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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Journal of Information Technology and Computer Science Knowledge Engineering and Data Science JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Nusantara Hasana Journal Jurnal Informatika Terpadu Jurnal Janitra Informatika dan Sistem Informasi International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat Bhinneka Prosiding Seminar Nasional Pengabdian Kepada Masyarakat ROUTERS: Jurnal Sistem dan Teknologi Informasi Journal of World Future Medicine, Health and Nursing The Journal of Enhanced Studies in Informatics and Computer Applications Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
M. Syauqi Haris
Institut Teknologi, Sains, Dan Kesehatan RS Dr Soepraoen Kesdam V/Brw, Indonesia
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PREDIKSI PASIEN DENGAN PENYAKIT KARDIOVASKULAR MENGGUNAKAN RANDOM FOREST Mochammad Anshori; Nindynar Rikatsih; M. Syauqi Haris
TEKTRIKA Vol 7 No 2 (2022): TEKTRIKA Vol.7 No.2 2022
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v7i2.5279

Abstract

Cardiovascular disease is one of the deadliest diseases in the world. This is evidenced by data released by WHO which shows around 18 million deaths. This disease causes the cessation of the heartbeat which is the main source of life for the human body.This disease is caused by various things including an unhealthy lifestyle. Examples are consuming cigarettes and alcohol. In addition, it is also caused by other factors, namely health problems such as high blood pressure, cholesterol, diabetes, depression, or anxiety. The cardiovascular disease tends to be difficult to cure, therefore a precise and accurate prediction is needed in diagnosing patients. One method of making predictions is using machine learning techniques. In machine learning, there are various methods that can be used, one of which is the decision tree-based method, namely random forest. Before the random forest is implemented to create a model, the data is pre-processed by normalizing and applying cross-validation with k-fold = 10. The prediction results with the random forest in this study provide an accuracy of 98%. This accuracy is higher when compared to previous studies with the same dataset, namely 96.75% using the ensemble method and 91.61% with logistic regression. On this basis, it proves that the random forest can be used to predict cardiovascular disease. Key Words: cardiovascular disease, tree model, random forest, machine learning.
Co-Authors Ahsanun Naseh Khudori Anwar, David Saiful Arif Wahyudi Bagus Prasetyo Abdi , Benben Basmalah Abirestu Maulisuriandhy Erawati, Mourryn Galuh Fatwa Ramdani, Fatwa Faurika Faurika, F Faurika, Faurika Ferdy Riza Firahmi Rizky Fita Rusdian Ikawati Geraldo, Tostes Herry Prasetyo Wibowo Hevlie Winda Nazry S Iqbal, Ahmad Khofida, Nisa Khudori, Ahsanun N. Marilia, Busatto Mawardi, Ade Bagus Mika Debora Br Barus Mochamad Anshori Nindynar Rikatsih Nindynar Rikatsih Nugroho Teguh Yuono Pradini, Risqy Siwi Putriana, Rena Risqy Siwi Pradini Risqy Siwi Pradini Saletia, Maria Claudia Siwi Pradini, Risqy Tesa, Carrina A. Tri Astoto Kurniawan, Tri Astoto Wahyu Tejakusuma, Wahyu Wicaksana, Gigih Wisam Syahputra, Kelvin Yaniar Ramdani, Dandi Zuli Agustina Gultom