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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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Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems Rao, Avinash Nagaraja; Sinha, Sitesh Kumar; Mallaiah, Shivamurthaiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1269-1279

Abstract

Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries Noor Zelan, Muhammad Aiman; Hidayat, Muhammad Nabil; Nik Ali, Nik Hakimi; Umair, Muhammad; Mohd Mawardi, Muhammad Izzul; Ahmad, Ahmad Sukri; Abdullah, Ezmin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp783-796

Abstract

This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Design of a segmented current steering digital to analog converter using PMOS cascode current source in UMC 65 nm technology Adepu, Ashok Kumar; Narayanam, Balaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp821-830

Abstract

Digital to analog converters (DAC) are the fundamental data converters used in the digital data transmission. In this paper 8-bit DAC is proposed using current cells with LSB current of 2 µA and full-scale voltage of 420 mV. Current cells mean the current sources designed using the MOSFETs. When it comes to mixed-signal and analog integrated circuits, current cells are the fundamental building blocks that are available. The optimized performance of current source is obtained with the proper biasing circuit. The performance of these current mirrors is evaluated in terms of key parameters such as output impedance, transconductance and linearity. The simulations for testing these parameters are performed using Virtuoso Cadence tool in umc 65 nm technology. After transistor characterization, various types of current sources are designed, and for each current mirror, PVT analysis is carried out for comparison and Monte-Carlo analysis is carried out to find the mismatch in current mirrors. Then different digital blocks are designed, that are D-Latch, Binary-thermo decoder and Row-column decoder which are required for designing of current steering DAC. Creating an 8-bit segmented current steering DAC by combining the ideas of 4-bit unary weighted and 4-bit binary-weighted DACs is the aim of this research. Performance measurements such as signal-to-noise ratio (SNR), effective number of bits (ENOB), spurious-free dynamic range (SFDR), differential non-linearity (DNL), and integral non-linearity (INL) are calculated to assess the proposed 8-bit segmented DAC. The analysis and designing of current mirrors in advanced CMOS technologies are critical for the development of high performance integrated circuits. An 8-bit DAC implemented using ILSB current of 2 µA with an accuracy of ±2%.
Systematic literature review of learning model using augmented reality for generation Z in higher education Zulfachmi, Zulfachmi; Rahim, Normala; Rizhan, Wan; Rahayu, Puji; Saputra, Aggry
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1109-1120

Abstract

Higher education is evolving with innovations aimed at enhancing the quality of learning, and one prominent innovation is the integration of augmented reality (AR) technology into the learning process. AR merges real-world and virtual elements in real-time, creating interactive and immersive educational experiences. This technology supports the display and interaction with virtual objects, enhancing engagement and comprehension among students. However, effective integration of AR in higher education faces challenges such as limited technological infrastructure, the need for skilled lecturers, and the adaptation of teaching methods to suit generation Z's learning preferences. Despite their technological proficiency, many educational institutions struggle to optimally implement innovations like AR. This systematic literature review aims to explore and identify an AR-based learning model suitable for generation Z in higher education. Findings suggest that AR technology can significantly enhance learning by offering engaging visualizations and interactive experiences, aligning well with generation Z's characteristics and learning styles. Effective AR implementation requires suitable platforms, such as mobile, desktop, wearable, and projection platforms, each offering unique benefits. By designing AR learning models that cater to generation Z, educational institutions can improve learning outcomes and experiences.
Enhancing marketing efficiency through data-driven customer segmentation with machine learning approaches Purnamasari, Fanindia; Putri Nasution, Umaya Ramadhani M. O.; Elveny, Marischa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1399-1410

Abstract

The importance of understanding consumer behavior in transaction data has become a key to improving marketing efficiency. This study aims to explore the application of machine learning (ML) techniques for data-driven consumer segmentation, focusing on improving product marketing strategies. This work addresses the limitations in the existing literature, especially in terms of handling high-dimensional data that can reduce segmentation quality. Previously, various studies have used clustering algorithms such as K-means without considering dimensionality reduction, which often leads to decreased accuracy and long computation time. In this study, we propose a new approach that combines principal component analysis (PCA) for dimensionality reduction and K-means clustering for consumer segmentation based on purchasing behavior. Experimental results show that using PCA to reduce data dimensionality significantly improves segmentation quality with an inertia score of 1,455,650 and a silhouette score of 0.486366. By implementing this method, we can group consumers into three segments based on frequently purchased product categories and the most common payment methods. These findings provide a scalable, data-driven segmentation framework that can be applied to improve marketing effectiveness by providing special discounts on various products based on the payment method used.
Advancements and challenges in deep learning techniques for lung disease diagnosis Bagalkot, Laxmi; Kelapati, Kelapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1053-1062

Abstract

This study explores the application of deep learning (DL) techniques in diagnosing lung diseases using screening methods such as Chest X-Rays (CXRs) and computed-tomography (CT) scans. The motivation for this research stems from the need for advanced diagnostic tools in healthcare, with DL showing significant potential in medical image analysis. Despite advancements, challenges such as high costs of CT scans, processing time constraints, image noise, and variability persist. To address these issues, the study conducts a thorough literature survey to identify diverse preprocessing techniques, detection algorithms, and classification models designed for CXR analysis. In conclusion, this work contributes to the advancement of medical imaging technologies by offering innovative solutions, acknowledging existing limitations, and addressing the challenges in lung disease diagnosis. Future research should focus on further refining these techniques and exploring their application in broader clinical settings.
Design and implementation of smart farming prototype with renewable energy and IoT Susanto, Rudi; Lestari, Wiji; Hasanah, Herliyani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1326-1336

Abstract

Indonesia faces food security challenges in several regions, and the adoption of advanced technologies such as artificial intelligence (AI), internet of thing (IoT), and renewable energy in the agricultural sector has not been optimal. This research aims to develop an integrated smart farming system, including monitoring, controlling, and prediction features based on renewable energy to support national food security, especially for chili plants. The method used in the research is an experiment, starting from analysis, design, manufacture, and testing. The result of the research is a smart farming prototype that has been tested with experts, partners and farmers. The results of expert testing obtained that the monitoring feature, in this case the accuracy is 4.36 out of 5 for all sensors, as well as the controlling and prediction features have met technical, functional, and practical needs. The results of the usability evaluation using the system usability scale (SUS) method involving partners and farmers obtained an average SUS score of 73.125. This result is categorized as an excellent rating and can be given a grade B and the acceptance range is high. So, from this study it can be concluded that the smart farming prototype can be used by chili farmers.
A framework for security risk assessment of blockchain-based applications Qatawneh, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp952-962

Abstract

Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.
Development of ResNet-18 architecture to lesion identification in breast ultrasound images Andini, Silfia; Sumijan, Sumijan; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1236-1248

Abstract

Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
IoT-enabled smart healthcare system with machine learning for real-time vital sign monitoring and anomaly detection Deshmukh, Sanjay; Shah, Shrey; Wahedna, Asim; Sabnis, Nimish
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1155-1163

Abstract

This paper presents an innovative IoT-enabled smart healthcare system that combines real-time vital sign monitoring with machine learning-based anomaly detection. The system utilizes a MAX30102 photoplethysmography sensor interfaced with an ESP-32 microcontroller to collect heart rate and blood oxygen saturation (SpO2) data. MQTT protocol ensures efficient data transmission to a cloud database. A long short-term memory (LSTM) neural network architecture is employed for time-series prediction of vital signs and anomaly detection. The system demonstrates high accuracy, with mean squared errors of 0.3% in offline testing and over 90% accuracy in real-time prediction. This affordable and scalable solution offers continuous monitoring capabilities, making it viable for widespread adoption in healthcare settings. The integration of IoT and machine learning techniques provides a robust framework for early detection of health anomalies, potentially improving patient care and outcomes in various medical scenarios.

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