1Department of Electronics, Universitas Muhammadiyah Malang, Indonesia
2Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia
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@article{JTSISKOM13619, author = {Merinda Lestandy and Lailis Syafa'ah and Amrul Faruq}, title = {Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {8}, number = {3}, year = {2020}, keywords = {potential blood donor; KNN; Naïve Bayes; donors classification}, 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.}, issn = {2338-0403}, pages = {217--221} doi = {10.14710/jtsiskom.2020.13619}, url = {https://jtsiskom.undip.ac.id/article/view/13619} }
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