Student Graduation Value Analysis Based On External Factors With C4.5 Algorithm - Fristi Riandari; Hengki Tamando Sihotang; Rohit Gautama; Sethu Ramen (Jurnal Mantik, 2022)
Data mining is the process of extracting data into information that has not previously been conveyed, with the right techniques the data mining process will provide optimal results. Data Mining is divided into several methods. Data classification is a process of finding the same properties in a set of objects in a database and classifying them into different classes according to the defined classification model. The purpose of classification is to find a model from the training set that distinguishes attributes into the appropriate category or class, the model is then used to classify attributes whose class has not been previously known. The classification technique is divided into several techniques, one of which is the Decision Tree. One of the existing approaches in the classification technique is the C4.5 algorithm. The C4.5 algorithm is an approach in data mining classification techniques that can predict students' final grades. The variables used in analyzing the passing grades will be classified based on their attributes. The C4.5 algorithm with the decision tree method can provide predictive rule information to describe the process associated with analyzing student passing grades. The characteristics of the classified data can be obtained clearly, both in the form of a decision tree structure and rules so that in the testing phase the RapidMiner software can help predict student passing grades. With the formation of rules that can become new information that can be used as a tool in analyzing student passing grades.