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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 39, No 1: July 2025" : 65 Documents clear
Investigating the recall efficiency in abstractive summarization: an experimental based comparative study Anuradha, Surabhi; Sheshikala, Martha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp446-454

Abstract

This study explores text summarization, a critical component of natural language processing (NLP), specifically targeting scientific documents. Traditional extractive summarization, which relies on the original wording, often results in disjointed sequences of sentences and fails to convey key ideas concisely. To address these issues and ensure comprehensive inclusion of relevant details, our research aims to improve the coherence and completeness of summaries. We employed 25 different large language models (LLMs) to evaluate their performance in generating abstractive summaries of scholarly scientific documents. A recall-oriented evaluation of the generated summaries revealed that LLMs such as 'Claude v2.1,' 'PPLX 70B Online,' and 'Mistral 7B Instruct' demonstrated exceptional performance with ROUGE-1 scores of 0.92, 0.88, and 0.85, respectively, supported by high precision and recall values from bidirectional encoder representations from transformers (BERT) scores (0.902, 0.894, and 0.888). These findings offer valuable insights for NLP researchers, laying the foundation for future advancements in LLMs for summarization. The study highlights potential improvements in text summarization techniques, benefiting various NLP applications.
Predictive modelling of osteoporosis and effect of BMI on the risk of fracture in femur bone using COMSOL Multiphysics: a computational modelling approach Kamal, Aleena; Kamal, Minahil; Fatima, Mashal; Hussain, Syed Muddusir; Sami Ur Rahman, Jawwad; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp89-100

Abstract

This study explores the intricate relationship between osteoporosis, body mass index (BMI), and the risk of femur fractures using computational modeling. Osteoporosis is a silent metabolic disorder that depletes bone density and structure, significantly increasing the risk of fractures, particularly in weight-bearing bones such as the femur. To analyze the impact of mechanical stress on osteoporotic bones, COMSOL Multiphysics was utilized to simulate stress distribution in a femur under varying BMI conditions, providing valuable insights into how BMI influences bone health and fracture risk. A three-dimensional (3D) femur model was designed using computer-aided design (CAD) software, with specific material properties assigned for both healthy and osteoporotic bones. Finite element analysis was conducted by applying different load conditions, representing body weight, on the femur head. The results highlighted stress distribution and deformation patterns, identifying regions most prone to fracture. The findings demonstrate that while higher BMI typically correlates with increased bone density, it also leads to greater deformation in osteoporotic bones under stress, emphasizing the complex interplay between BMI and bone strength. These insights underscore BMI’s critical role in fracture risk management. Future research should incorporate advanced fracture mechanics models and clinical data to enhance predictive accuracy and develop targeted strategies for fracture prevention in osteoporotic patients.
Optimizing stress resistance in MEMS inertial sensors through material and thickness variations Aziza, Miladina Rizka; Setyawati, Onny; Jumiadi, Jumiadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp110-117

Abstract

Stress on the micro-electromechnical system (MEMS) sensors significantly decreases sensor accuracy. Thermomechanical stresses induced by the packaging assembly process and external loads during operation induce a shift in the output signal (offset) of MEMS sensors. To achieve high precision in accelerometers, gyroscopes, and other MEMS devices, it is crucial to employ advanced modeling and simulation techniques to mitigate stress-induced offset drift. Therefore, this paper aims to explore and simulate stress on inertial sensors by designing a gyroscope tuning fork with a perforated proof mass to reduce the damping effect. Our findings provide insights for decreasing stress by varying the material and thickness of the inertial sensor. The least stress was obtained from an inertial silicon sensor with 5 and 20 mm thicknesses.
Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD filter-CLAHE Khozaimi, Ach; Darti, Isnani; Anam, Syaiful; Kusumawinahyu, Wuryansari Muharini
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp644-655

Abstract

Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: PeronaMalik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.
High-accuracy classification of banana varieties using ResNet-50 and DenseNet-121 architectures Riska, Suastika Yulia; Sulistyo, Danang Arbian; Siti Maharani, Farah Shafiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp322-335

Abstract

Bananas are a popular fruit in Indonesia due to their affordability, availability, and rich nutritional content. Identifying different banana types is crucial for consumption and processing, yet some types are difficult to distinguish visually. This study aims to classify banana types using convolutional neural network (CNN) architectures, specifically ResNet-50 and DenseNet-121. The dataset consists of five banana classes, which were processed using preprocessing techniques to enhance image quality prior to model training. The results demonstrate that the proposed models can classify banana types with high accuracy. The research methodology includes data collection, preprocessing, CNN model implementation, and performance evaluation using a confusion matrix. The dataset was split into training and testing sets in an 80:20 ratio, with validation data extracted from the training set in a 90:10 ratio. The models were trained on the training data, validated with validation data, and tested on the testing data to assess final performance. The study concludes that the CNN architectures employed are effective in classifying banana types, with the DenseNet-121 model achieving 93.02% accuracy, outperforming the ResNet-50 model, which achieved 92.44%. These results indicate that the models can capture essential features from banana images and produce accurate predictions.
A compact study on methodological insights on navigational systems in vehicular traffic system Thimmappa, Prathibha; Kundu, Mayuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp585-591

Abstract

Navigation system has witnessed a significant inclusion of potential technological advancement in the area of vehicular traffic system. Since the last decade, there are various evolution of innovative techniques that has identified and addressed some serious problem towards vehicular navigation system. With a progress of time, artificial intelligence (AI) has evolved as contributory role model towards optimizing the performance of navigation system. However, still it is quite challenging to acquire a quick snapshot of overall stand of all such methodologies and its effectiveness. Hence, this paper presents a precise, compact, and highly crisp discussion of core taxonomies of methods towards improving navigation system. The paper also contributes towards highlighting their strength and weakness followed by updated research trend to understand the true picture. Finally, the paper contributes to highlight the critical trade-off and gaps.
Benchmarking spectral handoff rate performance in cognitive wireless networks with real multi-user access Hernández, Cesar; Giral, Diego; Martínez, Fredy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp190-201

Abstract

Cognitive radio (CR) has proven to be an excellent alternative to the problem of inefficient spectrum use in wireless networks. However, the vast majority of proposals found in the current literature are restricted to the access of a single secondary user (SU) to the network, and the few proposals with multiple access do not take into account the access of other primary users (PUs) during the opportunistic transmission of the SU. The objective of this work is to perform a comparative evaluation of the spectral handoff (SH) rate in cognitive wireless networks with multi-user access in an environment with other PUs interacting. To carry out this evaluation, four SH models with better performance were selected: deep learning (DL), feedback fuzzy analytic hierarchy process (FFAHP), simple additive weighting (SAW), and Naïve Bayes (NB), which were validated according to the metric of the number of total handoffs, under four scenarios given by the combination of the following parameters: low spectral availability, high spectral availability, active presence of others SUs, and passive presence of others SUs. The results show that each model performs well according to the scenario in which it is executed, suggesting an adaptive multi-model as a proposal.
The impact of coordinator failures on the performance of Zigbee networks in various topologies Naubetov, Daulet; Yakubova, Mubarak; Yakubov, Bahodir; Smailov, Nurzhigit
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp235-246

Abstract

Zigbee, a key technology in the field of wireless networks for the Internet of Things, plays a significant role in the development of modern wireless network technologies. In this study, the analysis of coordinator failures in ZigBee networks with different topologies (“star”, “tree”, “mesh”) was carried out using the OPNET Modeler software tool. The problems related to the reliability and efficiency of systems using Zigbee technology are considered. Simulation of successive coordinator failures allowed us to compare the performance of topologies, revealing that the tree topology provides high traffic speed and bandwidth, but suffers from significant packet loss and delays. In turn, the star topology demonstrates minimal latency and high speed, and the mesh topology has better reliability with less packet loss, but the lowest speed and bandwidth. The findings emphasize the importance of choosing the optimal topology to ensure the efficiency and reliability of Zigbee networks in a volatile environment and increased load.
Optimization of 3D rendering algorithms for carbon reduction in virtual reality technology Purnomo, Fendi Aji; Arifin, Fatchul; Surjono, Herman Dwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp399-409

Abstract

Virtual reality (VR) systems are widely used across various domains, yet their high computational demands significantly contribute to energy consumption and carbon emissions. Optimizing rendering algorithms is essential to address these environmental challenges, particularly in multiuser VR environments where efficiency is critical. This study aims to evaluate the effectiveness of various rendering techniques in reducing energy consumption and carbon emissions as optimal solutions for multiuser VR applications. The research methodology followed the PRISMA framework, with a literature search conducted using the Scopus database and keywords such as “virtual reality” and “energy efficiency.” The search yielded 1,374 articles published after 2019, which were screened and narrowed down to 24 critical articles. Results demonstrate that Occlusion Culling achieves up to 85% energy savings per frame, translating to a carbon emission reduction of 76.5 g CO₂/hour, while LOD provides a 50% energy efficiency improvement, reducing carbon emissions by 45 g CO₂/hour. These findings highlight the critical role of these techniques in enhancing the sustainability of VR systems, particularly in multi-user environments, and underscore their potential as key strategies in reducing the environmental footprint of VR technology.
A hybrid APSO–ANFIS optimization based load shifting technique for demand side management in smart grids Faradji, Mohamed; Layadi, Toufik Madani; Rouabah, Khaled
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp45-61

Abstract

Cost and performance are considered important parameters to obtain an optimized configuration for smart grids. In this paper, a new optimization approach, based on a hybrid adaptive particle swarm with an adaptive neurofuzzy inference system (ANFIS) algorithm, has been proposed. This approach allows optimizing demand side management (DSM) using the load shifting technique. The impact of the latter on consumer profile, electricity pricing mechanisms, and overall grid performance are illustrated. In this simulation, the focus lies on modeling DSM using a day-ahead load shifting approach as a minimization problem. Simulation experiments have been tested separately on three different demand zones, namely, residential, commercial, and industrial zones. A comparative study of solutions was performed, focusing on both reduced peak demand and operational costs. The obtained results demonstrate that the optimization presented in this article approach outperforms the other approaches by achieving greater savings in the residential and commercial sectors. The study proved a significant reduction in peak demand. In fact, values of 23.76%, 17.61% and 16.5% in peak demand reduction are achieved in the case of residential, commercial, and industrial sectors, respectively. Furthermore, operational cost reductions of 7.52%, 9.6%, and 16.5% are obtained for the three different cases.

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