Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Krea-TIF: Jurnal Teknik Informatika
Laksono, Panji
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : TEKNIK INFORMATIKA

 1 
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 Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.28652

Abstract

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.
Co-Authors Fauzi, Muhammad Ridho Fitria Rachmawati Jejen Jaenudin Novita Br Ginting Pratama, Risky Adit Puspa Eosina