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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Teknologi Informasi dan Terapan (J-TIT) INTI Nusa Mandiri Jurnal Informatika dan Rekayasa Elektronik Jurnal Infortech Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Indonesian Journal of Networking and Security - IJNS Teknologika Konstelasi: Konvergensi Teknologi dan Sistem Informasi Journal of Applied Agricultural Science and Technology Paradigma Jurnal Ticom: Technology of Information and Communication
Verry Riyanto
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Pengoptimalan Seleksi Fitur Berbasis Particle Swarm Optimization pada Prediksi Gagal Jantung dengan Random Tree Ridwansyah Ridwansyah; Sri Rahayu; Jajang Jaya Purnama; Verry Riyanto; Abdul Hamid
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 8 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v8i1.16595

Abstract

Heart failure is one of the leading causes of hospitalization and mortality, particularly among older adults. Early detection is essential to support effective clinical decision-making. This study aims to develop a heart failure prediction model using the Random Tree classification algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. Random Tree was chosen for its simplicity and interpretability, while PSO was employed to identify the most relevant features and remove less important ones. The dataset was obtained from the UCI Machine Learning Repository and consists of 299 patient records with 12 clinical attributes. Model performance was evaluated using accuracy, precision, recall, and Area Under the Curve (AUC). The baseline Random Tree model achieved an accuracy of 75.58% and an AUC of 0.632. After applying PSO-based feature selection, the optimized model achieved an accuracy of 82.27% and an AUC of 0.740. These findings indicate that integrating PSO with Random Tree effectively improves heart failure prediction performance and has potential as a clinical decision-support tool
Co-Authors Abdul Hamid Abdul Hamid Abdul Hamid Achmad Maezar Bayu Aji Achmad Suryo Prayogo Andi Saryoko Anton Biktra Rudianto - STMIK Nusa Mandiri Jakarta Dahlia Dahlia Eni Pudjiarti Eri Riana Ganda Wijaya Ganda Wijaya Ganda Wijaya Ganda Wijaya, Ganda Hafis Nurdin Henny Destiana Herlambang Brawijaya Hikmah, Noer Imam Nawawi Imam Nawawi, Imam Jajang Jaya Purnama Lia Mazia, Lia Marimin Marimin Mohammad Roffi Suhendry Muhamad Syukur Nafila, Dzhikrokhatun Niar Adisty Putra Nuraeni Herlinawati Nurmalasari Rahmatul Husna Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Ridwansyah Rudianto, Biktra Sartini Sartini Shelvie Nidya Neyman Shena Arrahima Qolby Siti Faizah Sri Nurdiati Sri Rahayu Sri Rahayu SRI RAHAYU Sugiono Sumarna Sumarna . Sumarna Sumarna Sumarna Sumarna Suryadithia, Rachmat Susy Rosyida Tati Mardiana, Tati Titin Prihatin Toto Haryanto Tri Hartati, Tri WITRIANA ENDAH PANGESTI Yamin Nuryamin Yana Iqbal Maulana