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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 9,338 Documents
 930   931   932   933   934 
Confidence-driven adaptive operating-point optimization for photovoltaic systems under partial shading conditions Sarah Kawther Sedjar; Mourad Benmessaoud
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp672-682

Abstract

Partial shading conditions (PSC) generate highly nonlinear multi-peak photo voltaic (PV) characteristics, complicating reliable global maximum power point tracking (GMPP). Although numerous intelligent optimization techniques exist, most rely on extensive exploration mechanisms that limit their applicability in embedded real-time controllers. This paper introduces a confidence-driven adaptive maximum power point tracking (MPPT) framework in which the optimization search space is dynamically regulated according to the reliability of a predictive operating-region estimator. Unlike standard artificial neural network (ANN)-assisted MPPT strategies that offer only power prediction, the proposed approach exploits a confidence index to continuously contract or expand the exploration domain, minimizing search effort while preserving global tracking capability. The framework was developed using real measurements from the PV DAQ database and validated through a nonlinear two-diode thermal-electrical PV model incorporating irradiance mismatch and temperature-dependent effects. Static and dynamic PSC scenarios were investigated to evaluate convergence behavior and computational performance. Experimental results demonstrate that the proposed confidence-governed strategy achieves an average tracking efficiency of 90.89%, reduces convergence effort through adaptive search-space contraction, and matches real measurements with an R2 value of 0.8664, offering a low-complexity solution for real-time embedded PV energy management.
OCTModNet: a deep learning-based framework for optical coherence tomography image multi-class classification Saja Ataallah Muhammed; Abbas M. Ali; Dler Salih Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp495-506

Abstract

Sight is one of the most significant senses in human beings. Losing sight can change a human’s life dramatically. Early diagnosis and detection of retinal diseases will prevent sight loss. Optical coherence tomography (OCT) imaging is helpful in ocular imaging with its high resolution and non invasive imaging for diagnosing retinal diseases. This study proposes OCTModNet, a novel multi-class hybrid classification model that is capable of classifying seven different retinal diseases using a combination of publicly available datasets, Kaggle OCT-C8, and OCTDL datasets. For effective extraction of the significant features, the collected data is resized and normalized. Augmentation techniques are used to improve the training and learning process. For model development, we applied transfer learning and fine-tuned several state-of-the-art deep learning models, ResNet50, InceptionV3, DenseNet121, VGG16, and EfficientNetV2S. Two models that performed best, VGG16 and EfficientNetV2S, were selected as the model backbones with integration of a squeeze and excitation block. The results showed that the OCTModNet achieved an outstanding performance with 99% test accuracy, 99% precision, 99% recall, 99% F1-score, and an area under the curve (AUC) as 99% across the unseen data. These results point out the robustness and reliability of the proposed model to classify OCT images and have the potential to enhance clinical decision-making and assist ophthalmologists in the early detection of diseases.
Coordinated multi-battery control for single-stage islanded AC microgrids Adhi Kusmantoro; Lukman Harun
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp384-399

Abstract

Reliable power management is essential for islanded AC microgrids integrating photovoltaic (PV) generation and battery energy storage, particularly under variable solar irradiance and load conditions. This paper proposes a coordinated multi-battery control strategy using a single-stage AC-coupled configuration to ensure uninterrupted power supply while improving system reliability. The proposed topology employs two PV arrays: one directly connected to a battery inverter to supply the AC load and another dedicated to battery charging. Four battery units are coordinated through a fuzzy logic controller (FLC), which sequentially regulates battery discharge based on PV generation and load demand. Simulation studies were conducted under two operating scenarios. In the first scenario, at a solar irradiance of 1000 W/m², the PV system generated approximately 1200 W, and the proposed controller maintained power balance as the load demand increased. In the second scenario, when PV output decreased due to reduced solar irradiance or complete source interruption, the FLC coordinated battery operation at 0.07 s, 0.34 s, 0.64 s, and 0.93 s, ensuring continuous power delivery to the load. The simulation results demonstrate that the proposed coordinated control strategy effectively enhances power continuity, operational stability, and energy management in single-stage islanded AC microgrids.
A comprehensive review of memory BIST algorithms for SRAM static fault detection Aiman Zakwan Jidin; Razaidi Hussin; Mohd Syafiq Mispan; Lee Weng Fook; Loh Wan Ying
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp400-412

Abstract

Memory built-in self-test (MBIST) has become an essential design-for testability technique for ensuring the reliability and quality of embedded memories in modern integrated circuits. The effectiveness of an MBIST implementation is largely determined by its test algorithm, which defines the sequence of memory operations, directly influencing both test complexity and fault coverage. Designing an efficient test algorithm requires balancing low test complexity with comprehensive fault detection. This paper presents a comprehensive review of MBIST test algorithms for static fault detection in static random-access memory (SRAM). The review first summarizes the characteristics of major SRAM static faults and their corresponding detection requirements. It then compares representative test algorithms in terms of test sequence, computational complexity, fault coverage, and design methodology. Furthermore, the evolution of MBIST test algorithm development is discussed, ranging from conventional ad hoc approaches to enhanced algorithms derived from existing March tests. The comparative analysis indicates that an 18N-complexity test algorithm is generally required to achieve complete detection of all unlinked static faults in SRAM, whereas optimized 14N-complexity algorithms provide an effective trade-off between test time and fault coverage. Finally, the review identifies current research challenges, including efficient detection of dynamic and linked memory faults and the development of MBIST algorithms for emerging memory technologies such as magnetic random-access memory (MRAM), highlighting promising directions for future research.
An overview of SSP structures in dragon graphs and domination and independent domination fuzzy SSP graphs R. Mary Jeya Jothi
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp485-494

Abstract

Social network modeling is one of the many applications of dominant sets in graph theory. Social networks are made up of personalities or sets of individuals linked by relationships; graph theory provides a useful framework for examining and simulating these structures. We have already covered the idea of domination and how to calculate the domination numeral for various fuzzy graphs. Unlike anything found in the literature to date, we presented the idea of domination of fuzzy SSP graphs in this study. It is also spoken about the several super strongly perfect (SSP) structural parameters in fuzzy SSP graphs. The study appears to contribute to the ground of theory of graphs by extending the notion of domination to fuzzy SSP graphs. The paper also covers how to acquire specific dominant sets, coloring numbers, and cliques (maximal) in the context of fuzzy SSP graphs. In these kinds of scenarios, researchers can employ the following broad techniques to identify dominating sets.
Affine-invariant feature learning for accurate ulcer detection in wireless capsule endoscopy images S. Bhuvaneswari; M. Sulthan Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp595-606

Abstract

Ulcers are lesions that develop in the lining of the gastrointestinal (GI) tract, particularly in the stomach and small intestine, and may lead to severe complications such as Crohn’s disease and ulcerative colitis if not detected at an early stage. Conventional endoscopic procedures are often uncomfortable for patients and may provide limited visualization of the entire small intestine. Wireless capsule endoscopy (WCE) has emerged as a non-invasive alternative for comprehensive GI tract examination; however, automated ulcer detection from WCE images remains challenging due to image noise, complex tissue structures, and computational requirements. To address these issues, this paper proposes a Camargo’s Indexive Kuwahara filtering-based affine-invariant sliced regression (CIKF-AISR) framework for accurate and efficient ulcer detection. The proposed framework consists of image acquisition, preprocessing, segmentation, and feature extraction stages. Adaptive CIKF is employed to suppress noise while preserving edge information. Subsequently, Von Neumann locality segmentation combined with the Canberra distance measure is utilized to identify regions of interest (ROIs). Finally, affine-invariant saliency sliced regression extracts discriminative shape, color, and texture features for ulcer detection. Experimental evaluation on the Hyper-Kvasir dataset demonstrates that the proposed method achieves higher ulcer detection accuracy, improved precision, enhanced peak signal-to-noise ratio (PSNR), and lower detection time compared with existing deep CNN and VAE-GAN approaches. These results confirm the effectiveness of the proposed framework for computer-aided GI diagnosis.
Advanced personal bankruptcy prediction using tree-based deep learning models Nhat Nguyen Minh; Duy Ngo Hoang Khanh
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp640-650

Abstract

Due to the unstable economic conditions, worsened by the post-COVID-19 environment and persistent foreign wars in 2024, financial institutions have growing difficulties in accurately predicting customer default probability. This study examines the use of sophisticated tree-based deep learning and deep neural network models for forecasting personal bankruptcy. This research utilises a dataset of roughly 9,800 individuals from Vietnamese financial institutions, spanning from 2012 to 2022, to evaluate the efficacy of models including neural decision tree, deep forest, tabular convolutional neural networks (TBCNN), and neural oblivious decision ensembles (NODE). The results demonstrate that the Deep Forest model far surpasses its competitors, providing nearly flawless predicted accuracy and enhanced interpretability. The findings highlight the efficacy of tree-based deep learning and deep neural network models as effective instruments for financial risk management, especially in volatile and unpredictable economic environments.
Transformer-based sentiment modeling for identifying cross country fintech perception gaps Kayla Zhafira Ardinov; Muhardi Saputra; Riska Yanu Fa’rifah
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp522-533

Abstract

Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.

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