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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 19 Documents
 1   2 
Search results for , issue "Vol 9, No 3: September 2021" : 19 Documents clear
A Meandered Line Patch Antenna at Low Frequency Range for Early Stage Breast Cancer Detection Md Abdullah Al Rakib; Shamim Ahmad; Md. Humayun Kabir Khan; Mainul Haque; Tareq Mohammad Faruqi; Md Saroar Jahan; Jhuma Kabir Mim
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/.v9i3.2824

Abstract

Every year a concerning number of women are affected by breast cancer which is one of the deadliest and common types of cancers. Breast cancer is curable at early stages. For detecting breast cancer, there are several methods such as MRI, Mammography, Tomography, Ultrasound, and biopsy are available in medical technology. Still, none of them are as easy and efficient as a microwave imaging technique, in this method, the antenna plays an important role. Therefore, this paper focuses on developing an antenna at a low-frequency range for microwave imaging techniques to detect cancerous tissue inside the breast. For this, the antenna parameters, i.e., return loss, VSWR, directivity, current density, and specific absorption rate were studied, by setting the antenna over without tumor and with tumor breast as up-side-down, to ensure the compatibility of the antenna for the technique as well as for the patient’s body. A 5mm radius cancerous tumor was created inside the breast with dielectric conductivity of 4 and relative permittivity of 50. Cancerous cells were detected by reading the antenna parameters’ comparison between the healthy breast and the affected breast. The whole study was conducted by using CST MICROWAVE STUDIO SUITE 2020. 
M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems Ali Hussein Mohammed; Ayad Hameed Mousa; Nawal Mousa Almeyali; Intedhar Shakir Nasir
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/.v9i3.3032

Abstract

Currently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS have adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine post-adoption users' satisfaction and users’ intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users’ intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model’s factors. The authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed.
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.

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