An Approach to Classify Eligibility Blood Donors Using Decision Tree and Naive Bayes Classifier

Abstract

Blood donation is a process of taking blood from a person voluntarily to be stored in a blood bank for later use in blood transfusions. There are several criteria must be fulfilled by someone who want to be a blood donors as follow blood type, gender, age, blood pressure, hemoglobin, etc. Those criteria is process manually in order to classify eligibility of blood donors. However, those process frequently repeated and waste too much time. This work proposed a classification model to decrease time process using both decision tree and naive bayes classifier. In evaluation phase, both algorithm will compare by its accuration and performance. As the result, we obtained that decision tree has exactly 66,65% accuration value and 79,95% for naive bayes classifier. The other testing that applied 100 data testing dan 400 data training. We obtained that decision tree has exactly 78,5% and naive bayes classifier has 81,5%.