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All Journal Speed - Indonesian Journal on Computer Science Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Fibusi (Jurnal Online Fisika) Seruni - Seminar Riset Unggulan Nasional Inoformatika dan Komputer Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Biologi Tropis EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA SENTRALISASI Jurnal Teknik dan Informatika Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Inovasi Teknik Informatika Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Rekayasa, Teknologi, dan Sains PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam Indonesian Journal of Networking and Security - IJNS Journal Of Human And Education (JAHE) Sistem Pendukung Keputusan dengan Aplikasi Jurnal Riset Perbankan, Manajemen dan Akuntansi Jurnal Ilmu Multidisplin MDP Student Conference Science of Management and Students Research Journal (SMS) Asia Information System Journal Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer VCode Jurnal Suluah Komunitas Indonesian Journal of Economics and Strategic Management International Journal of Science and Mathematics Education International Journal of Mathematics and Science Education Menulis: Jurnal Penelitian Nusantara JURNAL SATYA MANDIRI MANAJEMEN DAN BISNIS
Indra Irawan
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Journal : MDP Student Conference

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Perbandingan Algoritma ID3, Naive Bayes, SVM Berbasis PSO untuk Prediksi Serangan Jantung Prayogi, M. Bagus; Irawan, Indra; Fajar, Yahya Ibnu
MDP Student Conference Vol 3 No 1 (2024): The 3rd MDP Student Conference 2024
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v3i1.6979

Abstract

This research aims to evaluate the precision of three primary predictive algorithms—ID3, naïve bayes, and SVM (SVM)—optimized using the particle swarm optimization (PSO) algorithm for detecting and predicting heart attacks. The methodology involves comparing these algorithms as tools for categorizing data into relevant groups and optimizing them using PSO to enhance prediction accuracy. Data from kaggle and uci repositories are managed using RapidMiner. The study reveals accuracy results: the ID3 algorithm achieves 75.20% accuracy with AUC 0.735, post-PSO optimization increases accuracy to 80.49% with AUC 0.815. The naïve bayes algorithm attains 81.52% accuracy with AUC 0.890, post-PSO optimization enhances accuracy to 83.94% with AUC 0.901. The SVM (SVM) algorithm records 82.13% accuracy with AUC 0.895, post-PSO optimization boosts accuracy to 84.83% with AUC 0.900.
Co-Authors Achmad Fauzi Affandi, Syawlani Afifah, Siti Alam, Andi Rifqah Purnama Anggraeni, Rosfitri Anggraini, Melani ardila, desi Ari Sulistyowati Azahra, Kaila Bambang Eka Purnama Better Swengky Cahyadi, Irsan Christoporus Indra Wahyu Darlius Desi Sri Pasca Sari Sembiring, Desi Sri Pasca Sari Deti Elice Dwi Andriani, Dwi Dwi Apriliana, Atun E. Suhendi Elrisa Ramadhani Emy Khikmawati Fajar, Yahya Ibnu Haidorizal, Rifkan Harlina, Sitti Iswati Kabul Warsito Khairi Murdy Kriswanda, Bagus Kumar, D. Senthil Lilik Hasanah Marwan Marwan Merri Silvia Basri Muhammad Firjatullah Nur Akbar Noor Islahudin, Ahmad Nur Ikhsan Nurlinda Nurlinda, Nurlinda Ocvanoel Patricia Agata, Veren Prayogi, M. Bagus Pribadi, Antoni Purnama, Pradani Ayu Widya Rahmi, Nadya Alinda Rajab, Junaidi Ramadhani, Elrisa Rizqy, Nurul Safni Marwa Sakinah, Mutiara Siti Safaria Sri Wahyu Widiati Sunardi Thesa Ramadhani Warsito, Kabul Wathan, M.Hizbul Yunan Hepy Urbani Yunita, Tyna