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Yusram, S.Pd., M.Pd
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International Journal of Artificial Intelligence
Published by Lamintang Education & Training Centre
ISSN : 24077275     EISSN : 26863251     DOI : https://doi.org/10.36079/lamintang.ijai
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
The aim is to publish high-quality articles dedicated to Artificial Intelligence. IJAI published in biannual, and in Indonesian, Malay and English.
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "Vol 6 No 2 (2019)" : 3 Documents clear
Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner's El-Kaber Hachem; Moulay Hachem Harouni Alaou
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-0602.39

Abstract

As part of our project which aims at the realization of a system named ASTEMOI. In this article, we display a new and productive facial image representation based on the Local Sensitive Hash (LSH). This technique makes it possible to recognize the learners who follow their training in our learning platform. Once recognized, the student must be oriented towards an appropriate profile that takes into account his strengths and weaknesses. We also use a light processing module on the client device with a compact code so that we don’t need a lot of bandwidth, a lot of network transmission capacity to send the feature over the network, and to be able to index many pictures in a huge database in the cloud.
Optimizing K-Means Initial Number of Cluster Based Heuristic Approach: Literature Review Analysis Perspective Harunur Rosyid; Ramlah Mailok; Muhammad Modi Lakulu
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-0602.40

Abstract

One popular clustering technique - the K-means widely use in educational scope to clustering and mapping document, data, and user performance in skill. K-means clustering is one of the classical and most widely used clustering algorithms shows its efficiency in many traditional applications its defect appears obviously when the data set to become much more complicated. Based on some research on K-means algorithm shows that Number of a cluster of K-means cannot easily be specified in much real-world application, several algorithms requiring the number of cluster as a parameter cannot be effectively employed. The aim of this paper describes the perspective K-means problems underlying research. Literature analysis of previous studies suggesting that selection of the number of clusters randomly cause problems such as suitable producing globular cluster, less efficient if as the number of cluster grow K-means clustering becomes untenable. From those literature reviews, the heuristic optimization will be approached to solve an initial number of cluster randomly.
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.

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