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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ismail, Nurlaila
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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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.
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.
Robust k-NN approach for classifying Aquilaria oil species by compounds Ahmad Sabri, Noor Aida Syakira; Syafiqah Noramli, Nur Athirah; Nik Kamaruzaman, Nik Fasha Edora; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp178-189

Abstract

Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions.
Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques Syafiqah Noramli, Nur Athirah; Ahmad Sabri, Noor Aida Syakira; Roslan, Muhammad Ikhsan; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp167-177

Abstract

Agarwood, a prized non-timber resource from the Aquilaria genus, is highly valued for its aromatic and medicinal properties, playing a significant role in the healthcare, fragrance, and pharmaceutical industries. This research analyzes essential oils from four Aquilaria species-A. beccariana, A. malaccensis, A. crassna, and A. subintegra-using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). The primary objective is to optimize classification efficiency by reducing computational time and reducing multicollinearity through feature selection. Pearson correlation analysis revealed strong relationships among six chemical compounds-β-selinene (A), dihydro-β-agarofuran (B), δguaiene (C), 10-epi-γ-eudesmol (D), γ-eudesmol (E), and pentadecanoic acid (F). Through feature selection, the three most significant compoundsdihydro-β-agarofuran (B), γ-eudesmol (D), and 10-epi-γ-eudesmol (E)-were identified, achieving a remarkable 90.02% reduction in computational time (from 0.0403 to 0.0040 seconds). These findings highlight the effectiveness of structured feature selection in refining essential oil profiling and enhancing species classification accuracy. Future research directions include exploring machine learning-based feature selection techniques to further streamline feature reduction processes and expand the scope of essential oil authentication. This study contributes to advancing the scientific understanding and practical utilization of agarwood essential oils, paving the way for more efficient and reliable analytical frameworks.
A ten-year retrospective (2014-2024): Bibliometric insights into the study of internet of things in engineering education Yusoff, Zakiah Mohd; Nordin, Siti Aminah; Othman, Norhalida; Bakar, Zahari Abu; Ismail, Nurlaila
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4213-4226

Abstract

This article presents a comprehensive ten-year retrospective analysis (2014-2024) of the evolving landscape of internet of things (IoT) studies within engineering education, employing bibliometric insights. The pervasive influence of IoT technologies across diverse domains, including education, underscores the significance of examining its trajectory in engineering education research over the past decade. Recognizing the dynamic nature of this intersection is crucial for educators, researchers, and policymakers to adapt educational strategies to IoT-induced technological shifts. Addressing this imperative, the study conducts a detailed bibliometric review to identify gaps, trends, and areas necessitating further exploration. Methodologically, the study follows a framework involving a comprehensive search of Scopus and Web of Science databases to identify relevant articles. Selected articles undergo bibliometric analysis using the Biblioshiny tool, supplemented by manual verification and additional analysis in Excel. This approach facilitates robust evaluation of citation patterns, co-authorship networks, keyword trends, and publication patterns over the specified timeframe. Anticipated outcomes include the identification of seminal works, key contributors, influential journals, and science mapping. The study aims to unveil emerging themes, track research trends, and provide insights into collaborative networks shaping IoT discourse in engineering education. This analysis offers a roadmap for future research directions, guiding educators and researchers toward fruitful avenues of exploration.
Application of self-organizing map for modeling the Aquilaria malaccensis oil using chemical compound Che Hassan, Mohammad Arif Fahmi; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2889-2898

Abstract

Agarwood oil, known as ‘black gold’ or the ‘wood of God,’ is a globally prized essential oil derived naturally from the Aquilaria tree. Despite its significance, the current non-standardized grading system varies worldwide, relying on subjective assessments. This paper addresses the need for a consistent classification model by presenting an overview of Aquilaria malaccensis oil quality using the self-organizing map (SOM) algorithm. Derived from the Thymelaeaceae family, Aquilaria malaccensis is a primary source of agarwood trees in the Malay Archipelago. Agarwood oil extraction involves traditional methods like solvent extraction and hydro-distillation, yielding a complex mixture of chromone derivatives, oxygenated sesquiterpenes, and sesquiterpene hydrocarbons. This study categorizes agarwood oil into high and low grades based on chemical compounds, utilizing the SOM algorithm with inputs of three specific compounds: β-agarofuran, α-agarofuran, and 10-epi-φ-eudesmol. Findings demonstrate the efficacy of SOM-based quality grading in distinguishing agarwood oil grades, offering a significant contribution to the field. The non-standardized grading system's inefficiency and subjectivity underscore the necessity for a standardized model, making this research crucial for the agarwood industry's advancement.
Optimizing nonlinear autoregressive with exogenous inputs network architecture for agarwood oil quality assessment Roslan, Muhammad Ikhsan; Ahmad Sabri, Noor Aida Syakira; Noramli, Nur Athirah Syafiqah; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3493-3502

Abstract

Agarwood oil is highly valued in perfumes, incense, and traditional medicine. However, the lack of standardized grading methods poses challenges for consistent quality assessment. This study proposes a data-driven classification approach using the nonlinear autoregressive with exogenous inputs (NARX) model, implemented in MATLAB R2020a with the Levenberg-Marquardt (LM) algorithm. The dataset, sourced from the Universiti Malaysia Pahang Al-Sultan Abdullah under the Bio Aromatic Research Centre of Excellence (BARCE) and Forest Research Institute Malaysia (FRIM), comprises chemical compound data used for model training and validation. To optimize model performance, the number of hidden neurons is systematically adjusted. Model evaluation uses performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), epochs, accuracy, and model validation. Results show that the NARX model effectively classifies agarwood oil into four quality grades which is high, medium-high, medium-low, and low. The best performance is achieved with three hidden neurons, offering a balance between accuracy and computational efficiency. This work demonstrates the potential of automated, standardized agarwood oil quality grading. Future research should explore alternative training algorithms and larger datasets to further enhance model robustness and generalizability.
Co-Authors Abdullah, Hasnain Ahmad Sabri, Noor Aida Syakira Ahmad, Azizah Al-Hadi, Anis Hazirah ‘Izzati Hasnu Almisreb, Ali Abd Bakar, Zahari Abu Che Hassan, Mohammad Arif Fahmi Haron, Mohamad Hushni Haron, Mohamad Hushnie Hasnu Al-Hadi, Anis Hazirah 'Izzati Huzir, Siti Mariatul Hazwa Mohd Mohamad Noor, Norhayati Mohd Huzir, Siti Mariatul Hazwa Mohd Kasim, Linda Mohd Nasir Taib Mohd Yusoff, Zakiah Nik Kamaruzaman, Nik Fasha Edora Noramli, Nur Athirah Syafiqah Nordin, Siti Aminah Othman, Norhalida Roslan, Muhammad Ikhsan Syafiqah Noramli, Nur Athirah Taib, Haji Mohd Nasir Tajuddin, Saiful Nizam Yassin, Ahmad Ihsan Mohd Yusoff, Zakiah Mohd Zaidi, Amir Hussairi