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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Shihab Hamad Khaleefah
Faculty of Computer Science, Al Maarif University College
Computer Science & IT Engineering

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A general framework for selecting appropriate criteria of student as research assistant using fuzzy delphi method Sulaiman Abd Anter; Bahbibi Rahmatullah; Shihab Hamad Khaleefah; Khairul Fikri Tamrin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp839-846

Abstract

This research aims to build a general framework for choosing the most appropriate set of criteria for recruiting student as a research assistant in a university research project. University researchers could benefit from such a framework because it could optimize the costs of research while also enhancing students research skills. In the same time, it is also essential that the quality of research ought to measure up to the grants provided by the university. Nevertheless, it is a challenging problem for many research supervisors in the selection of qualified research assistants. In this paper, we attempted to resolve this problem by building a general framework for selecting the appropriate criteria in the evaluation of student performance. We explored earlier studies on the proposed evaluation criteria of the research assistant and identified 47 most impactful criteria criteria. We obtained experts in engineering and information technology fields from two universities to answer questionnaires to identify their commonly used criteria for grant research assistant (GRA). Then, all the identified criteria were evaluated using the fuzzy delphi method (FDM) for finding the best fitting criteria which resulted in 16 most impactful criteria.
Comparative analysis of classification techniques for leaves and land cover texture Azri Azrul Azmer; Norlida Hassan; Shihab Hamad Khaleefah; Salama A Mostafa; Azizul Azhar Ramli
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.706

Abstract

The texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.
Co-Authors Azizul Azhar Ramli Azri Azrul Azmer Bahbibi Rahmatullah Khairul Fikri Tamrin Norlida Hassan Salama A Mostafa Sulaiman Abd Anter