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All Journal International Journal of Artificial Intelligence International Journal of Multimedia and Recent Innovation (IJMARI)
Aldrin Aran Bilong
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Computer Science & IT Decision Sciences, Operations Research & Management

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PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5 Ismail Yusuf Panessai; Muhammad Modi Lakulu; Mohd Hishamuddin Abdul Rahman; Noor Anida Zaria Mohd Noor; Nor Syazwani Mat Salleh; Aldrin Aran Bilong
International Journal of Artificial Intelligence Vol 6 No 2 (2019)
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-0602.42

Abstract

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.
Learning Logic Gates Using Augmented Reality Nurul Hidayah Rahani; Aldrin Aran Bilong; Muhammad Rafiq Mat Suruji; Ismail Yusuf Panessai
International Journal of Multimedia and Recent Innovation Vol 2 No 1 (2020)
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijmari-0201.81

Abstract

This research is important to enhance students understanding and learning performance in Computer Organization and Architecture subject especially in logic gate circuit topic as student also can improve their knowledge and usage in new technology like AR. The appropriate methodology chosen in development of this research is ADDIE model which is consists of five phases. The application were evaluated by the student who take Computer Organization and Architecture subject in Sultan Idris Education University. By using ADDIE model as its methodology, the planning of this mobile application was successfully proceeding smoothly starting from the analysis phase until evaluation phase. The details gained from the phase of evaluation will be explained through the next chapter of this research. Overall from this research, the AR-LoGates Apps has shown that it is a necessary product by receiving a good reception from students. The results proved that AR-LoGates application can help students in learning especially in easy to understanding the logic gate topic and make the learning become interesting way.
Co-Authors Ismail Yusuf Panessai Mohd Hishamuddin Abdul Rahman Muhammad Modi Lakulu Muhammad Rafiq Mat Suruji Noor Anida Zaria Mohd Noor Nor Syazwani Mat Salleh Nurul Hidayah Rahani