Prediksi Kelulusan Mata Kuliah Mahasiswa Teknologi Informasi Menggunakan Algoritma K-Nearest Neighbor
Abstract
This research aims to develop a predictive model using the K-Nearest Neighbor (KNN) method to forecast the course completion of students in the Information Technology program. The issue at hand is the uncertainty in predicting student success based on historical data and specific attributes. This study focuses on the importance of understanding the factors that influence student success in the Database Management Systems course to provide accurate predictions and help improve student pass rates in this course. The objective of this research is to build a predictive model using the KNN algorithm and to implement this model using the PHP programming language. The study aims to offer valuable insights for educational institutions to enhance teaching and learning processes and to expand understanding of data mining concepts in specific case studies. The prediction aims to determine whether a student will pass or fail the Database Management Systems course based on predetermined training and testing data. By calculating the nearest distance between the training data and the test data. The results showed an accuracy rate of 90% for predicting course completion using k=5, with a dataset consisting of 40 training data points and 20 testing data points.
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