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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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Machine Learning Centered Energy Optimization In Cloud Computing: A Review Nomsa Puso; Tshiamo Sigwele; Oba Zubair Mustapha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.5037

Abstract

The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.
Energy Management Analysis of Residential Building Using ANN Techniques Lohit Kumar Sahoo; Mitali Ray; Sampurna Panda; Subash Ranjan Kabat; Smita Dash
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4607

Abstract

  The process of limiting the amount of energy that is utilized is known as energy conservation. This can be accomplished by making more effective use of the energy that is available. As a result, there is a requirement for more effective management of the consumption of energy in buildings. It is essential to have an accurate load calculation for a residential building because the loads for heating and cooling add up a significant portion of the total building loads. In this study, the load analysis of the HVAC (Heating, Ventilation, and Air Conditioning) system in a residential building was carried out by taking into consideration three different neural networks. These networks are known as the feed forward network, the cascaded forward back propagation network, and the Elman back propagation network. During the process of conducting a load study of the heating and cooling loads on an HVAC system, performance measurements like MAE (mean absolute error), MSE (mean square error), MRE (mean relative error), and MAPE (mean absolute percentage error) are taken into consideration. It has been discovered that the cascaded forward back propagation method is the most effective method, with MAE, MSE, MRE, and MAPE values of 0.08, 0.0336, 0.0051, and 0.51% respectively for heating load and MAE, MSE, MRE, and MAPE values of 0.0975, 0.0406, 0.0053, and 0.53% respectively for cooling load.
Simplified Kinetic Model of Heart Pressure for Human Dynamical Blood Flow Saktioto Saktioto; Defrianto Defrianto; Andika Thoibah; Yan Soerbakti; Romi Fadli Syahputra; Syamsudhuha Syamsudhuha; Dedi Irawan; Haryana Hairi; Okfalisa Okfalisa; Rina Amelia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.3473

Abstract

The blood flow that carries various particles results in disturbed physical flow in the heart organ caused by speed, density, and pressure. This phenomenon is complicated resulting in a wide variety of medical problems. This research provides a mathematical technique and numerical experiment for a straightforward solution to cardiac blood flow to arteries. Finite element analysis (FEA) is used to study and construct mathematical models for human blood flow through arterial branches. Furthermore, FEA is used to simulate the steady two-dimensional flow of viscous fluids across various geometries. The results showed that the blood flow in the carotid artery branching is simulated after the velocity profiles obtained are plotted against the experimental design. The computational method's validity is evaluated by comparing the numerical experiment with the analytical results of various functions.
Optimizing U-Net Architecture with Feed-Forward Neural Networks for Precise Cobb Angle Prediction in Scoliosis Diagnosis Mohamad Iqmal Jamaludin; Teddy Surya Gunawan; Rajendra Kumar Karupiah; Suriza Ahmad Zabidi; Mira Kartiwi; Zamzuri Zakaria
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.5009

Abstract

In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms.
Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques Siriporn Chimphlee; Witcha Chimphlee
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4892

Abstract

Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness.  The results show that the RF and XGB algorithms function exceptionally well, with high R-squared values and low error rates.  KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower R-squared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.
The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus Lola Oktavia; Okfalisa Okfalisa; Pizaini Pizaini; Rahmad Abdillah; Saktioto Saktioto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4885

Abstract

The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable.
Investigation of Photovoltaic Hosting Capacity Using Minimum Generation Operation Approach Syafii Syafii; Thoriq Kurnia Agung; Dawam Habibullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4856

Abstract

Photovoltaic (PV) have become a priority renewable energy source to be developed in Indonesia to achieve new and renewable energy (NRE) target of 23% in 2025 and 31% in 2050. The operation of a significant number of rooftop PV can also change the type of power system operating configuration to Distributed Energy Generation (DEG). The majority of DEGs which are NRE generators are capable of causing new problems because of their intermittent nature. Hosting Capacity is a high penetration limit for NRE without causing problems and limits on operational violations. The hosting capacity method used is based on the generator's minimum operation. In the test system consisting of 3 power plants such as hydro power plant, coal power plant, and geothermal power plant, the PV capacity that can be injected into the system is 139.1 MW. With PV injection based on hosting capacity, the system becomes better with the same average voltage profile as before PV injection, namely 0.991 p.u. System stability by reviewing the frequency, rotor angle, and rotor speed, the system after PV injection is better than before PV injection.
Synthesis of Bandpass Filter as a Four-Pole Based on a Non-Homogeneous Line Kozlovskiy, Valeriy; Kozlovskiy, Valerii; Boiko, Juliy; Balanyuk, Yuriy; Yakymchuk, Natalia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5537

Abstract

The article deals with the synthesis of band-pass filters (BPF) for the design of microwave filtering devices, by using non-homogeneous lines (NL) with the selection of the appropriate wave impedance W. For this purpose, equivalent NL substitution circuits were created in the region of resonant and antiresonant frequencies, and four-pole matrices of the transmission line were determined, whose matrix of impedances and admittances does not have partial poles, and the transmission admittance and transmission impedance do not have zeros. BPF prototypes were synthesized with two parallel plumes based on a closed homogeneous line and one plume based on three NLs. A band-pass filter with an extended blocking band was implemented, and its amplitude-frequency characteristics were obtained. The use of NLs as resonators allows the choice of wave impedance to increase the blocking band of the BPF compared to the BPF on resonators based on homogeneous lines.
The Analytical Approach to Evaluate the Bit Error Rate Performance of PLC System in Presence of Cyclostationary Non-White Gaussian Noise Rahman, M. M.; Alam, M. T.; Ashiquzzaman, M.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5386

Abstract

In a Powerline Communication (PLC) system, improper connections of associated hardwires can lead to the generation of unwanted RF signals, overriding the transmitted signal and producing undesired RF spurious signals. Noise in powerlines also arises from the corona effect, voltage impulses, and arcs occurring in transmission and distribution lines, significantly compromising the integrity of the PLC network. Analysis indicates that powerline noise exhibits a non-white cyclostationary characteristic. Due to its severity, PLC noise is categorized primarily as background noise and impulsive noise. This paper evaluates the characteristics of a powerline network under severe noisy conditions, particularly focusing on Cyclostationary Non-White Additive Gaussian Noise (CNWAGN) across broadband and narrow frequency communication channels. Accordingly, an analytical model is developed to specifically examine the bit error rate (BER) in environments affected by non-white additive Gaussian noise. BER and receiver sensitivity are also assessed for various bit rates using MATLAB simulations, demonstrating performance in terms of BER. This analytical model provides a straightforward method to evaluate results across different bit error rates in frequency-dependent and independent scenarios, surpassing traditional approaches. It proves highly effective in assessing Powerline Communication System performance, with analytically derived outcomes closely aligning with simulation results.
Classifications of Arabic Customer Reviews Using Stemming and Deep Learning Khelil, Hawraa Fadhil; Ibrahim, Mohammed Fadhil; Hussein, Hafsa Ataallah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5452

Abstract

With the emergence of AI text-based tools and applications, the need to present and investigate text-processing tools has also been raised. NLP tools and techniques have developed rapidly for some languages, such as English. However, other languages, like Arabic, still need to present more methods and techniques to present more explanations. In this study, we present a model to classify customer reviews which are written in Arabic. The HARD dataset is used to be adopted as the dataset. Three Deep Learning classifiers are adopted (CNN, LSTM, RNN). In addition to that, three stemmers are used as text processing techniques (Khoja, Snowball, Tashaphyne). Furthermore, another three feature extraction methods were utilized (TF-IDF, N-gram, BoW). The results of the model presented several explanations. The best performance resulted from using (CNN+ Snowball+ N-Gram) with an accuracy of (%93.5). The results of the model stated that some classifiers are sensitive toward using different stemmers, also some accuracy performance can be affected if there are different feature extraction methods used. Either stemming of feature extraction has an impact on the accuracy performance. The model also proved that the dialectal language could cause some limitations since different dialects can give conflict meaning across different regions or countries. The outcomes of the study open the gate towards investigating other tools and methods to enrich Arabic natural language processing and contribute to developing new applications that support Arabic content.

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