Teknik Data Mining menggunakan Algoritma Decision Tree (C4.5) untuk Prediksi Seleksi Beasiswa Jalur KIP pada Universitas Muhammadiyah Kotabumi

Abstract

Smart Indonesia Card Scholarship (KIP) is an educational scholarship provided by the government for equivalent SMA/SMK/MA alumni who have good academic potential and wish to continue their studies to a higher level. KIP lectures themselves are distributed by the government through the Ministry of Education and Culture to universities in their implementation. Universitas Muhammadiyah Kotabumi (UMKO) is one of the universities that opens registration for university admission through KIP scholarships. Based on the data, it is known that there are 210 applicants and from the results of the selection of participants who registered for the KIP selection, there were 204 prospective students who took the test process. So far, in determining the scoring data, the results have not referred to and utilized the previous year's data. While the data can be used as a reference as a source of knowledge data. Therefore, a technique is needed to describe the data more concisely and quickly. Data mining is a technique that can be used to describe a series of processes to obtain knowledge or a pattern from a data set. Decision Tree Algorithm (C4.5) is one of the data mining algorithms that can be used for data classification to help solve classification problems. Based on the results of the research on the implementation of data mining using the decision tree algorithm (C4.5), an accuracy value of 100% was obtained. The accuracy test was also carried out using the Naive Bayes algorithm to obtain a comparison of the levels of accuracy. Based on the accuracy test of the two algorithms, data obtained with an accuracy level of 100% on the Decision Tree (C4.5) algorithm and 90.16% on the Naive bayes algorithm. It can be concluded that the accuracy of the Decision Tree (C4.5) algorithm for predicting prospective students receiving KIP scholarships is better than the Naive Bayes algorithm.