Articles
<![CDATA[Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification ]]>
<![CDATA[Mohd Hatta Jopri]]>;
<![CDATA[Abdul Rahim Abdullah]]>;
<![CDATA[Jingwei Too]]>;
<![CDATA[Tole Sutikno]]>;
<![CDATA[Srete Nikolovski]]>;
<![CDATA[Mustafa Manap]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v20.i1.pp1-8
<![CDATA[A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and naïve bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.]]>
<![CDATA[Phishing detection system using machine learning classifiers ]]>
<![CDATA[Nur Sholihah Zaini]]>;
<![CDATA[Deris Stiawan]]>;
<![CDATA[Mohd Faizal Ab Razak]]>;
<![CDATA[Ahmad Firdaus]]>;
<![CDATA[Wan Isni Sofiah Wan Din]]>;
<![CDATA[Shahreen Kasim]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v17.i3.pp1165-1171
<![CDATA[The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.]]>
<![CDATA[An efficient hybrid model for secure transmission of data by using efficient data collection and dissemination (EDCD) algorithm based WSN ]]>
<![CDATA[Mustafa Mahmood Akawee]]>;
<![CDATA[Mohanad Ali Meteab Al-Obaidi]]>;
<![CDATA[Haider Mohammed Turki Al-Hilfi]]>;
<![CDATA[Sabbar Insaif Jassim]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v20.i1.pp545-551
<![CDATA[Wireless sensor network (WSN) is one of the most important elements of the Internet of Things paradigm. Energy consumption is a vital issue in IoT and WSN. Security primitives in the IoT are energy consuming. Addressed the security issue for transmitted data by IoT sensor node add another challenge in term of energy consumption. finding the satisfactory solutions that reduce power consumption at the same time as making sure the required security services is not always an easy undertaking. Therefore, in this article, we proposed an efficient hybrid model for secure transmission of data from sensor nodes to receivers in WSN applications. The proposed model includes two algorithms rivest–shamir–adleman (RSA) and efficient data collection and dissemination (EDCD). The key idea behind the proposed model is to prevent to secure sensed data if no significant change between the current data and the last transmitted data by the apply EDCD1 algorithm, which that will help in saving the sensor node energy. The reason for that the size of cipher data is so large compared to the sensed data, which that will increase the energy consumption. The outcome results shown that the proposed model has a high performance compared to RSA in term of energy consumption.]]>
<![CDATA[Dual-band bandpass filter based on two U-shaped defected microstrip structure ]]>
<![CDATA[Mussa Mabrok]]>;
<![CDATA[Zahriladha Zakaria]]>;
<![CDATA[Yully Masrukin]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v22.i2.pp909-918
<![CDATA[This paper presents design of dual-band bandpass filter by integrating conventional quarter-wavelength short circuit stubs bandpass filter with U-shaped defected microstrip structure notch filter. Based on the parametric analysis, it is found that high attenuation level can be achieved by using two U-shaped defected microstrip structure separated by specific distance. The designed circuit simulated using advanced design system and fabricated based on Roger 4350B. The simulation results are in good agreement with measured results. The designed filter covered two pass bands centered at 2.51 GHz and 3.59 GHz with 3-dB fractional bandwidth of 15.94% and 15.86%, respectively, return losses better than 15 dB, and insertion losses better than 1 dB. The designed device can be used for wireless communication applications such as WLAN and WiMAX.]]>
<![CDATA[An analysis of manual and autoanalysis for submicrosecond parameters in the typical first lightning return stroke ]]>
<![CDATA[Muhammad Akmal Bahari]]>;
<![CDATA[Zikri Abadi Baharudin]]>;
<![CDATA[Tole Sutikno]]>;
<![CDATA[Ahmad Idil Abdul Rahman]]>;
<![CDATA[Mohd Ariff Mat Hanafiah]]>;
<![CDATA[Mazree Ibrahim]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v23.i3.pp1451-1457
<![CDATA[The mechanism on how lightning detection system (LDS) operated never been exposed by manufacturer since it was confidential. This scenario motivated the authors to explore the issue above by using MATLAB to develop autoanalysis software based on the feature extraction. This extraction is intended for recognizing the parameters in the first return stroke, and compare the measurement between the autoanalysis software and the manual analysis. This paper is a modification based on a previous work regarding autoanalysis of zero-crossing time and initial peak of return stroke using features extraction programming technique. Further, the parameter on rising time of initial peak is added in this autoanalysis programming technique. Finally, the manual analysis using WaveStudio (LeCroy product) of those two lightning parameters is compared with autoanalysis software. This study found that the autoanalysis produce similar result with the manual analysis, hence proved the reliability of this software.]]>
<![CDATA[Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection ]]>
<![CDATA[Farizuwana Akma Zulkifle]]>;
<![CDATA[Rohayanti Hassan]]>;
<![CDATA[Mohammad Nazir Ahmad]]>;
<![CDATA[Shahreen Kasim]]>;
<![CDATA[Tole Sutikno]]>;
<![CDATA[Shahliza Abd Halim]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v24.i3.pp1499-1514
<![CDATA[Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.]]>
<![CDATA[Integrated hybrid optical networking for 5G access networks ]]>
<![CDATA[Dawit Hadush Hailu]]>;
<![CDATA[Gebrehiwet Gebrekrstos Lema]]>;
<![CDATA[Gebremichael T. Tesfamariam]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v17.i3.pp1647-1656
<![CDATA[Today, deployment of optical fiber has offered large transmission capacity which cannot be efficiently utilized by the electronic switches. Rather, Integrated Hybrid Optical Network (IHON) is a promising approach which combines both packet and circuit switching techniques. As a result, it achieves efficient utilization of the bulk capacity and guarantees absolute Quality of Service (QoS) by optimizing the advantages of the two switching schemes while diminishing their disadvantages. Transpacket has developed a Fusion node implementing IHON principles in Ethernet for the data plane. Hence, this paper investigates and evaluates IHON network for 5G access networks. The simulated results and numerical analysis confirm that the Packet Delay Variation (PDV), Delay and Packet Loss Ratio (PLR) of Guaranteed Service Transport. (GST) traffic in IHON network met the requirements of 5G mobile fronthaul using CPRI. The number of nodes in the network limits the maximum separation distance between Base Band Unit. (BBU) and Remote Radio Head (RRH), link length; for increasing the number of nodes, the link length decreases. In addition to this, we verified how the leftover capacity of fusion node can be used to carry the low priority packets and how the GST traffic can have deterministic characteristics on a single wavelength by delaying it with Fixed Delay Line (FDL). For example, for LSM1GE=0.3 the added Statistical Multiplexing (SM) traffic increases the 10GE wavelength utilization up to 89% without any losses and with SM PLR=1E−03 up to 92% utilization.]]>
<![CDATA[User identification system for inked fingerprint pattern based on central moments ]]>
<![CDATA[Esraa Jaffar Baker]]>;
<![CDATA[Sundos Abdulameer Alazawi]]>;
<![CDATA[Nada Thanoon Ahmed]]>;
<![CDATA[Mohd Arfian Ismail]]>;
<![CDATA[Rohayanti Hassan]]>;
<![CDATA[Shahliza Abd Halim]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v24.i2.pp1149-1160
<![CDATA[The use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.]]>
<![CDATA[Live to learn: learning rules-based artificial neural network ]]>
<![CDATA[Aseel Shakir I. Hilaiwah]]>;
<![CDATA[Hanan Abed Alwally Abed Allah]]>;
<![CDATA[Basim Akhudir Abbas]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v21.i1.pp558-565
<![CDATA[An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.]]>
<![CDATA[Distribution of attempted leader with monsoon seasons and negative cloud-to-ground flashes in Melaka, Malaysia ]]>
<![CDATA[Nur Asyiqin Isa]]>;
<![CDATA[Zikri Abadi Baharudin]]>;
<![CDATA[Hidayat Zainuddin]]>;
<![CDATA[Tole Sutikno]]>;
<![CDATA[Maslan Zainon]]>;
<![CDATA[Ahmad Aizan Zulkefle]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v23.i3.pp1324-1330
<![CDATA[Ninety (90) waveforms recognized as attempted leader were identified with both positive (84 events) and negative (6 events) initial polarity observed from four consecutive years of data (N=10,206). The positive attempted leader shows no correlation with the number of thunderstorms producing it during monsoon. Meanwhile, the negative attempted leader during monsoon and both polarity of attempted leader (positive and negative) during inter-monsoon shows positive correlation with the number of thunderstorms producing it. In this study, the yearly statistical distribution of negative cloud-to-ground (CG) lightning flashes which were classified as positive preliminary breakdown pulses (214 events) and negative preliminary breakdown pulses (4982 events) in accordance of their preliminary polarity were also presented. In addition, there is no relationship of attempted leader and the initial breakdown of negative ground flash since both mechanisms performed as a negative correlation.]]>
