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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Nasir Taib
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering Engineering

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Student performance classification: a comparison of feature selection methods based on online learning activities Alias, Muhamad Aqif Hadi; Abdul Aziz, Mohd Azri; Hambali, Najidah; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4675-4685

Abstract

The classification of student performance involves categorizing students' performance using input data such as demographic information and examination results. However, our study introduces a novel approach by emphasizing students' online learning activities as a rich data source. To avoid misinterpretation during the classification, we therefore presented a study comparing several feature selection (FS) methods combined with artificial neural network (ANN), for classifying students’ performance based on their online learning activities. At first, we focused on tackling the issue of missing values by implementing data cleaning using variance threshold. Feature selection techniques were implemented which encompass both filter-based (information gain, chi-square, Pearson correlation) and wrapper-based, sequential selection (forward and backward) techniques. In the classification stage, multi-layer perceptron (MLP) was used with the default hyperparameters and 5-fold cross-validation along with synthetic minority oversampling technique (SMOTE) were also applied to each method. We evaluated each feature selection method's performance using key metrics: accuracy, precision, recall, and F1-score. The outcomes highlighted information gain and sequential selection (forward and backward) as the top-performing methods, all achieving 100% accuracy. This research underscores the potential of leveraging online learning activities for robust student performance classification within the specified constraints.
Development of machine learning algorithms in student performance classification based on online learning activities Alias, Muhamad Aqif Hadi; Aziz, Mohd Azri Abdul; Hambali, Najidah; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7126-7136

Abstract

The field of educational data mining has gained significant traction for its pivotal role in assessing students' academic achievements. However, to ensure the compatibility of algorithms with the selected dataset, it is imperative for a comprehensive analysis of the algorithms to be done. This study delved into the development of machine learning algorithms utilizing students' online learning activities to effectively classify their academic performance. In the data cleaning stage, we employed VarianceThreshold for discarding features that have all zeros. Feature selection and oversampling techniques were integrated into the data preprocessing, using information gain to facilitate efficient feature selection and synthetic minority oversampling technique (SMOTE) to address class imbalance. In the classification phase, three supervised machine learning algorithms: k-nearest neighbors (KNN), multi-layer perceptron (MLP), and logistic regression (LR) were implemented, with 3-fold cross-validation to enhance robustness. Classifiers’ performance underwent refinement through hyperparameter tuning via GridSearchCV. Evaluation metrics, encompassing accuracy, precision, recall, and F1-score, were meticulously measured for each classifier. Notably, the study revealed that both MLP and LR achieved impeccable scores of 100% across all metrics, while KNN exhibited a noticeable performance boost after using hyperparameter tuning.
Statistical analysis for chemical compound based on several species of aquilaria essential oil Ahmad Sabri, Noor Aida Syakira; Nik Kamaruzaman, Nik Fasha Edora; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3663-3673

Abstract

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.
Feature selection techniques and classification algorithms for student performance classification: a review Alias, Muhamad Aqif Hadi; Hambali, Najidah; Abdul Aziz, Mohd Azri; Taib, Mohd Nasir; Jailani, Rozita
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3230-3243

Abstract

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
Agarwood oil quality identification using artificial neural network modelling for five grades Mohd Huzir, Siti Mariatul Hazwa; Tajuddin, Saiful Nizam; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Almisreb, Ali Abd; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2254-2261

Abstract

Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.
Pattern analysis on Aquilaria Malaccensis using machine learning Hasnu Al-Hadi, Anis Hazirah 'Izzati; Mohd Huzir, Siti Mariatul Hazwa; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushnie; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5562

Abstract

Aquilaria Malaccensis was found to generate agarwood. Because of its multiple benefits, agarwood essential oil, sometimes known as “black gold” is highly regarded universally. There is currently no accepted method for classifying various grades of agarwood essential oil. Due to the fact that the agarwood essential oil is assessed using a human sensory panel, the existing grading method is ineffective. Since different people may have different viewpoints on how to grade agarwood essential oil, it is not practical to apply the method universally. Several innovative methods for determining the classification of agarwood essential oil have been proposed and put into practise as a result of advanced technology. The study has constructed a pattern analysis on different grades of agarwood essential oil using 2D scatter plot. The results successfully indicate the scatter plots are scattered groupedly.
Pre-processing technique of Aquilaria species from Malaysia for four different qualities Mohd Huzir, Siti Mariatul Hazwa; Hasnu Al-Hadi, Anis Hazirah 'Izzati; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushni; Almisreb, Ali Abd; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5577

Abstract

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
Artificial neural networks classification of s-band absorption performance in eco-friendly microwave absorbers Ahmad, Azizah; Taib, Mohd Nasir; Abdullah, Hasnain; Ismail, Nurlaila; Yassin, Ahmad Ihsan Mohd; Mohd Kasim, Linda; Mohamad Noor, Norhayati
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1007-1014

Abstract

Microwave absorbers are essential for applications such as radar stealth and electromagnetic compatibility. Nevertheless, traditional materials encounter obstacles related to cost and sustainability, which has led to the exploration of new options such as materials derived from agricultural waste. This study focuses on the classification challenge of evaluating the absorption performance of eco-friendly microwave absorbers in the S-band (2 to 4 GHz) frequency. Three multilayer perceptron (MLP) algorithms, namely levenberg marquardt (LM), resilient backpropagation (RBP) and scale conjugate gradient (SCG) are assessed for classification accuracy. The dataset consists of 135 absorption performance values of microwave absorbers that were taken from experimental measurements using the naval research laboratory (NRL) arch free. The MLP algorithms will be divided into three divisions, which are training, validation and testing, evaluating important criteria such as accuracy, precision, sensitivity and specificity. The performance of three types of algorithms will be compared using two basic inputs: the absorption values and the single slot sizes. The RBP algorithm achieved 100% accuracy, and a lower mean squared error (MSE) of 0.02500 compared to the LM and SCG. This study provides valuable insights for designing better microwave absorbers and highlights the commercial potential of agricultural waste materials in such applications.
Pyramidal microwave absorbers: leveraging ceramic materials for improved electromagnetic interference shielding Rosli, Nur Shafikah; Abdullah, Hasnain; Kasim, Linda Mohd; Abdullah, Samihah; Taib, Mohd Nasir; Kasim, Shafaq Mardhiyana Mohamat; Noor, Norhayati Mohd; Ahmad, Azizah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp435-447

Abstract

This study presents the development and optimization of pyramidal microwave absorbers designed for efficient electromagnetic interference (EMI) reduction in anechoic chambers. Based on prior research, this work transitions from conventional flat cement-carbon absorbers to a novel pyramidal design, incorporating silicon carbide (SiC) as ceramic materials. Introducing ceramic materials into the cement-carbon composite aims to enhance absorption across a broader frequency range while maintaining structural integrity. The study evaluates five sets of pyramidal absorbers with varying SiC content within the 1–12 GHz frequency range. Reflectivity performance was assessed using the naval research laboratory (NRL) Arch free space method at a 0° incidence angle. Among the tested absorbers, the set containing 10% SiC demonstrated superior performance, achieving minimum and maximum reflectivity values of -26.6215 and -55.2752 dB, respectively, particularly in the C-band. The findings highlight the significant impact of material composition and porosity on the absorber's effectiveness, providing valuable insights for the future design of high-performance EMI absorbers.
Investigation of microwave absorption performance of anti-radiation plastic cement brick Kasim, Linda Mohd; Idris, Hasnain Abdullah; Taib, Mohd Nasir; Noor, Norhayati Mohamad; Ahmad, Azizah; Ismail, Noor Azila; Kasim, Nazirah Mohamat; Anuar, Nur Qaisarah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2900-2910

Abstract

The increasing demand for effective anti-microwave radiation materials motivates the exploration of sustainable and eco-friendly alternatives. This research investigates the microwave absorption properties of various brick compositions, including commercial brick (CB) and solid bricks (SB1, SB2 and SB3) incorporating recycled materials, polyethylene terephthalate (PET) and palm oil fuel ash (POFA). The dimension of the developed brick is 200×100×60 mm (length×width×height). The absorption performance of the bricks was measured in 100 mm and 60 mm thickness across the frequency range of 1 to 12 GHz using the naval research laboratory (NRL) free space arch method. At 100 mm thickness, SB3 shows the highest absorption up to-32.2061 dB at 1.98 GHz. At 60 mm thickness, SB1 achieved the maximum absorption at -57.6511 dB at 2.505 GHz. SB2 shows consistent average absorption performance at 15.2064 dB at 100 mm thickness and -19.5 dB at 60 mm thickness respectively. The compressive strength of the brick was measured, and it was shown that SB2 exhibited the highest average compressive strength of 7.17 MPa. Considering the standard wall thickness and brick strength, SB2 shows the most effective performance due to its enhanced composition and consistent performance across frequencies.
Co-Authors Abdul Aziz, Mohd Azri Abdullah, Hasnain Abdullah, Samihah Ahmad Sabri, Noor Aida Syakira Ahmad, Azizah Alias, Muhamad Aqif Hadi Almisreb, Ali Abd Anuar, Nur Qaisarah Aziz, Mohd Azri Abdul Che Hassan, Mohammad Arif Fahmi Hambali, Najidah Haron, Mohamad Hushni Haron, Mohamad Hushnie Hasnu Al-Hadi, Anis Hazirah 'Izzati Idris, Hasnain Abdullah Ismail, Noor Azila Ismail, Nurlaila Kasim, Linda Mohd Kasim, Nazirah Mohamat Kasim, Shafaq Mardhiyana Mohamat Mohamad Noor, Norhayati Mohd Huzir, Siti Mariatul Hazwa Mohd Kasim, Linda Mohd Yusoff, Zakiah Nik Kamaruzaman, Nik Fasha Edora Noor, Norhayati Mohamad Noor, Norhayati Mohd Noramli, Nur Athirah Syafiqah Roslan, Muhammad Ikhsan Rosli, Nur Shafikah Rozita Jailani, Rozita Syafiqah Noramli, Nur Athirah Tajuddin, Saiful Nizam Yassin, Ahmad Ihsan Mohd Yusoff, Zakiah Mohd Zaidi, Amir Hussairi