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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 
Low-power high-speed FinFET DRAM array using sleep transistors N Praveena; N Shylashree
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.pp413-424

Abstract

Dynamic random-access memory (DRAM) is a fundamental memory technology widely employed in modern digital systems because of its high storage density and simple cell structure. However, conventional DRAM cells suffer from considerable power dissipation and propagation delay, which limit their suitability for high-speed and low-power applications. This paper presents three novel FinFET-based DRAM architectures incorporating sleep transistor techniques to reduce power consumption while improving operating speed. The proposed designs include a 2T-DRAM with sleep transistors and two configurations of 3T-DRAM with sleep transistors. The FinFET technology enhances switching performance and reduces propagation delay, whereas the sleep transistor technique effectively suppresses leakage, dynamic, and short-circuit power during memory operations. The proposed DRAM cells are designed and evaluated using the cadence virtuoso analog design environment. Simulation results demonstrate significant improvements over conventional DRAM architectures. The proposed 2T-DRAM achieves a 66% reduction in write delay, while the proposed 3T-DRAM achieves up to a 98% reduction in read delay. Furthermore, write power consumption is reduced by 56.8%, 63.02%, and 99.6% for the 2T, 3T-B, and 3T-C configurations, respectively. During read operations, power consumption is reduced by 99.8%, 99.5%, and 99.8%, respectively. These results demonstrate that the proposed FinFET DRAM architectures provide an effective solution for high-speed, low-power embedded memory applications.
Simulation of a frequency-reconfigurable multiband antenna for 5G and Wi‑Fi 6E/7 applications Ismahane Refsi; Miloud Benchehima; Mohammed Hicham Hachemi; Salah Eddine Brezini
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.pp439-449

Abstract

This paper presents a compact frequency-reconfigurable multiband antenna design suitable for 5G and Wi‑Fi 6E/7 applications. Designed on a low-cost FR4 substrate, the antenna occupies a compact footprint of 28×15×1.6 mm, making it one of the smallest designs reported and suitable for integration into space-constrained devices. Its simple geometry enables easy design and integration across platforms. Frequency reconfigurability is achieved using PIN-diode-controlled reactive elements that manipulate current distribution to shift resonant frequencies. The design and optimization were performed using CST Microwave Studio to ensure accurate electromagnetic performance. Simulation results demonstrate that the proposed design supports up to eight distinct operating modes, highlighting its versatile frequency-reconfigurable capability. Among these modes, some achieve excellent performance at specific frequency bands including Wi‑Fi 6E/7 (2.4, 5 and 6 GHz) and 5G sub-6 GHz (3.5 GHz). These findings are confirmed with simulation results showing clearly that the antenna exhibits S_11 below –20 dB across the targeted frequency bands. The antenna also demonstrates satisfactory gain, with values above 1.5 dBi across the desired frequency bands. The antenna reveals a voltage standing wave ratio (VSWR) below 1.5 across the intended frequency bands. These characteristics make the proposed antenna a compact, versatile and efficient solution for current and future wireless communication systems.
Evaluation of hybrid parallelism for scalable training of DenseNet-121 in diabetic retinopathy classification Indar Sugiarto; Djoni Haryadi Setiabudi; Darrell Cornelius Rivaldo; Taweesak Kijkanjanarat; Resmana Lim
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.pp662-671

Abstract

Training large and complex deep learning models is often constrained by GPU memory limitations and prolonged training times. While several parallelism strategies have been proposed, this study specifically evaluates hybrid parallelism—a combination of data parallelism and pipeline parallelism—to address both challenges simultaneously. Using a case study on diabetic retinopathy (DR) classification with the DenseNet-121 architecture, we analyze the trade-off between computational efficiency and memory scalability. Results show that although hybrid parallelism does not yet provide speedup compared to a single-GPU setup—due to communication overhead and pipeline fragmentation—it enables training of large models that exceed the memory capacity of a single GPU. The trained model achieved a validation accuracy of 0.737, a quadratic weighted kappa (QWK) of 0.861, and a weighted F1-score of 0.749. In contrast, pure data parallelism showed a potential speedup of up to 1.9× in scenarios where the model still fits within a single GPU. These findings highlight the critical role of hybrid parallelism in overcoming the memory wall in large-scale model training, though optimization to reduce overhead remains a key challenge.
PID-based performance optimization of ultra-wideband microstrip patch antennas for indoor positioning systems Fredelino A. Galleto Jr.; Aaron Don M. Africa; Bettina Gaille H. Dayrit; Gia Kyla S. Guevarra; Chrismon Elijah Q. Mansilungan; Michael Angelo M. Obciana; Mariah Venice A. Rodriguez; Keane Dwight A. Sulit
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.pp450-459

Abstract

Ultra-wideband (UWB) technology has become a key enabler for high accuracy indoor positioning systems (IPSs), where antenna performance directly influences localization accuracy, signal quality, and communication reliability. However, designing compact UWB microstrip patch antennas with wide bandwidth, low reflection loss, and stable radiation characteristics remains a significant challenge. This paper presents a PID-based performance optimization approach for UWB microstrip patch antennas to improve antenna characteristics for indoor positioning applications. The proposed methodology integrates MATLAB-based electromagnetic simulation with parameter optimization to refine antenna geometry while incorporating optimization concepts inspired by rough set theory and fuzzy logic to support efficient design parameter selection. Using MATLAB and the Parallel Computing Toolbox, the proposed approach significantly reduces computational complexity while accelerating the optimization process. Experimental results demonstrate substantial improvements in reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern, and antenna directivity, particularly around the target operating frequency of 10 GHz. Among the evaluated configurations, the optimized rectangular microstrip patch antenna consistently outperformed the triangular design in terms of impedance matching and radiation performance. The proposed optimization framework provides an effective and computationally efficient solution for enhancing UWB antenna performance, making it well suited for high-precision indoor positioning and next-generation wireless communication systems.
An artificial neural network-based decision support model for early prediction of mathematics learning challenges using the CRISP DM framework Harry Dhika; Surajiyo Surajiyo; Lasia Agustina; Abdul Muchlis
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.pp618-627

Abstract

Identifying mathematics learning difficulties remains a challenge for educators due to the subjectivity and inefficiency of conventional methods in capturing psychological factors. To address this limitation, this study proposes an artificial neural network (ANN)-based decision support model developed within the cross-industry standard process for data mining (CRISP-DM) framework. The model integrates 16 academic indicators (quizzes, exams, remedial frequency, online activity) and psychological factors (motivation, anxiety, self-confidence, interest) from 163 student records at SMA Muhammadiyah 16 Jakarta. Synthetic minority over-sampling technique (SMOTE) and focal loss are applied to handle class imbalance and improve reliability. The proposed model achieves 98% accuracy and a 0.97 F1-score in classifying students into three difficulty levels: Easy, moderate, and difficult. These findings demonstrate the model’s effectiveness in capturing complex relationships between cognitive and affective features. Unlike prior studies that rely solely on academic performance, this work contributes a robust, comprehensive data-driven framework that enhances multiclass classification for early educational intervention within the Indonesian context.
Improved sizing of a damped passive filter for harmonic attenuation using a teaching-learning-based optimization algorithm for an industrial site Brahimi Omar; Rabah Djekidel; Hadjadj Abdechafik; Sid Ahmed Bessedik
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.pp363-383

Abstract

Based on measurements of current harmonics that adversely affect power quality in an industrial setting, and to ensure compliance with permissible levels according to IEEE 519, this paper aims to compare the effectiveness of conventional and optimized sizing of damped passive filter components to reduce total harmonic distortion (THD) and improve the power factor (PF). The optimization of passive filter element values relies on a powerful metaheuristic algorithm called teaching-learning-based optimization (TLBO). Simulation results show that the THD of current and voltage reach values of 17.34% and 8.61%, respectively, and decrease to very low values after implementation of the damped passive filter in the optimal case using the TLBO algorithm. The results also demonstrated a proportional relationship between the harmonic currents in the load and the overall increase in losses in the transformer. Optimizing the damped passive filter reduced harmonic distortion, improved the PF, and limited energy losses, thus confirming the superior ability of this algorithm to identify a very high efficiency solution.
Hybrid deep learning-based congestion prediction for intelligent traffic management in 5G/6G networks T. Anuradha; Giribabu Sadineni; Laxmi Pamulaparthy; M. L. M. Prasad; N. Vijay; Rajamahendravarapu Lakshmi Durga
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.pp472-484

Abstract

Future-generation communication networks, including millimeter-wave local area networks, broadband wireless access systems, and emerging fifth- and sixth-generation (5G/6G) networks, require intelligent traffic management to satisfy stringent quality of service (QoS) requirements, including ultra-low latency, high reliability, and massive device connectivity. As network traffic becomes increasingly dynamic and heterogeneous, accurate congestion prediction is essential for preventing resource overloading, maintaining network slicing performance, and ensuring efficient resource utilization. This paper proposes a hybrid deep learning (DL)-based congestion prediction model that combines long short-term memory (LSTM) and support vector machine (SVM) techniques to capture temporal traffic characteristics while improving prediction accuracy. The proposed framework was evaluated through a one-week simulation involving heterogeneous devices operating under varying network conditions to assess its robustness and generalization capability. Experimental results demonstrate that the proposed model achieved an overall prediction accuracy of 93.23%, while also exhibiting strong performance in terms of specificity, recall, F-score, and computational efficiency. By accurately predicting network congestion before service degradation occurs, the proposed framework enables proactive traffic management and adaptive resource allocation. These findings demonstrate that the hybrid LSTM–SVM model provides an effective, reliable, and scalable solution for intelligent congestion prediction in next-generation 5G/6G communication networks.
Video summarization using deep image captioning models Qudes M. B. Aljelawy; Sarah S. Mohammed; Entessar K. Hanoun
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.pp547-554

Abstract

This research presents a novel approach for video summarization by leveraging deep image captioning models. A pretrained image captioning model, namely Salesforce's bootstrapped language image pretraining (BLIP), is used to extract keyframes from a video at regular intervals and produce natural language descriptions. These textual descriptions are then filtered for non-repetition and concatenated into a coherent summary, allowing users to understand the video’s content without viewing it in full. The proposed framework aims to improve video browsing, indexing, and retrieval efficiency, particularly for big datasets. The proposed method achieves a significant reduction in redundancy by 40% compared to raw captioning sequences. Evaluation using semantic consistency checks demonstrates that the BLIP-based framework maintains high descriptive accuracy even in complex scenes, providing a scalable solution for large-scale video indexing.
Adaptive feature selection for credit scoring models Mostafa Mohamed SeifElnasr; Yasser Omar; Saleh Mesbah
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.pp507-521

Abstract

Accurate credit scoring and delinquency prediction are critical for financial institutions, particularly when evaluating both banked and unbanked clients. Traditional feature selection approaches such as wrapper, filter, and embedded methods often require repeated retraining and may not efficiently explore optimal feature subsets in multi-class credit risk settings. This study proposes a reinforcement learning (RL)–based adaptive feature selection framework using tabular Q-learning to dynamically identify informative feature subsets for credit bucket classification and delinquency prediction. The framework was evaluated on two datasets, including proprietary credit datasets (24,533 records with 18–19 features). Using stratified 10-fold cross-validation, the proposed approach achieved 62.84% accuracy (F1=0.63) for 8-class credit bucket prediction, outperforming traditional wrapper-based methods by up to 4.2% while reducing computational cost compared to exhaustive subset evaluation. For delinquency prediction, the model achieved approximately 70% F1-score, demonstrating improved minority-class sensitivity. Compared to filter and embedded methods, the RL-based framework produced more compact feature subsets while maintaining competitive computational efficiency. These findings demonstrate that adaptive RL driven feature selection provides a practical and scalable mechanism for enhancing predictive performance in credit risk modelling, supporting automated and consistent decision making in financial institutions.
Intelligent land use and land cover classification using Sentinel-2 multispectral imagery Neha Vyas; Koushik Sundar; Narayan Vyas
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.pp586-594

Abstract

Accurate land use and land cover (LULC) classification is essential for environmental monitoring, agricultural planning, and sustainable resource management. This study investigates the effectiveness of Sentinel-2 multispectral satellite imagery for LULC classification by comparing the performance of three supervised classification algorithms: maximum likelihood classifier (MLC), minimum distance classifier (MDC), and neural networks (NN). Before classification, Sentinel-2 imagery underwent comprehensive preprocessing, including atmospheric correction, radiometric calibration, and cloud masking using ERDAS software to improve image quality and classification reliability. The Villupuram district of Tamil Nadu, India, was selected as the study area due to its diverse land cover characteristics. Classification performance was evaluated using overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), and the Kappa coefficient. Experimental results demonstrate that the MLC achieved the highest OA of 94.81% with a Kappa coefficient of 0.9308, outperforming MDC (90.65%, 0.8753) and NN (84.38%, 0.7917). These findings confirm that supervised classification of Sentinel-2 multispectral imagery provides reliable and accurate LULC mapping, offering valuable geospatial information to support precision agriculture, environmental monitoring, land resource management, and sustainable regional planning.

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