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All Journal Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) CogITo Smart Journal Jurnal Ilmiah Matrik IICS Servitium Smart Journal Jurnal Pengabdian Kepada Masyarakat Indonesia
Green Ferry Mandias, Green Ferry
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Journal : Jurnal Ilmiah Matrik

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Analisis Komparatif Algoritma Klasifikasi untuk Prediksi Diabetes Menggunakan Pembelajaran Mesin Mandias, Green Ferry; Ivanna Junamel Manoppo
Jurnal Ilmiah Matrik Vol. 27 No. 1 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/zwrvpg71

Abstract

Diabetes is a chronic disease with an increasing global prevalence, posing a serious threat to public health. This study aims to compare the performance of three classification algorithms—Logistic Regression, Decision Tree, and Support Vector Machine (SVM)—in predicting diabetes risk using secondary data from Kaggle. A quantitative approach was used, with model performance evaluated based on accuracy. Results show that SVM achieved the highest accuracy at 74.46%, followed by Logistic Regression at 73.59%, and Decision Tree at 70.56%. SVM excels in handling high-dimensional data and variability, while Logistic Regression is easier to interpret. Although Decision Tree is intuitive and easy to visualize, it is more prone to overfitting. These findings suggest that SVM is the most suitable algorithm for data-driven diabetes prediction, supporting the development of early detection systems that are fast, efficient, and cost-effective.
Penerapan Model Machine Learning untuk Memprediksi Serangan Jantung Dini Mandias, Green Ferry; Manoppo, Ivanna Junamel
Jurnal Ilmiah Matrik Vol. 27 No. 2 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/2cg02a51

Abstract

Heart disease is one of the leading causes of death worldwide, and early detection is crucial in reducing mortality rates. In Indonesia, heart disease is a primary cause of death, exacerbated by limited access to healthcare, especially in rural areas. Traditional diagnostic methods, such as physical examinations and EKG, often lack accuracy in predicting heart attacks. This research aims to develop an early prediction model for heart attacks using machine learning, specifically Random Forest and Support Vector Machine (SVM). These models were trained using a dataset containing various medical variables, including age, gender, blood pressure, cholesterol levels, and ECG results. The study finds that the Random Forest model outperforms SVM, with an accuracy of 90% and a recall of 93% for heart disease detection, making it more reliable for early detection of at-risk patients. The results suggest that machine learning can significantly enhance early heart attack detection, offering a potential solution to reduce heart disease-related mortality.
Co-Authors Christiady Somba Sirappa Devid Runtuwene Edson Yahuda Putra Erienika Meiling Lompoliu Feldryansa Somba Green Arther Sandag Haryanto Reza Musak Ivanna Junamel Manoppo Jacquline Waworundeng Jimmy H Moedjahedy Joe Yuan Mambu Marchel Thimoty Tombeng Marchelino Joseph Bojoh Maria Andriani Sambur Moedjahedy, Jimmy H. Oktoverano Lengkong Pierre Jericho Effendy Reynoldus Andrias Sahulata Stenly Ibrahim Adam Susi Susanti Timothy Matthew Jeremi Dirk Tombeng, Marchel Timothy Yohanes Septiawan