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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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Dynamic Spectrum Allocation Access Using Cognitive Radio Networks in a Maritime Dickson E Onu; Mamilus Aginwa Ahaneku; Michael O Ezea; Henry O Osuagwu; Udora Nwabuoku Nwawelu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2661

Abstract

The maritime environment is unique due to radio wave propagation over water, surface reflection and wave obstruction. In dealing with the challenging maritime environment, a dynamic spectrum allocation access using cognitive radio network through optimization is proposed. Existing works in this area are limited in performance due to the long duration in achieving the probability of false alarm. Matched filtering technique which is known as the optimum method for detection of primary users (PUs) faces the challenge of large power consumption as various receiver’s algorithm are needed to be executed for detection. This work provides a platform that enables minimum energy utilization by secondary users (SUs) thereby, enhancing throughput. An algorithm for throughput maximum in spectrum allocation was developed and used based on demand based model. The implementation of the developed model was carried out using Java program and the spectrum analysis using long distance path loss model and adaptive modulation code to estimate the minimum bandwidth of the secondary users. A simulation of cognitive radio mesh network for the testing and validation of the demand based algorithm preference, and also the cognitive radio network traffic was carried out using Cisco packet tracer and results shown on MATLAB. Simulation results indicate that using the demand based algorithm, the throughput rose with time and almost stabilized. This increase and steady throughput indicates effectiveness in the algorithm which shows that the PUs and SUs activities increase as holes’ detection effort varies, unlike that of genetic algorithm where the throughput rose gradually, got to a peak value at certain time and then fell which indicates instability in the variation of the throughput. Also, the average throughput of the demand based algorithm is far greater than that of genetic algorithm which shows that demand based algorithm outperforms the genetic by a far greater percentage. The percentage of optimization is approximately 26%.
Towards a better understanding of the Organizational Characteristics that affect Acceptance of Big Data Platforms for Academic Teaching Adnan Aldholay; Osama Isaac; Abdullah Nabeel Jalal; Farah Akmar Anor; Ahmed M. Mutahar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2902

Abstract

In today's era of information, data has been growing at an exponential rate to become big data, and it needs platforms to allow users to govern, access, deliver, analyze, and use these huge databases. Academics in higher education need to utilize these platforms in teaching to enrich and empower the educational experience of their students of these institutions. The purpose of the current study is to investigate the impact of organizational characteristics on the acceptance of big data platforms for academic teaching among higher education institutes in Malaysia. 143 respondents participated to examine the effect of organizational characteristics (Management Drive, Bandwagon Pressure, and Training) on the acceptance of big data platforms for academic teaching. Besides, examining the moderating role of task technology fit. The results illustrate that management drive, bandwagon pressure has a significant impact on the acceptance, with an insignificant impact of training on the acceptance. However, task technology fit has not moderated any of the proposed relationships. This study would give insight for the higher education institutes managements to improve their academics acceptance of the big data platforms in teaching and therefore drive them to use the aforementioned platforms.
Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems Fatimetou Abdou VADHIL; Mohamedade Farouk NANNE; Mohamed Lemine SALIHI
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.3219

Abstract

Nowadays, it became difficult to ensure data security because of the rapid development of information technology according to the Vs of Big Data. To secure a network against malicious activities and to ensure data protection, an intrusion detection system played a very important role. The main objective was to obtain a high-performance solution capable of detecting different types of attacks around the system. The main aim of this paper is to study the lacks of traditional and open source Intrusion Detection Systems and the Machine Learning techniques commonly used to overcome these lacks. A comparison of some existing works by Intrusion Detection System type, detection method, algorithm and accuracy was provided.
Design of FIR digital filters using Semi-ellipse window Henry N. Uzo; Ogbonna U. Oparaku; Vincent C. Chijindu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2481

Abstract

A fixed window function which is similar in shape to a semi-ellipse is proposed. The semi–ellipse which has its major axis to be equal to the window length and the minor axis at unity produced about 4.2 dB lower ripple ratio than the rectangular window. The proposed window function is derived from the equation of an ellipse in the explicit and parametric forms. First of all, the spectral characteristic of the proposed window is studied in terms of spectral parameters and compared with other fixed windows like Rectangular, Bartlett, Hann, Hamming and Blackman windows. The window simulation results reveal that the proposed window produced comparable spectral characteristic with existing standard fixed windows. Secondly, the paper presents the application of the proposed window in a digital filter design. The filter analysis comparison results with other fixed windows namely Bartlett, Von Hann, Hamming, and Kaiser window, an adjustable window, confirm that filter design with the proposed window exhibits good spectral characteristic, and can be used to design better filter than the Bartlett window using less than half the Bartlett’s filter length for a fixed transition width. The similicity of its coefficients formulation and design algorithm makes it a good choice for digital filter design applications.
Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia Sarifah Putri Raflesia; Taufiqurrahman Taufiqurrahman; Silfi Iriyani; Dinda Lestarini
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2723

Abstract

Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network’s learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model.
Ultra-wideband bandpass filter with notch band based on quadratic Koch Island structure Manju Bhaskar; Thomaskutty Mathew
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2547

Abstract

An ultra-wideband bandpass filter with a notch band centered at 7.2 GHz is proposed to remove the interference caused by satellite communication signal coexciting within the ultra wide band. The filter comprises of two seperated quadratic koch island structures connected to the main transmission line to generate the notch band at the desired frequency. The designed ultra wide bandpass filter passes frequencies from 3.09 GHz to 10.61 GHz with a notch band from 7.12 to 7.46 GHz centered at 7.2 GHz and with a rejection level of 21.3 dB.The resonant frequency and bandwidth of the notch can be varied by the variation in the physical parameter of the filter. The proposed filter is fabricated, tested and compared with simulated results.
Robust and Effective Banknote Recognition Model for Aiding Visual Impaired People Asfaw Alene Shefraw
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i3.2937

Abstract

Visual disabled Ethiopians find great difficulty in recognizing banknotes. Each Ethiopian banknote has an identical feel, with no Braille markings, irregular edges, or other tangible features that make it easily recognizable by blind persons. In Ethiopia, there's only one device available that will assist blind people to acknowledge their notes. Internationally, there are devices available; however, they're expensive, complex, and haven't been developed to cater to Ethiopian currency. Because of these facts, visually impaired people may suffer from recognizing each folding money. This fact necessitates a higher authentication and verification system that will help visually disabled people to simply identify and recognize the banknotes. This paper presents a denomination-specific component-based framework for a banknote recognition system. Within the study, the dominant color of the banknotes was first identified and so the exclusive feature for every denomination-specific ROI was calculated. Finally, the Colour-Momentum, dominant color, and GLCM features were calculated from each denomination-specific ROI. Designing the recognition system by thereby considering the denomination-specific ROI is simpler as compared to considering the entire note in collecting more class-specific information and robust in copying with partial occlusion and viewpoint changes. The performance of the proposed model was verified by using a larger dataset of which containing banknotes in several conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves a 98% recognition rate on our challenging datasets.
Utilising Target Adjacency Information for Multi-target Prediction Ruhaila Maskat; Ramli Musa; Norizah Ardi; Noor Afni Deraman; Zaaba Ahmad; Qingchen Wang; Shukor Sanim Mohd Fauzi; Ray Adderley JM Gining; Tajul Rosli Razak
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3218

Abstract

In this paper, we explored how information on the cost of misprediction can be used to train supervised learners for multi-target prediction (MTP). In particular, our work uses depression, anxiety and stress severity level prediction as the case study. MTP describes proposals which results require the concurrent prediction of multiple targets. There is an increasing number of practical applications that involve MTP. They include global weather forecasting, social network users’ interaction and the thriving of different species in a single habitat. Recent work in MTP suggests the utilization of “side information” to improve prediction performance. Side information has been used in other areas, such as recommender systems, information retrieval and computer vision. Existing side information includes matrices, rules, feature representations, etc. In this work, we review very recent work on MTP with side information and propose the use of knowledge on the cost of incorrect prediction as side information. We apply this notion in predicting depression, anxiety and stress of 270,322 anonymous respondents to the DASS-21 psychometric scale in Malaysia. Predicting depression, anxiety and stress based on the DASS-21 fit an MTP problem. Often, a patient experiences anxiety as well as depression at the same time. This is not unusual since it has been discovered that both tend to co-exist at different degrees depending on a patient’s experience. By using existing machine learning algorithms to predict the severity levels of each category (i.e., depression, anxiety and stress), the result shows improved precision with the use of cost matrix as side information in MTP.
Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network Samson Iyanda Ojo; Zachaeus Kayode Adeyemo; Rebeccah Oluwafunmilayo Omowaiye; Oluwatobi Omolola Oyedokun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3179

Abstract

Accurate detection of White Space (WS) is of paramount importance in a Cognitive Radio Network (CRN) to prevent authorized users from harmful interference. However, channel impairment such as multipath fading and shadowing affects accurate detection of WS resulting in interference. The Existing Feature Detection (EFD) technique used to address the problem is faced with computational complexity and synchronization resulting in long sensing time, bandwidth inefficiency, energy constrain and poor detection rate. Hence, this paper proposes autocorrelation based multiple antenna with energy harvesting for WS detection in a CRN using Radio Frequency (RF) energy harvesting and autocorrelation of the received signal with a modified Equal Gain Combiner (mEGC). Antenna Switching (AS) RF energy harvesting with mEGC are used to harvest energy and information from the received PU signal in a multiple antenna configuration. Autocorrelation is then obtained and compared with the set threshold of zero to determine the presence or absence of WS. The proposed technique is evaluated using Spectral Efficiency (SE), Probability of Detection (PD) and Sensing Time (ST) by comparing with EFD technique. The results obtained revealed that the proposed technique shows better performance than EFD.
Synthesis and Characterization of Ba0.6Sr0.4Fe12O19/LaMnO3 Composites as Microwave Absorbers Yohanes Edi Gunanto; Maya Puspitasari Izaak; Henni Sitompul; Wisnu Ari Adi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3239

Abstract

The synthesis and characterization of Ba0.6Sr0.4Fe12O19/LaMnO3 composite material has been successfully carried out by mechanical alloying method using high energy milling. Crystal structure and surface morphology were characterized using x-ray diffraction and scanning electron microscopy. While the value of magnetization and the ability to absorb microwaves, vibrating sample magnetization and vector network analyzing were used, respectively. With variations in weight, does not change the crystal structure. The Ba0.6Sr0.4Fe12O19phase has a hexagonal structure and the LaMnO3 phase has an orthorhombic structure. Surface morphology has a heterogeneous size in the range of 200-450 nm with the form of platelets. The composite material Ba0.6Sr0.4Fe12O19/LaMnO3 is a hard-soft magnetic material with a magnetic saturation of Ms ~ 46.83 emu/g, Mr ~ 28.8 emu/g, and a coercive field of Hc ~ 3.88 kOe. The minimum reflection loss value is – 13.0 dB at 11.2 GHz frequency and 1.52 GHz bandwidth.

Page 44 of 79 | Total Record : 783

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