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All Journal ComEngApp : Computer Engineering and Applications Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Ilmiah FIFO Faktor Exacta Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Asia International Conference on Industrial Revolution for Polytechnic Education Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology Kapas: Kumpulan Artikel Pengabdian Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nasional Global Science: Journal of Information Technology and Computer Science
Yudo Devianto
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : comengapp computer engineering and applications journal

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Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan, Wawan; Devianto, Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.485

Abstract

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.
Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan , Wawan; Devianto , Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.
Co-Authors Aditya Wibowo Ahmad Fauzi Andika Bayu Shantya Budi ARIF, SUTAN MOHAMMAD Avrizal, Reza Baihaqi, Kiki Ahmad Bambang Sukowo Corry Coriah Haerudin Danny Manongga Dwiki Jatikusumo Eko Sediyono Eliyani Eliyani Eugenius Kau Suni Fauzi Ahmad Muda Galih Priambodo Galih Priyo Waseso Gugun Gunawan Gunawan, Wawan Herry Derajad Wijaya Hidayat, Rahmat Rian Inda Wulan Irawati Khatin Faisah Muhammad Reza Ramdani Nur Septiyani Aglis Paradilla Shabilla Pertiwi, Anggun Popy Yuliarty, Popy Ratna Mutu Manikam Rusmin Saragih, Rusmin Saputra, Dony Ramadhan Sari, Anggi Puspita Sari, Yunita Sartika Saruni Dwiasnati Sony Wirdianto Saputra Sri Yulianto Joko Prasetyo Sumarto, Marco Alfan Susafaati, Susafaati Wawan Gunawan Wawan Gunawan Wawan Gunawan Yana Cahyana Yuliarty, Poppy