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All Journal International Journal of Electrical and Computer Engineering Erudio: Journal of Educational Innovation Jurnal Transformatika Journal of Development Research Journal of Information Technology and Computer Science SMARTICS Journal Prosiding Seminar Nasional Teknoka Jurnal Tecnoscienza JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Perwira Journal of Science and Engineering (PJSE) Smart Techno (Smart Technology, Informatic and Technopreneurship) Jurnal Inovasi dan Pengembangan Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP)
Rizdania, Rizdania
Universitas PGRI Wiranegara Pasuruan, Indonesia
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : jurnal transformatika

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IDENTIFIKASI PENYAKIT JANTUNG MENGGUNAKAN MACHINE LEARNING: STUDI KOMPARATIF Sintiya, Endah Septa; Rizdania, Rizdania; Afrah, Ashri Shabrina; Pramudhita, Agung
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6274

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns that are close to heart disease. Prediction of heart disease is important given the behavior of people who are still prone to risk factors. Conditions where predictions using machine learning for heart disease have not been compared with many using machine learning methods. Predictions of heart disease are needed along with the interrelationships of the variables. This research compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value of 82% in the KNN method with the confusion matrix test, precision, accuracy, re-call, and fi-score. These results can be applied to real case studies of heart disease
Identifikasi Penyakit Jantung Menggunakan Machine Learning: Studi Komparatif sintiya, endah septa; Rizdania, Rizdania; Afrah, Ashri Shabrina
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.7144

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns approaching heart disease. This study compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value in the KNN method with precision, accuracy, re-call, fi-score tests. It is hoped that these results can be applied to real case studies of heart disease.
Co-Authors Afrah, Ashri Sabrina Alizunna, Defirra Ashri Shabrina Afrah Ayubi, Muhammad Nofa Burhan, M.Shochibul Endah Septa Sintiya Fitriyah, Rizqi Sofi Nur Frihandhika Permana Frihandhika Permana Hadiatulloh, Dimas Rega Henny May Indahsari Herman Tolle Ikawati, Deasy Sandhya Elya Izzati, Afifah Nurul Kusmiyati Muladi, Muhammad Nasikhul Muniroh, Amirotul Nasiruddin, Niswatun Khasanah Permana, Frihandhika Pramudhita, Agung Puji Utami Rakhmawati Rani Darmayanti Rasyidah Nur Aisyah Riskitasari, Septyana Sapto Hadi Riono Sherina Izzaty Sherina Izzaty Sintya, Endah Septa Sumantri Syahnaryanti, Dinda Mareta Utaminingrum, Fitri Wayan Firdaus Mahmudy