COMPARISON BETWEEN KNN ALGORITHM AND RANDOM FOREST ALGORITHM TO PREDICT STUDENT GRADUATION
Daftar Isi:
- This study aims to compare the results of the accuracy of the KNN Algorithm and the Random Forest Algorithm based on the classification of student data from the student achievement index and student graduation status. The algorithms used in data classification are KNN (K-Nearest Neighbor) and Random Forest. The data used in this study are dummy data. For data processing, the KNN algorithm uses a split data method. Meanwhile, for data processing the Random Forest Algorithm uses car k folds cross validation split. To get the best k value in the KNN Algorithm using the brute force search method. For testing using a lot of training data and testing data used. In the Knn algorithm, the aim is to find the closest neighbors with the tested data. While the Random Forest algorithm is based on data samples using a random variable in the formed decision tree. The amount of data can affect the resulting accuracy value. The speed of classification of the two algorithms is influenced by the amount of data used. Each algorithm also calculates accuracy, precision, recall and f1 score