy Kurniawan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12745

Abstract

The increasing use of Artificial Intelligence (AI) in education has influenced various learning activities. However, excessive AI usage has the potential to create dependency patterns that may affect students’ learning independence and critical thinking abilities. This study aims to analyze patterns of AI usage dependency in learning activities using a machine learning approach and to interpret the factors influencing such dependency. The analysis was conducted using a publicly available dataset representing usage intensity, session duration, AI assistance level, repeated usage behavior, and students’ academic characteristics. The research stages consisted of data preprocessing, categorical variable encoding, feature engineering, the construction of the Knowledge Dependency Level variable, class imbalance handling using SMOTE, and model evaluation using Stratified 5-Fold Cross Validation. The dataset was divided into 80% training data and 20% testing data, then modeled using Logistic Regression, Random Forest, and XGBoost. The results showed that XGBoost achieved the best performance with an accuracy of 0.6845, precision of 0.7288, recall of 0.6845, F1-score of 0.7028, and an AUC value of 0.860, indicating better discrimination capability compared to Random Forest and Logistic Regression. To support the knowledge discovery process, an interpretative analysis using SHAP was conducted to identify the contribution of each feature to the classification results. The interpretation revealed that SatisfactionRating was the most dominant feature influencing the prediction of AI usage dependency levels, followed by FinalOutcome, while academic factors such as StudentLevel and Discipline contributed relatively less. These findings transform previously implicit AI usage behavior patterns into explicit knowledge.
Co-Authors A. Salwa Aurelya Putri Ahmad Rifai Ahmad Rifai Ahmad Rifai Ali Ibrahim Alif, Rahmat Allsela Meiriza Allsela Meiriza, Allsela Amalsyah, Muhammad Rizky Anik Rahmawati, Anik Annisa Khoiriah Annisa Khoiriah Apriansyah Putra Apriansyah Putra Arba'i, Sultan Ardina Ariani Ari Wedhasmara Ariansyah Saputra Ariansyah Saputra Ariansyah Saputra Athiyyah Nuha Rotifa Bayu Wijaya Putra Billan, Angel Caroline Cherliana Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Enjelina Tampubolon Fathoni - Fatimah Azzahrah Fidela Tertia Alfino Fika Febrika Hanifah, Izzati Millah Hikmah, Lailatul Huda Ubaya Huda Ubaya Huda Ubaya Jaidan Jauhari Junia Kurniati Karisa Anjani Fakhri Kemahyanto Exaudi Ken Ditha Tania Ken Ditha Tania Lulu Monica Sari M Rudi Sanjaya M Rudi Sanjaya Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya Sanjaya Michael Darwin Muhammad Mayda Ary Pratama Muhammad Qurhanul Rizqie Muhammad Syarief Albani Nadia Anggraini Nilda Syah, Naziah Oktadini, Nabila Rizky Pacu Putra Perdani, Tharisa Antya Purwita Sari Purwita Sari Purwita Sari, Purwita Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri, Naila Raihana Rahma Ardhia Cahyani Rahmad Fadli Isnanto Rahmat Alif Akbari Rahmat Fadli Isnanto Rahmat Fadli Isnanto Risyahputri, Aliyananda Rizka Dhini Kurnia Rizki Kurniati Rossi Passarella Rudi Sanjaya Rudi Sanjaya Rusdi Effendi Sanjaya, M Rudi Sevtiyuni, Putri Eka Sultan Arba'i Sutarno Sutarno Sutarno Sutarno Syamsuryadi Syamsuryadi Yadi Utama Yesinta Florensia