Data classification algorithm in machine
learning is very helpful in analyzing a number of medical
data with a large size and helps in making decisions to
diagnose a disease. Not all supervised classification
algorithms get accurate results in analyzing data sets. For
this reason, testing the accuracy of each supervised
classification algorithm is necessary, this can be used as a
comparison in determining which types of algorithms are
most accurate in measuring small amounts of data, and
which algorithms are the most accurate in measuring large
amounts of data. In this paper we will examine several
classification algorithms including Naïve Bayes algorithms,
functions (Support Vector Classifier algorithms), rules
(decision table algorithms), trees (J48) by looking at the
results of measurements made by each algorithm with
measurement variables, which are Correctly Classified,
incorrect classifieds, Precision, and Recall. The purpose of
the study was to find the weaknesses and strengths of the
supervised classification algorithm based on the
measurement variables that have been determined against
the testing of predictive databases of diabetes. Based on the results in this study, the best algorithm that can be used to help make a decision to diagnose a disease is the SVM algorithm with an accuracy value of 77.3%.