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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 65 Documents
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Search results for , issue "Vol 38, No 3: June 2025" : 65 Documents clear
Vulnerability detection in smart contact using chaos optimization-based DL model Vaddadi, Srinivas A; Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1793-1803

Abstract

This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) for enhancing detection of vulnerability. The evaluation of the suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, the vulnerability detection process utilizes BiLSTM, with the optimization of the loss function achieved through LogT. Results indicate promising performance in identifying vulnerabilities in SC, highlighting the efficacy of the LogT-BiLSTM approach.
Optimization of single electron transistor based digital logic design Gopnarayan, Shobhika Pankaj; Markande, Shriram D.; Raut, Vaishali P
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1553-1563

Abstract

This paper addresses the challenge of high-power consumption and delay in conventional complementary metal-oxide-semiconductor (CMOS) circuits, particularly in the design of digital logic gates. The objective is to develop a hybrid CMOS-single-electron transistor (SET) model that reduces power consumption while maintaining acceptable performance in terms of delay. The proposed model leverages coulomb oscillation in SETs to create a changeable transconductance area, which significantly reduces energy usage. Simulation results demonstrates that the hybrid CMOS-SET circuits achieve up to 30% lower power dissipation compared to traditional CMOS designs, although a slight increase in delay is observed in complex gates like the OR gate. The novelty of this work lies in its use of coulomb oscillation for dynamic transconductance control, providing an innovative approach to balancing power efficiency and speed in nano-scale digital circuits. This makes the proposed model a promising candidate for future low-power, high-performance integrated circuits.
Segmentation and classification of plant leaf disease using advanced deep learning approach and ensemble classifier Huddar, Suma S.; Rudagi, Jayashri; Jakati, Jagadish S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1489-1502

Abstract

An essential component of maintaining global food production is plants. On other hand, a number of plant diseases can threaten agricultural output and cause large losses if left unchecked. Agricultural specialists and botanists physically track plant diseases in a labor-intensive, error-prone manner using a conventional method. AI can give evaluations that are quicker and more accurate than those made using conventional approaches by automating the identification and analysis of diseases. This technical development presents a viable way to lessen crop losses and lessen the severity of infections. As a result, we describe an ensemble machine learning strategy for plant disease classification in this study that is enabled by deep learning. Data augmentation is done in the first part of the study, and in the second step, we provide a modified Mask R-CNN model for plant leaf segmentation. Afterwards, a model to extract the deep features based on CNN is shown. Lastly, the ensemble classifier is built using support vector machine classifier (SVM), random forest (RF), and decision tree (DT) with the aid of majority voting. The suggested method's effectiveness is tested on plant village, apple, maize, and rice, yielding overall accuracy values of 99.45%, 96.30%, 96.85%, and 98.25%, in that order.
A comprehensive access control model integrating zero trust architecture Jyosthna, Pattabhi Mary; Reddy, Konala Thammi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1896-1904

Abstract

In contemporary IT landscapes, trust in entities, whether internal or external, within organizations has become obsolete. Establishing and enforcing strict access controls, alongside continuous verification, is imperative to safeguard organizational resources from potential insider and outsider threats. The emergence of zero trust architecture (ZTA) addresses this need by advocating for a paradigm shift in security. This research proposes a comprehensive access control model aligned with the fundamental ZTA security principles, namely least privilege, conditional access, and continuous monitoring. The model integrates well-established access control paradigms, including role-based access control (RBAC) to uphold the least privilege principle, attribute-based access control (ABAC) to support conditional access, and trust-based access control (TBAC) to enable continuous monitoring. To determine the trust level of a user requesting access, an analysis of the user's log activities is conducted using the Nmedian outlier detection (NMOD) technique. This analysis aids in evaluating the trustworthiness of the user seeking access to resources. Furthermore, this research assesses the efficiency and efficacy of the proposed integrated access control model in comparison to existing access control models, primarily focusing on their respective functionalities.
Design and analysis for robotic arm position for automatic electric vehicle Kharde, Mukund Ramdas; Kalam, Sayyad Abdul; Teku, Kalyani; Reddy, Thumu Srinivas; Satya Srinivas, Gollapalli Veera; Kollamudi, Pavani; Fariddin, Shaik Baba; Kumar, Gopinati Pranay
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1517-1526

Abstract

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.
Implementation of an app-controlled robotic arm to optimize loading processes in Callao-Peru Gómez-Huamán, Javier Junior; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1591-1601

Abstract

Efficient handling of heavy loads represents a constant challenge for businesses, which traditionally rely on significant numbers of staff, involving considerable financial costs and occupational health risks exacerbated by the need for specialized infrastructure. Despite technological limitations and structural deficiencies, this solution has prevailed in practice. However, engineering has responded with innovations aimed at optimizing these processes. In this context, the study proposes to adopt an approach based on implementing a robotic arm supported by technologies such as Arduino, Bluetooth devices, servo motors, and remote-control software developed in App Inventor. This approach promises not only the reduction of labor costs and the improvement of job security but also a positive impact in social and economic terms. A preliminary prototype is presented that validates the basic functionality of the proposed robotic arm. This study presents a technically and economically viable alternative for managing heavy loads in enterprise environments, reducing dependence on a large workforce, and improving operational efficiency.
A comparative study on electricity load forecasting using statistical and deep learning approaches Butt, Tehreem Fatima; Tameer, Sana; Saleem, Muhammad; Ur Rehman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1540-1552

Abstract

Load forecasting has become reproving aspect of an energy management system (EMS). It gives basic advantage to grid stability, cost effectiveness and battery storage system (BSS). For this purpose, machine learning (ML) is widely adopted to forecast the electricity load. This research paper investigates the performances of various time series estimating models applied to electricity load data for an Irish company. The research mainly adopts the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) networks and transformer neural network (TNN) to forecast the electricity load. A comparison evaluation is conducted encompassing various quantifying measures such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The results are then compared to get an understanding whether the TNN using attention-based mechanism is better than the two state of the art models. Hence provides a complete understanding about which of the model needs improvements in its architecture for enhancement of operational efficiency and cost effectiveness in the realm of EMS.
Influences from SiO2 particles on optical properties of white diodes verified through computer simulation Trang, Le Thi; Quoc Anh, Nguyen Doan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1572-1579

Abstract

For typical white-illumination diodes (WLEDs) based on phosphor conversion, heat consistency would be an essential element in phosphor samples, which is based on consistent discharge intensity, apex profile, as well as location as the samples function under different heat levels. With the goal of attaining desirable heat consistency, the study herein concerns the thermic mechanism in different phosphor samples singularly or dualincorporated with Ce3+ and Eu2+. Based on our acquired data, the luminescent features for the samples exhibit copious alterations when subject to different heat levels, primarily decided by phosphor bases’ crystalline formation. The assessment of the interaction among the thermic mechanism and base latticework in the samples suggest that a merger between firm crystalline formations and symmetrical locations would result in desirable thermic consistency in samples. As such, the study herein also assesses a number of formations possessing firm foundations as well as specific approaches for avoiding thermic irregularities in phosphor samples, aiming to identify reliable samples as well as approaches for augmenting heat consistency for said samples.
Speed drives control using particle swarm optimization for PMSM drives Mat Lazi, Jurifa; Nizam Talib, Md Hairul; Bin Kasdirin, Hyreil Anuar; Bin Hashim, Mohd Ruzaini; Alias, Azrita
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1440-1449

Abstract

The paper presents a contemporary method for controlling the speed of a permanent magnet synchronous machine (PMSM) by optimizing the parameters of a proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. This approach aims to enhance the robustness and dynamic performance of the drive system, resulting in improved accuracy and sensitivity to load changes and wide range of speed. The study evaluates two tuning techniques for the PI controller, which are the traditional trial-and-error method and the PSO optimization method. The performance of the PMSM is assessed based on speed response performance, including rise time, overshoot, and settling time. The PSOtuned controller significantly minimizes overshoot compared to the trialand-error method. And also achieves a shorter settling time, indicating a more stable response. However, the rise time is slightly longer with the PSO-tuned controller compared to the conventional tuning method just for the medium speed. For the rated speed, PSO still having shorter rise time compared to trial-and-error PI method. These findings imply that while the PSO method may result in a longer rise time, its overall advantages in reducing overshoot and settling time make it a more effective option for speed control in PMSMs. This is consistent with other research suggesting that PSO can outperform traditional methods in optimizing control parameters across various applications.
Advancing SSVEP-based brain-computer interfaces: a novel approach using cross-subject multi-modal fusion technique Swetha, Kalenahally R.; Krishnegowda, Ravikumar G.; Venkataramu, Shashikala S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1755-1764

Abstract

Brain-computer interfaces (BCIs) represent an innovative paradigm for device control and communication, relying solely on the analysis of brain activity. Steady-state visually evoked potentials (SSVEPs), characterized by neurophysiological responses synchronized with periodic visual stimuli, have gained prominence in BCI research due to their high information transfer rates (ITRs) and minimal user training requirements. However, the translation of SSVEP-based BCIs into practical applications faces challenges stemming from variations in user responses and stimuli. To address these issues, this study introduces a groundbreaking methodology known as the cross-subject multi-modal fusion technique (CMFT). CMFT revolutionizes template design by creating invariant templates resilient to user and stimulus differences, thereby enhancing SSVEP detection across diverse subjects and stimuli. The implications of this research extend to various fields, including assistive technologies, human-computer interaction, and cognitive neuroscience. CMFT presents a promising solution to make SSVEP-based BCIs more practical and widely applicable. The methodology involves intricate steps, including spatial filters, data pre-processing, and template generation, ensuring precise SSVEP detection. Through CMFT, this study contributes to advancing the effectiveness and versatility of SSVEP-based BCIs, fostering improved accessibility and interaction in a range of domains.

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