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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Abdullah Sani Abd Rahman
Universiti Teknologi PETRONAS
Computer Science & IT Electrical & Electronics Engineering Engineering

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Green building factor in machine learning based condominium price prediction Suraya Masrom; Thuraiya Mohd; Abdullah Sani Abd Rahman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp291-299

Abstract

The negative impact of massive urban development promotes the inclusion of green building aspects in the real estate and property industries. Green building is generally defined as an environmentally friendly building, which rapidly emerged as a national priority in many countries. Acknowledging the benefits of green building, Green Certificate and Green Building Index (GBI) has been used as one of the factors in housing prices valuation. To predict a housing price, a robust approach is crucial, which can be effectively gained from the machine learning technique. As research on green building with machine learning techniques is rarely reported in the literature, this paper presents the fundamental design and the comparison results of three machine learning algorithms namely deep learning (DL), decision tree (DT), and random forest (RF). Besides the performance comparisons, this paper presents the specific weight correlation in each of the machine learning models to describe the importance of the green building to the model. The results indicated that RF has been outperformed others while Green Certificate and GBI have only been slightly important in the DL model.
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms Suraya Masrom; Rahayu Abdul Rahman; Masurah Mohamad; Abdullah Sani Abd Rahman; Norhayati Baharun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1153-1163

Abstract

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.
Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning Suraya Masrom; Rahayu Abdul Rahman; Norhayati Baharun; Syed Redzwan Sayed Rohani; Abdullah Sani Abd Rahman
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5037

Abstract

Nowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM). The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.
Co-Authors Masurah Mohamad Norhayati Baharun Rahayu Abdul Rahman Suraya Masrom Syed Redzwan Sayed Rohani Thuraiya Mohd