Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5: Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5 - Lili Dwi Yulianto; Agung Triayudi; Ira Diana Sholihati (Jurnal Mantik, 2020)
Data Mining is very useful and widely applied in various fields, one of which is in the field of education. Data mining can be applied to the field of Educational institutions and is also often referred to as Educational Data Mining (EDM). Colleges currently compete with each other to improve the quality of their education for the creation of competent and high-integrity human resources. The amount of data contained in Higher Education can be utilized for the need for useful information so that the data attributes can be known so that the data is analyzed to improve student performance and achievement. And also the results of the analysis are expected to be able to anticipate the problem of delays in the study period that is often experienced by students. In this study conducted using two algorithm models namely K-Nearest Neighbor and Decision Tree C4.5. The best accuracy value is the K-Nearest Neighbor algorithm model with an accuracy rate of 59.32%, whereas in the Decision Tree C4.5 model the accuracy rate is 54.80%, the application of EDM and is expected to be maximized and developed so that it can contribute and develop in education world especially in data mining