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Lailis Syafa'ah
Department of Electrical Engineering, Universitas Muhammadiyah Malang
Computer Science & IT Electrical & Electronics Engineering

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Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin Merinda Lestandy; Lailis Syafa'ah; Amrul Faruq
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (10.282 KB) | DOI: 10.14710/jtsiskom.2020.13619

Abstract

Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.
Co-Authors Amrul Faruq Merinda Lestandy