Open AccessPSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5
Author(s) -
Ismail Yusuf Panessai,
Muhammad Modi Lakulu,
Mohd Hishamuddin Abdul Rahman,
Noor Anida Zaria Mohd Noor,
Nor Syazwani Mat Salleh,
Aldrin Aran Bilong
Publication year - 2019
Publication title -
international journal of artificial intelligence (batam)/international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2686-3251
pISSN - 2407-7275
DOI - 10.36079/lamintang.ijai-0602.42
Subject(s) - respondent , computer science , confusion matrix , naive bayes classifier , machine learning , classifier (uml) , decision tree , artificial intelligence , id3 , confusion , web application , data mining , decision tree learning , support vector machine , world wide web , psychology , political science , law , psychoanalysis
PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5
This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model.