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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
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Grasshopper sound acoustic signal analysis using FFT and Butterworth filter Khairunnisa Khairunnisa; Sarifudin Sarifudin; Annisa Maulidia Damayanti
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp708-720

Abstract

Grasshoppers are among the most destructive agricultural pests, making early detection essential to reduce crop losses while limiting excessive pesticide use. Acoustic monitoring provides a non-invasive and environmentally friendly approach for pest detection; however, its effectiveness is often constrained by strong environmental noise in open field conditions. This study proposes a structured acoustic signal analysis framework for grasshopper detection based on fast fourier transform (FFT) and Butterworth bandpass filtering. Grasshopper sound recordings were collected in rice field environments and pre-processed using Butterworth filters with empirically determined cutoff frequencies to suppress out-of band noise. FFT was applied to extract dominant spectral features, and signal quality was evaluated using both direct signal-to-noise ratio (SNR) and power spectral density (PSD)-based SNR estimated via the Welch method. Results indicate that grasshopper acoustic energy is consistently concentrated within the frequency range of approximately 5.8–9 kHz. Although direct time-domain SNR slightly decreases after filtering due to attenuation of out-of-band components, PSD-based SNR improves significantly, reaching 25–28 dB, demonstrating effective spectral concentration and noise suppression. The proposed approach is computationally efficient, interpretable, and suitable as a foundational module for low-cost, real-time acoustic pest detection systems in precision agriculture.
A hybrid large language model-graph neural network framework for Arabic sentiment analysis Hani Mohammadn Iwidat
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp391-402

Abstract

Arabic sentiment analysis (SA) faces significant challenges due to the language’s morphological richness and dialectal diversity. This study introduces a novel hybrid large language model-graph neural network (LLM-GNN) framework designed to address these challenges. The proposed model integrates the contextual understanding of AraBERT v2 with the structural learning capability of a graph convolutional network (GCN). It constructs a graph of sentences using cosine similarity, allowing the GCN to capture crucial inter-sentence semantic dependencies often missed by sequential models. Findings: The model is evaluated on a publicly available Arabic 100k Reviews dataset consisting of authentic user-generated Arabic reviews balanced across positive, negative, and mixed sentiment classes. The results demonstrate that the proposed LLM-GNN model performed better as compared to the baseline models, including fine-tuned AraBERT, AraBERT-BiLSTM, AraBERT-MLP, and multilingual BERT. The hybrid model achieves an overall accuracy of 66.8% and a F1-score of 66.55%, with an improvement of 7.6% and 4.4%, respectively. The model demonstrated stable convergence from the first training epoch. Research limitations/implications: The graph construction is performed at the mini batch level, which restricts the modeling of global semantic relationships across the entire corpus. The results show that the hybrid model identifies subtle sentiment cues that sequential models frequently miss by fusing relational graph reasoning with contextual embeddings. By effectively identifying subtle sentiment cues, the hybrid model can significantly enhance the accuracy of real-world applications such as social media monitoring and customer review analysis for Arabic content.
Comparative deep learning CNN architectures for breast cancer detection from thermal imaging Md. Sumon Hosen; Mustafizur Rahman; Zaid Bin Sajid; Md Naeem Hossan; Apu Biswas; Md. Mijanur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp369-379

Abstract

It has been observed that breast cancer is a severe disease among women globally. Mammography is the most effective screening method for detecting this severe illness. Over the last thirty years, mammography has been widely recognized as a preventive measure against breast cancer. In recent years, convolutional neural networks (CNN) and artificial intelligence (AI) have become more common in digital mammography for automated breast cancer detection. For classifying breast cancer, this study examines the five CNN models: LeNet-5, AlexNet, VGG-16, ResNet-50, and Inception-v3, using the database for mastology research with infrared images (DMR-IR) dataset's thermal image. These models were trained and validated using accuracy, recall, F1-score, specificity, and AUC as evaluation criteria after the dataset was preprocessed using normalization and data augmentation. Among the experimental models, Inception-v3 achieved 99.44% accuracy, outperforming other CNNs by 1–2%, while other models performed with accuracy levels above 97%. These results show the tremendous efficacy of CNN-based deep learning methods for breast thermogram analysis. The research points out thermography as a useful support for traditional imaging and InceptionV3 as a potential option for correctly detecting clinical breast cancer.
An enhanced deep learning model with context-aware attention for diabetes prediction J. Jannathul Firthous; G. Murugeswari
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp509-517

Abstract

A plethora of people worldwide suffer from diabetes, a chronic, potentially fatal illness that resulted serious risks and complications if left untreated. Effective management requires early prediction and intervention. Despite their advantages, traditional machine learning techniques frequently find it difficultly in grasping the intricate temporal as well as geographical correlations included with in medical stats. For the purpose of effectively forecast diabetes mellitus, the proposed work suggests a unique deep learning model called multilayer diabetes deep learning attention with context mechanism (MLDDAM). This model incorporates a hybrid architecture that integrates an Attention with Context Mechanism to enhance the model’s efficiency will be conversant with emphasizing on key aspects, convolutional neural networks (CNN) are utilized to extract traits, and bidirectional long short-term memory (BiLSTM) captures sequential dependencies. This innovative design enables the model to perform better by utilizing the input data’s temporal and geographical properties. Experiments using benchmark datasets show that the suggested MLDDAM model is efficient and robust, with outstanding 99.43% prediction accuracy for diabetes. These outcomes demonstrate the MLDDAM model’s effectiveness as a precise and dependable tool to assist clinical decision-making in the management of diabetes.
Metaheuristic optimization of wind turbine farm siting in power grids: a comparative study of PSO and GA Taha Rachdi; Yahia Saoudi; Larbi Chrifi-Alaoui; Ayachi Errachdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp666-677

Abstract

This paper addresses the optimal integration of wind turbines into distribution networks with the aim of reducing active power losses and improving voltage stability. Two metaheuristic optimization methods genetic algorithm (GA) and particle swarm optimization (PSO) are applied to determine the optimal siting and sizing of wind turbines in the IEEE 14-bus system. The problem is formulated as a multi-objective function combining loss minimization and voltage profile enhancement under standard network constraints. Simulation results using MATLAB/PSAT show that both algorithms improve system performance compared to the base case, with PSO providing superior loss reduction and voltage stability. Wind variability is represented through a Weibull distribution to reflect realistic operating conditions. The study demonstrates the effectiveness of metaheuristic optimization for renewable integration and highlights PSO’s stronger robustness. The work contributes a comparative evaluation of GA and PSO, supported by stability analysis and realistic wind modelling.
Voice portraits: building faces through voice analysis Anandhu T. G.; John K. Joseph; Navneeth Krishnan J.; Richu Shibu; Elizabeth Isaac
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp902-912

Abstract

Generation of a person’s appearance from their voice alone is an intriguing challenge. The proposed framework centers on recreating a person’s facial image based solely on a short audio recording of that person speaking. Using a deep neural network trained on millions of YouTube recordings where faces and voices appear together, the system learns voice-face relationships, enabling it to generate images that capture physical traits such as age, gender, and ethnicity. Operating in a self-supervised manner, this method takes advantage of the pairing of faces and voices in online videos, eliminating the need for explicit property modeling. The model achieved a classification accuracy of (95%) for gender, (83%) for age, and (65%) for race prediction from voice inputs, demonstrating an exceptional performance in demographic trait identification. The generated images are evaluated against real photographs of the speakers, assessing how closely these reconstructions resemble actual appearance. This framework has practical applications in forensic analysis, security systems, and privacy-conscious biometric identification, offering a non-invasive alternative to traditional facial recognition methods.
Evaluation of aerodynamic and structural design to enhance solar energy absorption for e-Cars Mohamed Abubakr Mahgoub Hassan; Belal Ahmed Hamida; El Sayed Soliman; Muhammed Zaharadeen Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp649-665

Abstract

The integration of photovoltaic (PV) systems into electric vehicles (EVs) offers a promising solution for extending driving range and reducing dependence on grid-based charging. However, vehicle-integrated PV systems are limited by aerodynamic drag, structural integration challenges, thermal losses, and inefficient energy management. This study presents a multidisciplinary simulation framework to evaluate aerodynamic and structural optimization strategies for enhancing solar energy absorption in EVs. Computational fluid dynamics (CFD) was used to analyze airflow and reduce aerodynamic drag, while finite element analysis (FEA) assessed structural integrity and weight optimization after PV integration. PV energy flow and thermal models were also developed to evaluate power generation, battery charging behavior, and temperature-dependent efficiency losses. The optimized design reduced the drag coefficient from 0.310 to 0.236, a 23.7% improvement, while maintaining a structural safety factor above 1.75 through lightweight composite materials. The optimized PV configuration increased solar conversion efficiency from 17.6% to 22.3% and daily energy generation from 2.91 kWh/day to 3.75 kWh/day, corresponding to a 28.9% increase in harvested energy. Thermal management strategies lowered average PV operating temperature by about 12 °C, improving efficiency by an additional 5%–7%. Unlike existing studies that examine aerodynamic, structural, or PV performance separately, this work provides a unified framework that evaluates their combined impact on solar energy harvesting in EVs. The proposed integrated design approach demonstrates that coordinated aerodynamic, structural, thermal, and energy-management optimization can substantially improve the practicality and energy contribution of solar-assisted EVs in high-irradiance environments.
Development of an IoT-based waste monitoring and notification system for smart environmental management Enggar Utari; Ika Rifqiawati; Wahyuni Martiningsih; Izzal Ihasani; Aditya Rahman; Bagus Dwicahyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp729-741

Abstract

Rapid urban population growth has intensified solid waste generation, while many existing waste management systems still rely on manual inspection and single-parameter monitoring, resulting in delayed responses and inefficient handling. Previous studies have primarily focused on isolated sensing or offline monitoring, highlighting the need for integrated, real-time, and user-oriented waste monitoring solutions. This study used RnD method, proposes a smart garbage level and information hub (SIGALIH), an IoT based waste monitoring and notification system designed to address these limitations. SIGALIH combines multi-parameter sensing, including waste level, temperature–humidity, gas concentration, and ambient light, with an ESP32 microcontroller, a cloud-based data platform, and a real-time notification service using a messaging bot. System evaluation involved sensor accuracy testing, communication latency analysis, and functional verification. Experimental results indicate an average sensor accuracy of 96.8%, with an average data transmission latency of 1.84 seconds and a notification delay of 2.14 seconds, indicating reliable real-time performance under varying network conditions. Functional testing confirmed stable operation of all system modules. The system was also integrated into an Environmental Education learning module to support environmental literacy and awareness through contextual learning on sustainable waste management. SIGALIH is designed for small- to medium-scale urban and community-based applications. However, performance depends on wireless network availability, which may reduce reliability in low-connectivity areas. Overall, SIGALIH provides a low-cost, scalable, integrated solution supporting smart environmental management and sustainable urban waste initiatives.
Beef spoilage assessment using e-nose and machine learning on unbalanced dataset Noreddine Kazitani; Miloud Chikr-El-Mezouar; Elhocine Boutellaa
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp552-560

Abstract

Food quality and freshness especially meat which have short shelf life like beef meat is a real problem in nowadays. This kind of food should be stored at suitable temperature and humidity conditions. For this purpose, a system is created to detect different states of freshness through an open unbalanced dataset. Machine learning models' performance is affected by unbalanced classes, which leads to biased outcomes and poor performance on minority classes, to address this issue this study uses synthetic minority oversampling technique (SMOTE). For this purpose, an open dataset containing 10800 samples, where four classes are distinguished (excellent, good, acceptable and spoiled). In this study, the proposed e-nose is composed of 10 sensors. For classification 6 machine learning methods are used. The best results are obtained from k-nearest neighbors (KNN) model with 99.83% of accuracy, 99.86% of precision, 99.80% of recall and 99.83% of F1-score.
Velocity hemodynamic patterns in aortic valve stenosis: a study of inlet velocity during systole phase Nur’Afifah Yousri; Nabilah Ibrahim; Ishkrizat Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp884-891

Abstract

This work highlighted a close-up version of the aortic valve that provide detail parameter and clearer graphics compared to the 3D version. Therefore, four simplified models are designed and simulated by using computational fluid dynamics (CFD) which are one healthy valve model (100%) and three stenotic models with varying valve opening (70%, 50%, and 30%). The model dimensions and setup parameters are determined by comparing the healthy aortic valve with the previous data. The analysis focused on two different views, which are the view on a targeting line velocity along the x-axis, and the view at the y-axis around the aortic valve. Results on the evaluation graph at the x-axis and y-axis show significant differences in flow patterns between healthy and aortic valve stenosis. The healthy model of 100% valve opening depicted a lower velocity (m/s) at 1.5m/s compared to the stenotic model of 70%, 50%, and 30% valve opening that showed higher velocities of 3.24 m/s, 6.09 m/s, and 14.57 m/s, respectively, due to the narrowing of the valve opening. Thus, the smallest orifice of the valve produced a higher velocity. This finding highlights the importance of hemodynamic assessment in aortic valve stenosis by providing valuable insight for clinicians in pre-surgical evaluation.

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