Machine Learning for the Model Prediction of Final Semester Assessment (FSA) using the Multiple Linear Regression Method - Rachmawati, Fitria; Jaenudin, Jejen; Ginting, Novita Br; Laksono, Panji (JURNAL TEKNIK INFORMATIKA, 2024)
Corona virus (COVID-19) is the reason behind the collapse of the National Assembly. The first is the Final Semester Assessment (FSA) , which is a component of the student's graduation. The aforementioned evaluation process is a crucial consideration for the teacher since it uses several intricate surveys and mark components. A prediction model is employed to assist teachers in providing suitable results for student learning. The method that is used is called the multiple linear regression. This multiple linear regression algorithm yields an accuracy level of approximately 92%. The analysis results using the method are used as a guide to understanding student’s index. This index is a rating that appears based on the Minimum Credit Count (MCC). Therefore, the goal of this study is to determine students' understanding of the FSA prediction value, which will be taken into consideration through the results of the MCC weights in the form of a range in the form of "Grade." Additionally, the research aims to determine the accuracy of the results from the model obtained using multiple linear regression algorithms in predicting students' FSA.