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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Infotek : Jurnal Informatika dan Teknologi Indonesian Journal of Informatic Research and Software Engineering BATOBO: Jurnal Pengabdian Kepada Masyarakat Journal of Islamic Finance and Accounting Research Journal of System and Technology
Dian Ramadhani
Universitas Riau
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Other

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Journal : Journal of System and Technology

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Analisis Perbandingan Algoritma Klasifikasi untuk Prediksi Risiko Penyakit Jantung Muhammad Muttakin; Najwa Rokhan Rusmana; ramadhani, dian
Journal of System & Technology (SYSTEC) Vol. 1 No. 2 (2025): Journal of System & Technology (December Edition)
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Riau

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Abstract

Heart disease is a serious global health problem, causing 9.4 million deaths each year and is expected to increase to 23.3 million by 2030. The lack of early detection and unhealthy lifestyles are major factors contributing to the rise in cases, especially in developing countries. This study aims to develop an accurate and efficient heart disease risk prediction system as support for early diagnosis. The methods used involve four classification algorithms: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), which are evaluated based on accuracy, precision, and F1-score as well as cross-validation. Previous research has shown that KNN is effective in classifying medical data and can detect accurately. In this study, the evaluation results show that Decision Tree and Random Forest have the best accuracy, reaching 99%. Meanwhile, KNN and SVM have accuracies of 84% and 88%. Therefore, the selection of the model must consider the balance between accuracy and generalization ability. It is recommended to use larger and more diverse datasets to improve the reliability of the model in real-world applications, so that early detection systems can help reduce mortality rates due to heart disease.
Analisis Perbandingan Kinerja Algoritma K-Nearest Neighbors dan Support Vector Machine untuk Klasifikasi Penyakit Diabetes  Hatta Irsyad, Hidayat; Ikran Syafwan, Muhammad; ramadhani, dian
Journal of System & Technology (SYSTEC) Vol. 1 No. 2 (2025): Journal of System & Technology (December Edition)
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Riau

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Abstract

Diabetes remains a significant global health challenge, with the number of cases increasing annually. Early detection is essential to prevent severe complications and reduce the burden on healthcare systems. However, traditional diagnostic methods often demand considerable time and resources. This study investigates the performance of two machine learning algorithms—K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM)—in classifying diabetes using the Healthcare-Diabetes dataset. The models were evaluated based on accuracy, precision, recall, and F1-score. Experimental results indicate that the K-NN algorithm outperforms SVM, achieving an accuracy of 92.20% and an F1-score of 0.93. In comparison, the SVM algorithm attained an accuracy of 88.39% and an F1-score of 0.89. These findings suggest that the K-NN algorithm is more effective for diabetes classification in this dataset context.
Co-Authors Al Aminuddin Ambarani, Isnani Andhi, Rahmat Rizal Anisa, Rahmi A’yuniyah, Qurotul Dian Saputra Edi Susilo Edi Susilo Elvira, Winda Feri Candra Hatta Irsyad, Hidayat Ikran Syafwan, Muhammad Intan, Sofia Fulvi IRSAN TAUFIK ALI Mahdiyah, Evfi Muhammad Muttakin Najwa Rokhan Rusmana Nazira, Nanda Qoniah, Nurul Nyi Yanti Andriyani Yudi Hadiwandra, Tengku