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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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Days of autonomy for optimal Battery Sizing in Stand-alone Photovoltaic Systems Meriem Andam; Jamila El Alami; Younes Louartassi; Rabie Zine
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4417

Abstract

The main purpose of our article is to optimize the battery sizing by identifying the most appropriate number of autonomy days. A case study has been established and simulated to define the optimal number. In the others current researches, only a small importance has been attributed to the battery autonomy. The objective is generally to ensure a continuous presence of energy especially for isolated systems while this is not always optimal nor economical and does not necessarily guarantee a safe supply. Nevertheless, an over dimensioning of the battery will lead to a consequent cost and a loss of energy. The results show that the number of days of autonomy must correspond to the minimum ratio linking the lack of energy to the surplus during a specific period.
SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis Anuradha Vishwajit Yenkikar; C. Narendra Babu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4381

Abstract

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.
An Effective Model Of Autism Spectrum Disorder Using Machine Learning Razieh Asgarnezhad; Karrar Ali Mohsin Alhameedawi; Hani Akram Mahfoud
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4060

Abstract

Autism spectrum disorder (ASD) is one of the most common diseases that affect human nerves and cause a decrease in the intelligence and comprehension of the person. This disease is a group of various disorders that are characterized by poor social behavior and communication. It affects all age groups, including adults, adolescents, children, and the elderly, but the symptoms of this disease always appear in their early years. ASD suffer from problems, the most important of which are data loss, low quality, and extreme values. This makes the process of diagnosing the ASD early. Our goals in this research is to solve the ASD problems. The cussent authors proposed a technical model that solves all data problems. We used ensemble techniques that include Bayesian Boosting, Classification by Regression, Polynomial by Binominal Classification. We also used classification techniques that include CHAID, Decision Stump, Decision Tree (Weight-Based), Gradient Boosted Trees, ID3. It is proven that the proposed model solves data problems, and has obtained the highest search accuracy that has reached 100% as well as we have obtained the highest f1 measurement that has reached 100%. This proves that our work is superior to its peers.
Techniques for Improving the Performance of Unsupervised Approach to Sentiment Analysis Farha Naznin; Anjana K. Mahanta
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4187

Abstract

In this work, few techniques were proposed to enhance the performance of unsupervised sentiment analysis method to categorize review reports into sentiment orientations (positive and negative). In review reports, generally negations can change the polarity of other terms in a sentence. Therefore, a new technique for handling negations was proposed. As it is seen that, the positions of terms in a report are also important i.e. the same term appearing at different positions in a report may convey different amount of sentiments. Thus, a new technique was proposed to assign weights to the terms depending on their positions of occurrences within a review. Again, another technique was proposed to use the presence of exclamatory marks in the reviews as the effects of exclamatory marks are equally important in categorizing review reports. After incorporating all these concepts in the first phase of the proposed method, in the second phase, analysis of sentiment orientations was done using cluster ensemble method. The proposed method was tested on a state-of-the-art Movie review dataset and 91.75% accuracy was achieved. A significant improvement over some of the unsupervised and supervised methods in terms of accuracy was achieved with incorporation of the new techniques.
Testable Design for Positive Control Flipping Faults in Reversible Circuits Mousum Handique; Hiren K D Sarma
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4184

Abstract

Fast computational power is a major concern in every computing system. The advancement of the fabrication process in the present semiconductor technologies provides to accommodate millions of gates per chip and is also capable of reducing the size of the chips. Concurrently, the complex circuit design always leads to high power dissipation and increases the fault rates. Due to these difficulties, researchers explore the reversible logic circuit as an alternative way to implement the low-power circuit design. It is also widely applied in recent technology trends like quantum computing. Analyzing the correct functional behavior of these circuits is an essential requirement in the testing of the circuit. This paper presents a testable design for the k-CNOT based circuit capable of diagnosing the Positive Control Flipping Faults (PCFFs) in reversible circuits. The proposed work shows that generating a single test vector that applies to the constructed design circuit is sufficient for covering the PCFFs in the reversible circuit. Further, the parity-bit operations are augmented to the constructed testable circuit that produces the parity-test pattern to extract the faulty gate location of PCFFs. Various reversible benchmark circuits are used for evaluating the experimental results to establish the correctness of the proposed fault diagnosis technique. Also a comparative analysis is performed with the existing work.
A Cost Sensitive SVM and Neural Network Ensemble Model for Breast Cancer Classification Tina Elizabeth Mathew
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.3934

Abstract

Breast Cancer has surpassed all categories of cancer in incidence and is the most prevalent form of cancer in women worldwide. The global incidence rate is seen to be highest in the country of Belgium as per statistics of WHO. In the case of developing countries specifically, India, it has overtaken other cancers and stands first in incidence and mortality. Major factors identified as impacting the prognosis and survival in the country is chiefly the late diagnosis of the disease and diverse situations prevailing in different parts of the country including lack of diagnostic facilities, lack of awareness, fear of undergoing existing procedures and so on. This is also true for many other countries in the world. Early diagnosis is a vital factor for survival. The implementation of machine learning techniques in cancer prediction, diagnosis and classification can assist medical practitioners as a supplementary diagnostic tool. In this work, an ensemble model of a polynomial kernel-based Support Vector machines and Gradient Descent with Momentum Back Propagation Artificial Neural Networks for Breast Cancer Classification is proposed. Feature selection is applied using Genetic Search for identifying the best feature set and data sampling techniques such as combination of oversampling and undersampling and cost senstivke learning are applied on the individual Neural Network and Support Vector Machine classifiers to deal with issues related with class imbalance. The ensemble model is seen to show superior performance in comparison with other models producing an accuracy of 99.12%.
A Novel Approach for improving Post Classification Accuracy of Satellite Images by Using Majority Analysis Swasti Patel; Dr. Priya Swaminarayan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4270

Abstract

In past one year, due to climatic changes and some anthropogenic activities, the forests of Uttarakhand are burning. To identify the damage caused by the forest fires, an area of Nainital district has been taken for the study. Multi temporal Landsat 7 images were taken from April - 2020 and April – 2021. This paper shows a novel approach to increase the accuracy of the classified image. The Support Vector Machine classification is first done and then to improve the accuracy of the classified image, a post-classification technique called Majority Analysis is applied. This method helps to classify the unclassified pixel and it also smoothens out the boundary of the classified pixels, leading to higher accuracy rate. The classification accuracy has improved significantly for April 2020 and April 2021 images from 89.35% to 98.71% and from 88.52% to 99.76% respectively. The change detection study showed a drastic increase in the barren land due to the forest fires and on the contrary, the forest, scarce forest and the shrub land areas have decreased.
Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey Brahim Hilali; Mohammed Ramdani; Abdelwahab Naji
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4009

Abstract

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase.
Pectoral Muscle Removal in Digital Mammograms Using Region Based Standard Otsu Technique Jacinta C. Anusionwu; Vincent C. Chijindu; Joy N. Eneh; ThankGod I. Ozue; Nnabuike Ezukwoke; Mamilus A. Ahaneku; Edward C. Anoliefo; Walter A. Ohagwu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4043

Abstract

Mammography is usually the first preference of imaging diagnostic modalities used for detection of breast cancer in the early stage. Two projections Cranio Caudal (CC) and Medio-Lateral Oblique (MLO) which depict different degrees for visualizing the breast are used during digital mammogram acquisition and the MLO view shows more breast tissue and Pectoral Muscle (PM) area when compared to CC view. Although, the PM is a criterion used to show proper positioning, it can result in biased results of mammographic analysis like: cancer detection and breast tissue density estimation, because the PM area has similar or even higher intensity than breast tissue and breast lesions if present. This paper proposed a Region Based Standard Otsu thresholding method for the elimination of PM area present in MLO mammograms. The proposed algorithm was implemented using 322 digital mammograms from the Mammographic Image Analysis Society (MIAS) database, and the difference between the PM detected and the manually drawn PM region by an expert was evaluated. The results showed an average: Jaccard Similarity Index, False Positive Rate (FPR) and False Negative Rate (FNR) of 93.2%, 3.54% and 5.68% respectively and also an acceptable rate of 95.65%
Development and Evaluation of a High-Performance Electrochemical Potentiostat-Based Desktop Application for Rapid SARS-CoV-2 Testing Faisal Ahmed Assaig; Teddy Surya Gunawan; Anis Nurashikin Nordin; Rosminazuin Ab. Rahim; Zainihariyati Mohd Zain; Rozainanee Mohd Zain; Fatchul Arifin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4645

Abstract

The COVID-19 pandemic has necessitated the development of rapid and trustworthy diagnostic tools. Reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for detecting SARS-CoV-2 but has cost and time constraints. The sensitivity, specificity, and low cost of electrochemical biosensors make them an attractive alternative for virus detection. This study aims to develop and evaluate a high-performance desktop application for an electrochemical potentiostat-based SARS-CoV-2 test device, with a user-friendly interface that automatically interprets results, to expedite the testing process and improve accessibility, particularly in resource-limited settings. The application was built with the Electron framework and the HTML, CSS, and JavaScript programming languages. Our findings indicate that the developed electrochemical potentiostat-based desktop application demonstrates high accuracy compared to commercial software, achieving rapid detection within 30 seconds. The graphical user interface was found to be straightforward and user-friendly, requiring minimal training for efficient system operation. Our electrochemical potentiostat-based desktop application represents a valuable tool for rapid SARS-CoV-2 testing, particularly in settings with limited resources. This research contributes to developing rapid and reliable diagnostic tools for SARS-CoV-2 and potentially other pandemic-causing viruses, addressing the pressing need for improved public health surveillance and response strategies.

Page 57 of 79 | Total Record : 783

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