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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohd Nasir Taib
Universiti Teknologi MARA (UiTM)

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Absorption performance of biomass hollow pyramidal microwave absorber using multi-slot array technique Mas Izzati Fazin; Ahmad Rashidy Razali; Mohd Nasir Taib; Norhayati Mohamad Noor; Linda Mohd Kasim; Nazirah Mohamat Kasim; Hasnain Abdullah Idris
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp895-902

Abstract

Electromagnetic interference (EMI) is an undesired electromagnetic (EM) wave by nearby electronic devices, process equipment, and measuring instruments. In this work, a novel multi-slot technique is applied to the hollow biomass pyramidal microwave absorber to study its absorption properties thoroughly. Two different slot arrangements in horizontal and vertical configuration are designed for the proposed microwave absorbers. Both slot design concepts have identical shape and size. This work aims to study, compare and analyze the absorption performance of the proposed designs at L, S, C and X frequency bands. The biomass material is used to form as absorbent material. The characteristics performance of the multi-slot design on biomass hollow pyramidal microwave absorbers are measured by using naval research laboratory (NRL) Arch space-free method. The frequency range set up for the measurement is in between 1 GHz to 12 GHz. The multi-vertical slots design exhibits better absorption performance at C-band and X-band which is -63.67 dB and -46.78 dB respectively while the multi-horizontal slots design provides better absorption performance at S-band which is -16.92 dB. The results shows that both design performances are frequency-dependent since horizontal slots design improve maximum absorption performance at low frequency while vertical slots design delivers better performance at high frequency. 
Quadratic tuned kernel parameter in Non-linear support vector machine (SVM) for agarwood oil compounds quality classification Muhamad Addin Akmal Bin Mohd Raif; Nurlaila Ismail; Nor Azah Mohd Ali; Mohd Hezri Fazalul Rahiman; Saiful Nizam Tajuddin; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1371-1376

Abstract

This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and the output is the quality of the oil either high or low. The input and output data were processed by following tasks; i) data processing which covers normalization, randomization and data splitting into two parts in which training and testing database (ratio of 80%:20%), and ii) data analysis which covers SVM development by tuning quadratic kernel parameter. The training dataset was used to be train the SVM model and the testing dataset was used to test the developed SVM model. All the analytical works are performed via MATLAB software version R2013a. The result showed that, quadratic tuned kernel parameter in SVM model was successful since it passed all the performance criteria’s in which accuracy, precision, confusion matrix, sensitivity and specificity. The finding obtained in this paper is vital to the agarwood oil and its research area especially to the agarwood oil compounds classification system.
The significance of artificial intelligent technique in classifying various grades of agarwood oil Aqib Fawwaz Mohd Amidon; Siti Mariatul Hazwa Mohd Huzir; Zakiah Mohd Yusoff; Nurlaila Ismail; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp261-269

Abstract

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.
Co-Authors Ahmad Rashidy Razali Aqib Fawwaz Mohd Amidon Hasnain Abdullah Idris Linda Mohd Kasim Mas Izzati Fazin Mohd Hezri Fazalul Rahiman Muhamad Addin Akmal Bin Mohd Raif Nazirah Mohamat Kasim Nor Azah Mohd Ali Norhayati Mohamad Noor Nurlaila Ismail Saiful Nizam Tajuddin Siti Mariatul Hazwa Mohd Huzir Zakiah Mohd Yusoff