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Nurul Fatihah Sahidan
Universiti Teknologi MARA

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Web-based problem-based collaborative learning for converted muslim in learning Islamic knowledge Nurul Fatihah Sahidan; Nazrul Azha Mohamed Shaari; Hayati Abd Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp516-523

Abstract

Web-based collaborative learning is well accepted learning methods since it provides flexibilities not only in term of learners’ interaction but also its timeliness learning materials. Even though there are many websites offer learning materials for Muslims generally but for new converted Muslims getting authentic materials and proper guidance are complicated. Hence, a web-based problem-based collaborative learning is proposed. This method integrates an elected virtual instructor to facilitate and support the new converted Muslims’ cognitive and emotional activities, develop their confidence of resolving and dealing with problems they are facing. This paper relates and analyzes the problem-based collaborative learning in terms of the functionality and user acceptance. Based on a survey conducted, it shows that 85% of the respondents found the system very useful and easy to use while 80%found that the system met their requirements. This system hypothetically able to help, and ease new converted Muslims in exploring Islamic authentic knowledge by using problem-based technique in web-based collaborative learning.
Evaluation of basic convolutional neural network and bag of features for leaf recognition Nurul Fatihah Sahidan; Ahmad Khairi Juha; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp327-332

Abstract

This paper presents the evaluation of basic Convolutional Neural Network (CNN) and Bag of Features (BoF) for Leaf Recognition. In this study, the performance of basic CNN and BoF for leaf recognition using a publicly available dataset called Folio dataset has been investigated. CNN has proven its powerful feature representation power in computer vision. The same goes with BoF where it has set new performance standards on popular image classification benchmarks and has achieved scalability breakthrough in image retrieval. The feature that is being utilized in the BoF is Speeded-Up Robust Feature (SURF) texture feature. The experimental results indicate that BoF achieves better accuracy compared to basic CNN.
Co-Authors Ahmad Khairi Juha Hayati Abd Rahman Nazrul Azha Mohamed Shaari Zaidah Ibrahim