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All Journal Jurnal Transformatika Mobile and Forensics
Pramudhita, Agung
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Neuroscience

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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
Co-Authors Ashri Shabrina Afrah Deddy Kusbianto Eka Apriani, Meyti Endah Septa Sintiya Maskuri, Rahmad Alfian Nurhalimah Ratsanjani, M.Hasyim Rawansyah, Rawansyah Rizdania, Rizdania