Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison
Abstract
Until now, many colleges are running to improve the quality of education to create a competitive environment. The wealth of data contained in the college can be put to good use according to the needs and processed into useful information to find out the relationship between the attributes of the data contained in it for analysis and the expected result in the form study achievements are related to study time, i.e. in adequate or late in the probable study period can be classified. Data mining, which refers to the analysis of data in the field of educational institutions, is also known as educational data mining (EDM). In the study conducted using two models of Naive Bayes Classifier i.e. Algorithms and C 4.5. The value of best accuracy in the Naive Bayes Classifier (NBC) algorithm model was 86.83% with a ratio of 80% training data, while in the model algorithm C 4.5 was 88.10% with a ratio of 90% training data. The application of EDM is expected to be maximized and developed so that it can contribute to the world of education and advance, especially in the field of data mining.
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