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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 867   868   869   870   871 
Implementation of innovative deep learning techniques in smart power systems Devi, Odugu Rama; Kolluru, Pavan Kumar; Shaik, Nagul; Trinadh Naidu, Kamparapu V. V. Satya; Mohan, Chunduri; Mohana Rai, Pottasiri Chandra; Bhukya, Lakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp723-731

Abstract

The integration of deep learning techniques into smart power systems has gained significant attention due to their potential to optimize energy management, enhance grid reliability, and enable efficient utilization of renewable energy sources. This research article explores the enhanced application of deep learning techniques in smart power systems. It provides an overview of the key challenges faced by traditional power systems and presents various deep learning methodologies that can address these challenges. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 18.4 for the random forest and 18.2 for the long short-term memory (LSTM) algorithm, while 28.6 was predicted by the support vector machine (SVM) algorithm. The authors' approach provides the most accurate forecast (15.7). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. The article also discusses recent advancements, future research directions, and potential benefits of utilizing deep learning techniques in smart power systems.
Enhancing SDN security using ensemble-based machine learning approach for DDoS attack detection Hirsi, Abdinasir; Audah, Lukman; Salh, Adeb; Alhartomi, Mohammed A.; Ahmed, Salman
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1073-1085

Abstract

Software-defined networking (SDN) is a groundbreaking technology that transforms traditional network frameworks by separating the control plane from the data plane, thereby enabling flexible and efficient network management. Despite its advantages, SDN introduces vulnerabilities, particularly distributed denial of service (DDoS) attacks. Existing studies have used single, hybrid, and ensemble machine learning (ML) techniques to address attacks, often relying on generated datasets that cannot be tested because of accessibility issues. A major contribution of this study is the creation of a novel, publicly accessible dataset, and benchmarking the proposed approach against existing public datasets to demonstrate its effectiveness. This paper proposes a novel approach that combines ensemble learning models with principal component analysis (PCA) for feature selection. The integration of ensemble learning models enhances predictive performance by leveraging multiple algorithms to improve accuracy and robustness. The results showed that the ensemble of random forests (ENRF) model achieved the highest performance across all metrics with 100% accuracy, precision, recall, and F1-score. This study provides a comprehensive solution to the limitations of existing models by offering superior performance, as evidenced by the comparative analysis, establishing this approach as the best among the evaluated models.
The integration of metaverse technology in healthcare: a comprehensive review and future research directions Roy, Rita; Das, Tarinmoy; Karras, Dimitrios Alexios
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp975-987

Abstract

The impact of using the metaverse in healthcare is investigated in this research work. Emerging technologies are essential to enhancing medical consultants’ care, especially in developing countries like India. The study filters and reviews the pertinent literature using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) methodology. The initial search yielded 180 articles. Forty-four articles were considered for the study after screening the papers in light of the research questions and relevant literature. The theory-context-characteristics-methodology (TCCM) framework is used in this study to assess future metaverse research trends. This study also used the context, intervention, mechanism, and outcome (CIMO) logic for planning and decision-making. This study examines the development of metaverse research over the past ten years and supports research findings published in peer-reviewed journals. Based on the TCCM framework, recommendations have been made for additional research.
Accurate segmentation of fruit based on deep learning Elsoud, Esraa Abu; Alidmat, Omar; Abuowaida, Suhaila; Alhenawi, Esraa; Alshdaifat, Nawaf; Aburomman, Ahmad; Chan, Huah Yong
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1331-1338

Abstract

In the last few years, deep learning has exhibited its efficacy and capacity in the field of computer vision owing to its exceptional precision and widespread acceptance. The primary objective of this study is to investigate an improved approach for segmentation in the context of various fruit categories. Despite the utilization of deep learning, the current segmentation techniques for various fruit items exhibit subpar performance. The proposed enhanced multiple fruit segmentation algorithm has the following main steps: 1) modifying the size of the filter, 2) the process of optimizing the ResNet-101 block involves selecting the most suitable count of repetitions. The multiple fruit dataset is split 80% in the training stage and 20% in the testing stage. These images were utilized to train a deep learning (DL) based algorithm, which aims to identify multiple fruit items within images accurately. The proposed algorithm has a lower training time compared to the other algorithms. The thresholds exhibit greater values compared to the thresholds of state-of-the-art algorithms.
Enhancing mobility with customized prosthetic designs driven by genetic algorithms Seeni, Senthil Kumar; Harshitha, Ganadamoole Madhava; Rathinam, Anantha Raman; Venkatara, Nagaiyanallur Lakshminarayanan; Sasirekha, Venkatesan; Tidke, Bharat; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp876-886

Abstract

Using genetic algorithms, this research intends to usher in a new era of prosthetic design that is redefining mobility. Through repeated evolutionary processes influenced by natural selection, the goal is to optimize prosthetic design parameters including material composition, structure, and control systems. The objective is to create prosthetic limbs that are more personalized to each user's requirements, improving their efficiency, comfort, and functioning via the application of genetic algorithms. The goal of this study is to show that the suggested strategy may improve mobility and user happiness more than standard ways by simulating and testing prosthetic devices in real-world settings. The end goal is to create conditions for a new age of prosthetic technology, where amputees' quality of life is greatly enhanced by devices that are individually designed to meet their biomechanical needs. The impact of prosthetic design and individual patient factors patient dataset derived from a random 5-sample with the following characteristics: ages 32–68, weight 65–90, height 155–180, crossover rate 0.6–0.9, mutation rate 0.05–0.2, population size 70–120, generations 30–60.
Textual and numerical data fusion for depression detection: a machine learning framework Aziz, Mohammad Tarek; Mahmud, Tanjim; Abdul Aziz, Md Faisal Bin; Siddick, Md Abu Bakar; Sharif, Md. Maskat; Hossain, Mohammad Shahadat; Andersson, Karl
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1231-1244

Abstract

Depression, a widespread mood disorder, significantly affects global mental health. To mitigate the risk of recurrence, early detection is crucial. This study explores socioeconomic factors contributing to depression and proposes a novel machine learning (ML)-based framework for its detection. We develop a tailored questionnaire to collect textual and numerical data, followed by rigorous feature selection using methods like backward removal and Pearson’s chi-squared test. A variety of ML algorithms, including random forest (RF), support vector machine (SVM), and logistic regression (LR), are employed to create a predictive classifier. The RF model achieves the highest accuracy of 96.85%, highlighting its effectiveness in identifying depression risk factors. This research advances depression detection by integrating socioeconomic analysis with ML, offering a robust tool for enhancing predictive accuracy and enabling proactive mental health interventions.
Investigation on TiO2/graphene as resistance-based gas sensor for volatile organic compound gases detection Mohd Chachuli, Siti Amaniah; Nor Azmi, Muhammad Haziq; Coban, Omer; Shamsudin, Nur Hazahsha
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp774-782

Abstract

Volatile organic compound (VOC) gases are usually produced from industrial activities. Short-term exposure to VOC gases can cause dizziness, headaches, nausea, and throat irritation. Years to a long time exposure to VOC gases can cause cancer and system damage in the human body. With the growth of gas sensor technology, a resistance-based gas sensor based on various structures of resistance-based gas sensors using Titanium dioxide/graphene (TiO2/graphene) were investigated as a sensing material for detecting volatile organic compound gases, which are acetone and ethanol. The TiO2/graphene gas sensor was deposited on a Kapton film using a screen printing technique. All TiO2/graphene gas sensors were exposed to acetone and ethanol at room operating temperature. The results revealed that the highest response values to acetone and ethanol were produced by T99_G1_2 and T98_G2_1, respectively. It can be concluded that design 1 generated the most consistent response to acetone, while design 2 generated the most consistent response to ethanol.
Exploring parents’ perceptions of sex education pedagogy in Moroccan schools using an association rules mining-based algorithm Ben Azza, Chaymae; El Hamdani, Sara; Bennani, Mohamed Taj; El Fahssi, Khalid; Lamrini, Mohamed; Elfar, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1124-1136

Abstract

Sex education is vital for promoting healthy relationships and preventing sexual exploitation by teaching boundaries, consent and abuse recognition. Customized strategies are needed for children, balancing age-appropriate content with parental and community perspectives. Our study assessed Moroccan parents’ views on sex education’s adoption in schools. Conducted in Taza city, the survey targeted 1946 parents of students over 7 years old. Using association rule mining (ARM), we analyzed their responses. Therefore, Apriori algorithm was implemented to discover strong association rules within parents’ selected responses. Results showed that 74.53% of parents aged 19-30 support sexual education, citing its absence as a factor in child abuse. Meanwhile, 60.48% of those aged 31-59 with university education believe psychological disorders contribute to assaults. While some fathers (32.48%) and some mothers (67.52%) support sexual education, others don’t, but all agree on restricting children’s internet use until age 16 to avoid harmful content. These findings can inform comparative studies, aid decision-makers and enhance AI-based EdTech systems by offering insights into sex education perceptions.
Empowering Malaysian micro agri-entrepreneurs: the role of key success factors in e-agribusiness adoption Tariq, Sehrish; Vaiappuri, Selvakkumar K N; Mahmood, Haider; Houaneb, Amira
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1033-1041

Abstract

Progress in the agricultural sector is one of the most imperative tools to enhance the productivity of agribusiness. This study identified the key success factors required for the adoption of e-agribusiness platforms in the Malaysian agriculture sector. The study analyzed potential key factors from the prior studies and contextually adjusted using a pilot study. These factors are categorized in various categories such as financial imperative, technological imperative, knowledge imperative, risk and trust factors, governance and public policy, and challenging business environment. The study has collected data from 302 micro agri-entrepreneurs within Malaysia through a questionnaire for quantitative analysis. The exploratory factor analysis (EFA) is used to see the impact of critical success factors (CSFs) that help to increase technological adoption thereby enhancing communication, advertisement, and overall sales of agri-products on the e-business platform. The study has a significant impact on key success factors (financial imperative, technological imperative, knowledge imperative, risk and trust factors, governance and public policy, and challenging business environment) on the adoption of e-agribusiness platforms. The findings provide guidelines to micro agri-entrepreneurs and policymakers that how to use key success factors to improve business performance by utilizing e-agribusiness platforms.
Chebyshev distance-embedded twin support vector machine for skewed classification problems Balasubramanian, Sai Lakshmi; Ganesan, Gajendran
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1383-1391

Abstract

Support vector machine (SVM) is a pivotal classification algorithm, and its evolutionary counterpart, the twin SVM (TWSVM), has gained acclaim for its advanced generalization capabilities, particularly in handling imbalanced data. TWSVMs achieve swift training by explicitly exploring a pair of non-parallel hyperplanes, yet selecting numerical values for hyperparameters poses a challenge due to the uncertainty introduced by random preferences. This paper presents a novel approach, the Chebyshev distance-based TWSVM, specifically designed for hyperparameter tuning in imbalanced binary classification. This innovative model mitigates the uncertainty of hyperparameter selection by leveraging Chebyshev distance, thereby enhancing the generalization capabilities of the TWSVM. To evaluate its efficacy, computational tests were conducted on publicly accessible real-world benchmark datasets across various domains, including non-linear cases. The results demonstrate that the Chebyshev distance-based TWSVM outperforms several existing methods, achieving superior performance with reduced computational time and setting a new benchmark in the field.

Page 869 of 914 | Total Record : 9138

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