Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin

Classification of potential blood donors using machine learning algorithms approach

*Merinda Lestandy  -  Department of Electronics, Universitas Muhammadiyah Malang, Indonesia
Lailis Syafa'ah orcid scopus  -  Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia
Amrul Faruq orcid scopus  -  Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia
Received: 7 Jan 2020; Revised: 21 Apr 2020; Accepted: 24 Apr 2020; Published: 31 Jul 2020; Available online: 25 Apr 2020.
DOI: https://doi.org/10.14710/jtsiskom.2020.13619 View
Blood Transfusion Service Center Data Set
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License URL: http://creativecommons.org/licenses/by-sa/4.0

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Section: Original Research Articles
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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.

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Keywords: potential blood donor; KNN; Naïve Bayes; donors classification

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