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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Zakiah Mohd Yusoff
Universiti Teknologi MARA
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification Noratikah Zawani Mahabob; Zakiah Mohd Yusoff; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Mohd Nasir Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5505-5514

Abstract

Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.
k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market Erny Haslina Abd Latib; Nurlaila Ismail; Saiful Nizam Tajuddin; Jasmin Jamil; Zakiah Mohd Yusoff
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3158-3165

Abstract

Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil’s resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area.
SIW Circular Cavity Single Mode Filter With Triangle Probe Siti Aminah Nordin; Mohd Khairul Mohd Salleh; Zuhani Ismail Khan; Norfishah Ab Wahab; Latifah Noh; Zakiah Mohd Yusoff
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp672-676

Abstract

A novel substrate integrated waveguide (SIW) circular cavity using triangle probe are proposed in this paper. Prior to this research work, circular cavity resonator was used to achieve a miniaturization for the overall circuit size. The proposed filter provides single resonant mode, TE110. The resonant frequency of TE110 can be adjusted by varying the length and width of the SIW cavity. The proposed filter are designed to operate at frequency 3.75 GHz and implemented on Rogers 3210 substrate with thickness of 0.64 mm. The insertion loss in operating band is less than 0.6 dB and the return loss is better than 24 dB. Simulated result obtained using Ansoft HFSS software.
IOT-Based smart street lighting enhances energy conservation Zakiah Mohd Yusoff; Zuraida Muhammad; Mohd Syafiq Izwan Mohd Razi; Noor Fadzilah Razali; Muhd Hussaini Che Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp528-536

Abstract

The electricity generation cost is escalating every year while the electric energy is indispensable and increasing in demand. The resources for energy generation is also depleting due to the increasing demand of power. Thus, a system that can reduce the energy wastage and the massive expenses is essential. Street light system is one of the systems that can reduce energy consumption. The massive energy consumption from the current street light system is not efficient enough to reduce the wasted energy. By implementing an IOT-based smart street light system, the power consumption of the street light will be optimized. This system will also provide the ability to monitor input voltage for Arduino MEGA 2560 microcontroller and control the street light through IOT. The concept of this smart system is to introduce an intelligent system which can decide to switch on or off the street light according to the movement detection by using an infrared sensor module. The data will be sent to Arduino Mega 2560, which is a microcontroller that will decide to turn on or off the street light. The Wi-Fi module ESP-01 is implemented to enable the microcontroller to connect to Blynk software for monitoring and controlling purpose. The result shows that the smart street light system is expected to reduce energy consumption up to 45.48% on weekdays and 32.22% on weekends from the present street light system which uses timer system. The IOT-based Smart Street Light system also shows the condition of the street light system based on the Blynk interfaces for maintenance purpose.
The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification Noor Syafina Mahamad Jainalabidin; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
International Journal of Advances in Applied Sciences Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (789.447 KB) | DOI: 10.11591/ijaas.v11.i3.pp242-252

Abstract

Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically, the oil quality is classified by using physical properties (odor and color) and this technique has several problems: not consistent in term of accuracy. Thus, this study presented a new technique to classify the quality of agarwood oil based on chemical properties. The work focused on the k-nearest neighbor (k-NN) modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification. It involved of 96 samples of agarwood oil, data pre-processing (data randomization, data normalization, and data division to testing and training datasets) and the development of k-NN model. The training dataset is used to train the k-NN model, and the testing dataset is used to test the developed model. During the model development, Mahalanobis and correlation are varied in k-NN distance metric. The k-NN values are ranging from 1 to 10. Several performance criteria including resubstitution error (closs), cross-validation error (kloss) and accuracy were applied to measure the performance of the built k-NN model. All the analytical work was performed via MATLAB software version R2020a. The result showed that the accuracy of Mahalanobis distance metric has a better performance compared to correlation from k = 1 to k = 5 with the value of 100.00%. This finding is important as it proved the capabilities of k-NN modelling in classifying the agarwood oil quality. Not limited to that, it also contributed to the agarwood oil research area as well as its industry.
Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation Siti Mariatul Hazwa Mohd Huzir; Aqib Fawwaz Mohd Amidon; Anis Hazirah ‘Izzati Hasnu Al-Hadi; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp735-741

Abstract

This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.
A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading Siti Mariatul Hazwa Mohd Huzir; Noratikah Zawani Mahabob; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1358-1365

Abstract

Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.
Wireless hand motion controlled robotic arm using flex sensors Zakiah Mohd Yusoff; Siti Aminah Nordin; Arni Munira Markom; Nurul Nadia Mohammad
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.pp133-140

Abstract

In today's world, in almost all industries, much of the work is performed by robots or robotic arms with varying degrees of freedom (DOF) as necessary. The aim of this study is to adjust the perception of remote controls for manually controlled robotic-arm operation. This paper offers a way of thinking and a way to eradicate the keys, joysticks and replace them with some of the more intuitive strategy that is to operate the full robotic arm by hand movements operators. The robotic arm is constructed in such a way that it consists of two movable fingers and other movement, which is, a spreading elbow and the up down movement. The robotic arm is designed to mimic the motions of human hands using a hand glove. The hand glove consists of 3 flex sensors for controlling the motions of the finger, the elbow, and other movements. Servo motors are the actuators used by the robotic arm. The proposed electronics device recognizes a basic hand gesture that will be made in real lifetime and will relay valued signals wirelessly through the RF module.
A novel application of artificial neural network for classifying agarwood essential oil quality Noratikah Zawani Mahabob; Zakiah Mohd Yusoff; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Mohd Nasir Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6645-6652

Abstract

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.
Fingerprint biometric voting machine using internet of things Zakiah Mohd Yusoff; Yusradini Yusnoor; Arni Munira Markom; Siti Aminah Nordin; Nurlaila Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp699-706

Abstract

Free elections are one of democracy's principles. Elections will be used to choose the representatives of the people. It is underlined on how important it is to organize free, fair, and secret elections. Traditionally, voting used to be conducted by stamping on paper, then placing it in a ballot box with the chosen candidate. Each vote in every ballot box must be counted separately, and the votes for each contender must then be added up to determine which candidate had the most votes. Everything was done manually, it will take longer to announce the winner. Numerous errors are being made, but they will not change the outcome. In this study, a significant system that stops electoral malpractices and expedites the voting process will propose. The controller utilized in this project is the Arduino Uno. The user is authenticated using a fingerprint. Everybody's fingerprints differ from one another. The device is programmed using the Arduino IDE, and the ballot card is displayed, and the results are stored in the cloud. Only a registered voter may cast a vote, and the system alerts users to any fraud. This project protects citizens' freedom to vote and ensures an impartial election.
Co-Authors Anis Hazirah ‘Izzati Hasnu Al-Hadi Aqib Fawwaz Mohd Amidon Arni Munira Markom Erny Haslina Abd Latib Jasmin Jamil Latifah Noh Mohd Khairul Mohd Salleh Mohd Nasir Taib Mohd Syafiq Izwan Mohd Razi Muhd Hussaini Che Hashim Noor Fadzilah Razali Noor Syafina Mahamad Jainalabidin Noratikah Zawani Mahabob Norfishah Ab Wahab Nurlaila Ismail Nurul Nadia Mohammad Saiful Nizam Tajuddin Saiful Nizam Tajuddin Siti Aminah Nordin Siti Aminah Nordin Siti Mariatul Hazwa Mohd Huzir Yusradini Yusnoor Zuhani Ismail Khan Zuraida Muhammad