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All Journal International Journal of Electrical and Computer Engineering Jurnal Manajemen Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Bulletin of Electrical Engineering and Informatics JAM : Jurnal Aplikasi Manajemen Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi The Asian Journal of Technology Management (AJTM) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo JSAI (Journal Scientific and Applied Informatics) Indonesian Journal of Electrical Engineering and Computer Science Budimas : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Makara Human Behavior Studies in Asia Jurnal Komunikasi, Sains dan Teknologi (JKST) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi Nomico Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Setiawan Assegaf
Universitas Dinamika Bangsa
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Comparison of AdaBoost and Random Forest Methods in Osteoporosis Risk Prediction Based on Machine Learning Parlindungan H, Edwardo; Assegaff, Setiawan; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5297

Abstract

This study aims to determine the most effective ensemble machine learning algorithm for osteoporosis risk prediction in resource-constrained healthcare settings, specifically comparing AdaBoost and Random Forest performance on Southeast Asian population data. We implemented nested 5-fold cross-validation on a dataset of 1,958 records with 15 lifestyle and demographic attributes. Both algorithms underwent hyperparameter optimization, and performance was evaluated using accuracy, precision, recall, F1-score, and clinical utility metrics including cost-effectiveness analysis. AdaBoost achieved superior performance with 86.90% accuracy (95% CI: 84.2-89.6%) and perfect precision (1.00) compared to Random Forest's 84.69% accuracy and 0.92 precision. Statistical significance testing confirmed AdaBoost's advantage (p=0.032). Clinical implementation in three health centers demonstrated 60% reduction in unnecessary referrals. This is the first study to compare these algorithms specifically for Southeast Asian populations with clinical validation and cost-effectiveness analysis, providing a ready-to-deploy model for resource-limited healthcare settings.
Optimizing Heart Disease Classification Using C4.5, Random Forest, and XGBoost with ANOVA, Chi-Square, and AdaBoost Pratama, Andika; Assegaff, Setiawan; Jasmir, Jasmir; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5430

Abstract

Heart disease remains one of the leading causes of mortality worldwide, underscoring the need for accurate and scalable prediction models within clinical informatics. This study proposes a leakage-safe machine learning pipeline combining stratified splitting, SMOTE-based imbalance handling, and in-fold feature selection using ANOVA, Chi-Square, and AdaBoost-assisted ranking to enhance classification performance on a large heart-disease dataset consisting of 10,000 samples and 21 attributes. Three widely used algorithms, C4.5, Random Forest, and XGBoost, were evaluated to determine the optimal model-feature selection configuration for structured medical data. The results demonstrate that feature relevance contributes more significantly to predictive performance than increasing model complexity, with Random Forest achieving the highest accuracy, precision, recall, and F1-Score at 98.43% when combined with Chi-Square or ANOVA feature selection. C4.5 showed the greatest relative improvement, rising from 76.52% to 97.57% using AdaBoost-assisted selection, while XGBoost improved from 66.32% to 94.88% after statistical filtering. The dominant features identified such as CRP, BMI, blood pressure, fasting glucose, LDL, triglycerides, and homocysteine align with well-established cardiovascular biomarkers, supporting clinical validity. This research provides an important contribution to computer science by demonstrating an efficient and scalable hybrid FS-boosting framework capable of reducing unnecessary model complexity, improving generalization, and supporting low-latency deployment in clinical decision-support systems. The findings highlight the potential of structured-data machine learning to strengthen digital health diagnostics in resource-limited environments.
Enhancement Of The C4.5 Decision Tree Algorithm With Anova For Predicting Academic Achievement Of Students At Smpn.16 Kota Jambi Osviarni, Rice; Assegaff, Setiawan; Jasmir, Jasmir; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5431

Abstract

This study aims to improve the accuracy of predicting student academic achievement by integrating the Analysis of Variance (ANOVA) method with the C4.5 Decision Tree algorithm. In the context of information systems, this research holds significant importance for the development of more reliable Decision Support Systems (DSS) or early warning systems in school environments. The research was conducted at SMPN 16 Jambi City using secondary data from three academic years (2022/2023-2024/2025) covering academic variables, attendance, and parental income. The main issue addressed was the limitations of the C4.5 algorithm in handling irrelevant features and unbalanced data, which, at the system implementation level, can lead to inaccurate recommendations or alerts.This research method employed a data mining approach with stages including data cleaning, numeric conversion, missing value imputation, formation of derived variables, and categorization of the target variable "Achievement." The initial C4.5 model produced 72.81% accuracy on the training data and 69.71% accuracy on cross-validation. After feature selection using ANOVA, one insignificant variable was removed, resulting in a hybrid C4.5+ANOVA model with nine key features. Test results showed an increase in accuracy to 80.44% on the training data and 73.66% on the cross-validation data, representing an improvement of 7.63 and 3.95 percentage points, respectively.This improvement in model performance directly translates to an enhancement in the quality of the information system's output, yielding more reliable reports and predictions for teachers and school management.
Co-Authors Abdul Haris Abdul Rahim Afiz Sodik, Nafuri Afrizal Nehemia Toscany agus Nugroho Ahad, Syahdi Zul Ahmad Hussaein Aileen, Aileen Akwan Sunoto Alfayyadh, Muhammad Bilal Alfurqon, Dian Andi Nurul Izzah Andika Pratama Andrianti, Ari Asyep Syaefudin Baiti, Baiti Rahmah Beni Purnama Bujangdek Chindra Saputra Cipta Hadi, Denny Setya DAFIT AFIANTO Deddy Ackbar Rianto Desy Kisbiyanti Djauhari, Teuku Dodi Sandra Dodo Zaenal Abidin Dwi Putri, Salsabila An-nisa Eddy Suratno Effiyaldi Efrima, Randa Bias Eltha, Rico Janpria Erick Fernando Erick Fernando Fachruddin Fadillah Effendi, Husnil Fardinal Maidoni Feranika, Ayu Hakim, Ibnu Hakim, Muhammad Furqan Hamzah, Kamandanu Harjun Saputra Haryati Haryati Hendrawan Hendrawan Hendri Hendri Hendri Hendri HERRY MULYONO Herti Yani Ibnu Sani Wijaya Ilhami, Mohamad Indrawata Wardhana Indria, Lily Irwan Kurniawan Iskandar Iskandar Jasmir Jasmir Jasmir Jasmir, Jasmir Johni Paul Karolus Pasaribu Joni Devitra, Joni Joni Joni Juadli, Minal Junaidi Junaidi Jupri Jupri Kiki Windia Arifta Kurniabudi Kurniabudi, Kurniabudi Kurniawansyah, Kevin Laras Sabrina, Hanan Laura Prasasti Lies Aryani Lola Yorita Astri Lubis, Nia Paramitha Manja Purnasari Mardiana R. Maria Rosario Martono Martono Martono Martono Martono Martono Martono Mashuri Mashuri Mawaddatarrohmah Mubarak, Ahmad Zaky Muchlis, Rafi Akbar Muhammad Firman Kahfi Muhammad Rizki Saputra Musi Andi, Aprisal Nabilah Ramadhan, Nabilah Naralia, Mirah NURHADI Nurhadi Nurhadi Nurhadi Nurhayati Nurhayati Osviarni, Rice Padila, Siti Pahlevi. B, M. Riza Parlindungan H, Edwardo Pebriana, Retno Pradana, Lazuardi Yudha Prasetio, Luky Prastiwi, Hani Pratama, Anggi Andika Purnama, Benni Purnamasari, Ade Putri Indri Fitria Ningrum Putri Sabila , Wilda Rasyad, Haza Ibnu Renaldi Yulvianda Rista Aldilla Syafri Rizky Rachmadiyanto ROBY SETIAWAN Rohaini, Eni Rokhmah, Zakiyatur Rolly Maulana Awangga, Rolly Maulana S, Elly Gustiyani Safitri, Feny Sambodo, Prakoso Setyo Santoso Santoso Saputra, Harjun Saquro, Abdan Sasgita, Nabila Sharipuddin, Sharipuddin Sika, Xaverius Sika Soyata, Agung Raga Suprapto, Cahyo Adi Susanti , Eka Sutoyo, Mochammad Arief Hermawan Syafrial Fachri Pane Syaifudin, Asep Syaputra, Abe Wisnu Tamala, David Tandy, Juliana Tanjung, Sawaluddin F Titania Arida Nandini Treseli, Nerviana Trivenna, Priskha Tunas Agung Jiwa Brata Widjaja, Calvinia Flora William William Xaverius Sika Sika Yaticha, Ni Luh Ayu Yoga Pranata Yossinomita Yuni Utami, Tri Dewi Zulgani Zulgani