Abstract
Diabetes is a serious disease that occurs when the pancreas does not produce enough insulin as a hormone that regulates blood sugar in the body. This disease also has an impact on health. This research builds an Android-based application called Diabetic to help classify and provide information related to diabetes and analyze the performance of the Term Frequency Inverse Document Frequency method and the Naive Bayes algorithm. The Term Frequency Inverse Document Frequency method is a technique for calculating the presence of words in a collection of documents by creating document vectors. The Naive Bayes algorithm is an algorithm that uses probability to solve a classification case. This algorithm has an efficient and fast calculation. Based on this research, it is known that the Naive Bayes Algorithm produces an accuracy of 66% by taking a computation time of 39 seconds with a memory consumption of 80 to 351 mb.
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