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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
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Evaluating low-cost internet of things and artificial intelligence in agriculture Elhattab, Kamal; Elatar, Said
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp968-975

Abstract

This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.
Single search investigation of various searches in recent swarm-based metaheuristics Kusuma, Purba Daru; Dinimaharawati, Ashri
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp186-196

Abstract

Swarm intelligence has become a popular framework for developing new metaheuristics or stochastic optimization methods in recent years. Many swarm-based metaheuristics are developed by employing multiple searches whether it is conducted through swarm split, serial searches, stochastic choose. Unfortunately, many existing studies that introduced new metaheuristic focused on assessing the performance of the proposed method as a single package. On the other hand, the contribution of each search constructing the metaheuristic is still unknown as the consequence of the missing of single or individual search assessment. Based on this problem, this work is aimed to investigate the performance of five directed searches that are commonly found in recent swarm-based metaheuristics individually. These five searches include: motion toward the highest quality member, motion relative to a randomly chosen member, motion relative to a random solution along the space, motion toward a randomly chosen higher quality member, and motion toward the middle among higher quality members. In this assessment, these five searches are challenged to find the optimal solution of 23 classic functions. The result shows that the first, fourth, and five searches perform better than the second and third searches.
AI in Moroccan education: evaluating student acceptance using machine learning classification models Mohamed, Khoual; Zineb, Elkaimbillah; Zineb, Mcharfi; Bouchra, El Asri
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp452-462

Abstract

Personalized learning is becoming a reality in education thanks to the rise of AI. This study investigates the possibilities of AI within the realm of education, focusing on the individualization of the learning experience. The research is based on the responses of 395 students from various faculties in Morocco. The questionnaire aimed to assess the students’ opinions of AI, their level of knowledge, their previous experiences, and their perception of the application of AI within educational settings. Employing classification techniques such as decision trees (DT), multilayer perceptron (MLP), and random forests (RF), our aim was to predict the receptivity of AI in education. The findings highlight significant differences in how Moroccan students perceive AI, identifying key factors such as familiarity with the technology, ethical concerns, and perception of its potential impact on the learning experience. Classification models showed varied performance in anticipating these attitudes. This study highlights the critical importance of understanding students’ perspectives on AI in education. These findings offer crucial insights for education policymakers as well as designers of educational technology solutions in Morocco. The findings can be used as a guide to adapt the incorporation of AI into the education sector with discernment, taking into account students’ perceptions and preferences.
Design of starting a three phase induction motor using direct on-line, variable frequency drive, soft starting, and auto transformer methods Siregar, Yulianta; Rotua Oktaviana Siahaan, Yosephine; Nabila Binti Mohamed, Nur; Candra Riawan, Dedet; Yuhendri, Muldi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp700-714

Abstract

The problem with 3-phase induction motors is that when starting the motor, the motor starting current can reach five to seven times the nominal current. This research compares slip, starting current, bus voltage, acceleration torque, motor torque, energy savings, and kVAR from the direct on-line (DOL), variable frequency drive (VFD), soft starting, and autotransformer starting methods in the electrical transient analyzer program (ETAP) software. This research result shows that the fastest VFD slip reaches a steady state, namely at 11+ seconds. The lowest starting/starting current is owned by the VFD method, namely <20% full load amps (FLA) in the first 2 seconds. The lowest decrease in bus voltage at steady state was experienced by the VFD method, namely 0.8152%. The quickest acceleration torque reaches a steady state in the VFD method, namely in 11+ seconds. The soft starting method owns the lowest starting torque, namely 20.75%. The soft starting method has the largest energy savings, namely 148.02 kW. Of the several variables observed, the best starting method is the VFD method.
Privacy preserving ZK-STARK based blockchain for agriculture food supply chain Arade, Madhuri Sadashiv; Pise, Nitin N.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1102-1111

Abstract

The blockchain-based applications such as food supply chain (FSC), healthcare are becoming increasingly popular due to their decentralized nature, distributed structure, and the ability to track products. Still, there are concerns regarding the privacy of transacted data, including personal identities, as all transactions are recorded on a ledger accessible to participating nodes. Existing technologies of privacy preservation are vulnerable to quantum attacks, which will pose a significant threat to blockchain applications in the future. To address this, a proposed model uses modified zero-knowledge scalable transparent argument of knowledge (ZK-STARK) in the blockchain FSC by utilizing three different polynomial interpolation methods. Performance measurements indicate that the fast fourier transform (FFT) outperforms the others. Unlike ZK-SNARK, ZK-STARK does not require a trusted setup, makes scalable and transparent. By defending against quantum attacks, this model enhances the security of the blockchain system. The blockchain-based FSC is implemented using hyperledger composer, with all entities completing transactions privately through ZK-STARK and smart contracts. But, ZK-STARK may add performance overhead into the blockchain FSC. Future work will aim to reduce the performance overhead of ZK-STARK, decide which operations should be off-chain or on-chain, and compare the performance of this new model to the existing system.
Comparative analysis of whale and Harris Hawks optimization for feature selection in intrusion detection Abualhaj, Mosleh M.; Hiari, Mohammad O.; Alsaaidah, Adeeb; Al-Zyoud, Mahran M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp179-185

Abstract

This research paper explores the efficacy of two nature-inspired optimization algorithms, the whale optimization algorithm (WOA) and Harris Hawks optimization (HHO), for feature selection in the context of intrusion detection and prevention systems (IDPS). Leveraging the NSL-KDD dataset as a benchmark, our study employs Python for implementation and uses decision tree (DT) as the classification model. The objective is to assess the impact of the HHO and WOA optimization techniques on the performance of IDPS through feature selection. The WOA and HHO techniques were able to lessen the features from 40 to 16 and 13, respectively. Results indicate that DT integrated with HHO achieves an impressive accuracy of 97.59%, outperforming the WOA-enhanced model, which attains an accuracy of 97.5%. This study contributes valuable insights into the comparative effectiveness of WOA and HHO optimization algorithms in enhancing the accuracy of IDPSs, shedding light on their potential applications in the realm of cybersecurity.
Multi-class chronic lung disease classification based on guided backpropagation convolutional neural network using chest X-ray images Raj, Rakesh Selva; Madalu Palakshamurthy, Pavan Kumar; Rangaswamy, Bidarakere Eswarappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1328-1338

Abstract

Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to the complexity of pathological abnormalities and detailed annotation, the computer-aided diagnosis of lung diseases is challenging. To overcome this challenge, this research proposes the guided backpropagation convolutional neural network (GBPCNN) for the classification of chronic lung disease into 14 classes, by adjusting the network’s weights in CNN layers. The GBP technique enhances result accuracy by pinpointing the regions in an input image. Initially, the chest X-ray radiography (CXR) dataset is collected for estimating the effectiveness of the classifier. After collecting the dataset, the pre-processing is performed by utilizing image denoising using gaussian filter and normalization techniques. Then, the pre-processed data is fed to the feature extraction process, and it is done by using EfficientNetB2. Finally, extracted features are provided to the classification process to categorize chronic lung disease into 14 classes. The experimental results show that the proposed GBPCNN method attains better results and it achieves the accuracy of 97.94% as compared to the existing approaches like MobileLungNetV2 and CX-Ultranet. These results highlight the potential of our approach for clinical applications.
Information system success model: continuous intention on users’ perception of e-learning satisfaction Fiati, Rina; Widowati, Widowati; Kusumo Nugraheni, Dinar Mutiara
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp389-397

Abstract

The information systems success strategy contributes to understanding of digitalization. This research aims to evaluate user satisfaction with the e-learning system continuously. The research method is hybrid, combining constructs of a unified theory of acceptance and use of technology, the technology acceptance model, and service quality (SVQ). Data collection was conducted through the distribution of questionnaires targeting instructors. Data analysis utilized structural equation modeling with partial least squares. This method was used to test the measurement model with factor loadings and average variance extracted (AVE) above 0.5. Meanwhile, validity testing on cross-loading had indicator values for each variable higher than other variables, with composite reliability above 0.7. These results were supported by hypothesis testing, which indicated that website quality positively influences user satisfaction, leading to sustained intention. The original sample obtained a value of 0.633; mean of 0.624; standard deviation of 0.105, and a p-value below 0.01. Additionally, user subjective norms have a strong relationship between sustained intention and system appropriateness, of 0.763 in using e-learning.
Innovative virtual reality solutions for technical training in heavy construction equipment repair and maintenance Istiono, Wirawan; Wira Pratama, Andhika Nugraha
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp627-635

Abstract

The construction industry is significantly impacted by heavy construction equipment, including bulldozers, excavators, and vehicles. This equipment speeds up building, moves supplies, and builds infrastructure. Using heavy construction equipment correctly can boost productivity and shorten project timelines. Due to their complexity and scale, this equipment must be maintained and repaired. Poor maintenance and repair of heavy construction equipment can reduce performance, damage, and even cause accidents. Due to these problems, this study focuses on the design and development of a simulation training application to enhance the technical skills of workers in maintaining and repairing heavy construction equipment using virtual reality (VR) technology, the development of this application will be carried out using Unreal Engine 5 and thereafter tested and implemented at PT Menara Indonesia or M-Knows Consulting, Indonesia. At the end of this study, the design and development of a VR training simulation application for heavy equipment repair has been successfully completed. After testing the VR application and conducting user acceptance tests, it was concluded that the created VR application greatly assists M-Knows Consulting in training workers to perform maintenance and repair on heavy equipment, with a user acceptance rate of 84%.
Design and evaluation of performance metrics of a pentaband broadband microstrip patch antenna for mm wave applications Jana, Subhasis; Kumar Singh, Raj; Mamta, Kumari
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp859-866

Abstract

This paper reports design and results of a microstrip patch antenna for broadband application in the millimeter wave communication with multiband features. Electromagnetic solver high-frequency structure simulator (HFSS) is employed to measure the effectiveness of the electromagnetic properties and electrical behaviour of the antenna. The proposed microstrip patch antenna (MPA) can be easily fabricated on a single substrate using standard photolithography process to attach the radiating element and feed lines to the dielectric material. On a 4.93 mm×5.86 mm metallic patch, over FR4 epoxy substrate with dielectric constant 4.4 and loss tangent 0.03, two L-shaped slots are placed along with a few micro slots of varied dimensions, and the antenna is fed with microstrip feedline with resistive load termination of 50 Ω. Pentaband resonant frequencies are realized in the K-band at 13.6 GHz, 23.2 GHz, 29.68 GHz, 32.96 GHz, and 38.56 GHz, with minimum return loss of -23.17 dB, bandwidth 2.32 GHz, omnidirectional radiation pattern, and maximum reported gain of 4.5 dB. The designed antenna achieved good electromagnetic radiation properties and electrical behaviour, and is a good choice for broadcasting over short distances, surveillance and monitoring, wireless sensor backhauls and telecommunication in the K-band networks.

Page 854 of 914 | Total Record : 9138

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