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All Journal Elkom: Jurnal Elektronika dan Komputer Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) EVOLUSI : Jurnal Sains dan Manajemen JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) SPEED - Sentra Penelitian Engineering dan Edukasi Journal of Computer Science and Informatics Engineering Justika : Jurnal Sistem Informasi Akuntansi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Prawara Jurnal Pengabdian Masyarakat
Amin Nur Rais
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Public Health Social Sciences Other

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Optimasi Akurasi Klasifikasi Pada Prediksi Smokte Detection dengan Menggunakan Algoritma Adaboost Amin Nur Rais; Warjiyono Warjiyono
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5154

Abstract

The problem of fire is a threat to nature and the environment. To deal with fire incidents, a smoke detector was created and developed in combination with an IoT device so that incident data can be recorded properly where the recorded data will be used as a reference for increasing the accuracy of early detection. Increasing the accuracy of smoke detectors so that they can be combined with artificial intelligence technology. This research proposes prediction optimization using the adaboost algorithm combined with the naïve Bayes classification algorithm with a measurement matrix based on accuracy, recall, and precision. The results showed that using the adaboost algorithm could increase the resulting accuracy value with a value of 0.987. If you look at the evaluation from the precision side, it also shows that the use of the adaboost algorithm can increase the precision value with a value of 0.971. But the recall evaluation showed that without boost it got a better score with a value of 0.995
Optimizing Heart Disease Prediction Using SMOTE, Decision Tree, and Random Forest: A Regional Analysis Approach Ryan Harrys Pratama; Ade Surya Budiman; Amin Nur Rais
Journal of Computer Science and Informatics Engineering Vol 5 No 2 (2026): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i2.1675

Abstract

Heart disease remains the leading cause of mortality globally, including Indonesia. However, developing accurate predictive models is often hindered by class imbalance in medical datasets, where positive cases significantly outnumber negative cases. This study optimizes heart disease prediction by applying SMOTE (Synthetic Minority Oversampling Technique) regionally to Decision Tree and Random Forest algorithms using the "Heart Attack Prediction in Indonesia" dataset from Kaggle, which contains rural and urban attributes. Following the CRISP-DM framework, SMOTE was applied separately for each region to capture local distributional diversity and reduce regional bias. Results demonstrate that regional SMOTE significantly improved recall and F1-scores for both algorithms, particularly in rural areas where Random Forest recall increased from 60.50% to 70.34%. Statistical significance was confirmed through paired t-tests and Wilcoxon signed-rank tests on 5-fold cross-validation results (p < 0.001). Fairness analysis using Demographic Parity Difference and Equalized Odds Difference confirmed equitable performance across populations (DPD < 0.005, EOD < 0.008). Random Forest consistently outperformed Decision Tree, achieving the highest F1-score of 66.19% in urban regions post-SMOTE. These findings support that regional SMOTE effectively enhances model sensitivity toward minority classes while maintaining spatial fairness in heart disease prediction
Co-Authors Agus Subekti Ahmad Ishaq Ardianto-Universitas Bina Sarana Informatika, Rian Biktra Rudiarto Budiman, Ade Surya Chalimatusadiah Chalimatusadiah Chandra Kesuma Desiana Nur Kholifah Desy Ayu Fatmawati Eka Rahmawati Fajar Akbar Fandhilah Fandhilah Ibnu Alfarobi Jordy Lasmana Putra Mochamad Firmansyah Muhammad Rifqi Firdaus Nani Purwati Noor Hasan Nur Azizah Nurul Ichsan Richky Faizal Amir Robi Aziz Zuama Rousyati Rousyati Ryan Harrys Pratama Saifudin Saifudin Siti Munawaroh Sobari, Irwan Agus Sofian Wira Hadi Thira, Indra Jiwana Tiara Aninditha Vinsensius Puri Dwi Setyawan Walim Walim Warjiyono Warjiyono Wawan Kurniawan Yahya Mara Ardi Yustina Meisella Kristania - STMIK Nusa Mandiri Jakarta, Yustina Meisella Kristania