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All Journal Annual Research Seminar Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Komputer Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Applied Science, Engineering, Technology, and Education Jurnal Generic KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ekonomi Jurnal Algoritma ABDIKOM The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Teknik Informatika dan Desain Komunikasi Visual
Dedy Kurniawan
Universitas Sriwijaya
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Komparasi Model Ensemble dan Algoritma Machine Learning Untuk Memprediksi Penyakit Jantung Muhammad Syarief Albani; Dedy Kurniawan; Ken Ditha Tania
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8346

Abstract

This study compared the performance of nine machine learning algorithms in predicting heart disease using a dataset dating back to 1988 and consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach totaling 1025 data. The dataset used includes medical features that reflect physiological states, clinical examination results, and cardiovascular risk factors, namely age, gender, type of chest pain, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, resting electrocardiography results, maximum heart rate, chest pain during physical activity, ST segment depression, ST segment slope, number of major blood vessels visible by fluoroscopy, and thalassemia status. The stages of this study include data cleaning, data transformation, and evaluation carried out using the data splitting method for training and testing as well as K-fold cross-validation with metrics of accuracy, precision, recall, F1 score, and AUC-ROC. The algorithms used in this study are Decision Tree, Random Forest, Support Vector Machine, MLP Classifier, Bagging Classifier, Gradient Boosting, CatBoost, XGBoost, and LightGBM with ensemble-based models, such as CatBoost, Random Forest, XGBoost, and LightGBM, showing consistent performance on various evaluation metrics when compared to non-ensemble models. Among all models tested, CatBoost showed the best performance, with an accuracy reaching 98%, an F1-Score of 0.980, and a Recall of 0.9875 then followed by other ensemble algorithms such as Random Forest, XGBoost and LightGBM. The results of this study indicate that ensemble models are proven to be more effective in predicting heart disease. This study aims to present an in-depth comparative study of the performance of ensemble algorithms and modern machine learning in predicting heart disease, as well as enriching the literature related to the application of Knowledge Discovery in the health sector and providing a basis for selecting more reliable prediction algorithms to support clinical decision making and the development of machine learning-based heart disease diagnosis support systems.
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 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 - Fathoni Fathoni Fatimah Azzahrah Fidela Tertia Alfino Fika Febrika Hanifah, Izzati Millah Hikmah, Lailatul Huda Ubaya Huda Ubaya Huda Ubaya Jaidan Jauhari 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 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 Rossi Passarella Rudi Sanjaya Rudi Sanjaya Sanjaya, M Rudi Sevtiyuni, Putri Eka Sultan Arba'i Sutarno Sutarno Sutarno Sutarno Syamsuryadi Syamsuryadi Yadi Utama