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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
 899   900   901   902   903 
Automatic wildlife species identification on camera trap images using deep learning approaches: a systematic review Mamapule, Siyabonga; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp968-977

Abstract

The foundation of systematic research depends on precise species identification, functioning as a critical component in the processes of biological research. Wildlife biologists are prompting for more effective techniques to fulfill the expanding need for species identification. The rise in open source image data showing animal species, captured by digital cameras and other digital methods of collecting data, has been monumental. This rapid expansion of animal image data, integrated with state-of-the-art machine learning techniques such as deep learning which has shown significant capabilities for automating species identification. This paper focuses on the role of deep neural network architectures in furthering technological advancements in automating species identification in recent years. To advocate further investigation in this field, an examination of machine learning architectures for species identification was presented in this work. This examination focuses primarily on image analyses and discusses their significance in wildlife conservation. Fundamentally, the aim of this article is to offer insights into the present advancements in automating species identification and to act as a reference for scholars who are keen to integrate machine learning techniques into ecological studies. Systems designed through Artificial Intelligence are extensive in providing toolkits for systematic identification of species in the upcoming years.
AlGaN/GaN MSM UV photodetector without and with BGaN back-barrier layer comparison study by SILVACO-TCAD Benyettou, Aicha; Hamdoune, Abedelkader; Benadda, Belkacem; Lachachi, Djamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp590-600

Abstract

Using DevEDIT and atlas under SILVCAO-TCAD, we were able to achieve high photodetector metal-semiconductor-metal (MSM) AlGaN/GaN/BGaN performance with high electronic mobility. Our device demonstrated a sensitivity of 286 (I illumination/I dark) at Vanode 20V with an illumination current of 26 mA, a photocurrent of 1.56e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 87% without BGaN, and we also studied the influence of the boron B0.03Ga0.97N back-barrier layer. As a result, we obtain a sensitivity of 293,4 at Vanode 20V with an illumination current of 27 mA, a photocurrent of 1,85e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 90%. Additionally, this type of photodetector has been effectively created to detect UV light in the 100–450 nm range, and it may find value in both medical and military settings. Astronomical, medical diagnostics, environmental sensing, remote sensing, thermal imaging, optical signal detection, night vision cameras, missiles, and target tracking.
Interactive multimedia e-collaboration for innovative linguistics education Rafiqa, Syarifa; De Vega, Nofvia; Arifin, Arifin
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1149-1157

Abstract

This study aims to investigate the needs of students and lecturers regarding interactive multimedia resources in linguistics at the Faculty of Teacher Training and Education, Universitas Borneo Tarakan, to facilitate further development. The findings reveal a significant gap between current instructional provisions and the specific needs of students and faculty, highlighting the necessity for pedagogical innovation to enhance interaction and understanding in linguistics. Utilizing a mixed-methods approach, the research included surveys and interviews with participants in linguistics courses. Results indicated that 86% of students sought in-depth knowledge of linguistics, and 73% felt that existing support was inadequate. It underscores a high demand for a focus on selected topics, simplified explanations, and multimedia interactivity. The findings demonstrate that instructional materials are poorly aligned with teaching needs, negatively impacting educational methodologies and failing to effectively address students' relevant needs. The implications of this study extend to practice and further research, urging faculty members to increasingly integrate multimedia elements into their teaching and develop tailored resources based on identified needs. Newly created materials should undergo practical evaluation to enhance student satisfaction and performance in linguistics studies.
Improving recommendations with implicit trust propagation from ratings and check-ins Medjroud, Sara; Dennouni, Nassim; Loukam, Mourad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp814-828

Abstract

This paper investigates how the propagation of implicit trust between users affects the quality of point-of-interest (POI) recommendations in location-based social networks (LBSNs). Through the analysis of user interactions via ratings and check-ins, this work proposes a recommendation model known as propagation of rating/check-in for implicit trust (PRCT). This model relies on two primary approaches: Similarity trust rating (STR), which utilizes user ratings, and similarity trust check-in (STC), which focuses on check-ins data. Both approaches employ trust propagation to enhance their similarity matrices between users. An evaluation of the PRCT model using the Yelp dataset shows that the STR approach surpasses other variants in terms of PRECISION and RECALL, while the STC approach demonstrates superior performance in terms of RMSE. Furthermore, while trust propagation in the PRCT model increases the density of its similarity matrices, it does not consistently enhance its PRECISION parameter. Only the similarity Jaccard check-in (SJC) and similarity cosine check-in (SCC) approaches show a significant improvement of this parameter. 
Interpretable federated deep learning models for predicting gait dynamics in biomechanics Ahamed, Shaik Sayeed; Pasha, Akram; Rahman, Syed Ziaur; Kumar, D. N. Puneeth
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1087-1099

Abstract

Accurate prediction of human joint angle dynamics and reliable gait classifica tion are essential for applications in rehabilitation, biomechanics, and clinical monitoring. Traditional machine learning (ML) models trained on centralized data raise concerns about privacy, scalability, and transparency. This study proposes a federated deep learning (DL) framework that integrates privacy preserving model training with interpretable predictions. Specifically, a gated recurrent unit- deep neural network (GRU-DNN) hybrid model is developed for regression of joint angles, while a Long short-term memory- convolutional neural network (LSTM-CNN) hybrid model is designed for binary and multi class gait classification. The framework is deployed using the federated av eraging (FedAvg) algorithm across simulated clients, with each client training locally on its data. To enhance interpretability, the local interpretable model agnostic explanations (LIME) algorithm is integrated at the client level to gener ate human-understandable explanations for model predictions. The experimen tal results demonstrate significant improvements, including a reduction in global mean squared error (GMSE) from 56.16 to 3.31 and an increase in R-squared score from 0.80 to 0.99 for regression, along with classification accuracies of 0.97 (binary) and 0.94 (multi-class). This scalable, privacy-preserving frame work bridges the gap between accuracy and transparency, offering impactful applications in biomechanics, healthcare, and personalized medicine.
End-to-end system for translating bahasa isyarat Indonesia sign language gestures into Indonesian text Putra, Satria; Rakun, Erdefi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp719-734

Abstract

This study addresses critical challenges in developing an end-to-end bahasa isyarat Indonesia (BISINDO) SLT by integrating advanced deep learning techniques to overcome complex background interference, transitional gesture recognition, and limitations in dataset availability. While existing SLT systems struggle with isolated word recognition and manual preprocessing, our work introduces three key innovations: (1) implementation of YOLOv8 for optimized object detection, achieving 88% mAP and reducing WER to 11.40%, outperforming YOLOv5/v7 in handling complex backgrounds; (2) automated removal of transitional gestures using Threshold conditional random fields (TCRF), which attained 95.68% accuracy, significantly improving upon MobileNetV2’s performance (WER: 6.89% vs. 93.53%); and (3) end-to-end BISINDO SLT by expansion of the BISINDO dataset to 435 word labels, enabling comprehensive sentencelevel translation. Experimental results demonstrate the system’s robustness, with 8.31% of WER, 84.13% of SAcc, and 87.08% of SacreBLEU after dataset expansion and redundancy elimination through grouping methods. The proposed framework operates without manual intervention, marking a substantial advancement toward real-world applicability.
Boosting carbon removal efficiency in wastewater treatment systems using a fuzzy model predictive control stategy Dhouibi, Saïda; Jarray, Raja; Bouallègue, Soufiene
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp629-639

Abstract

The efficient removal of carbon pollution has always presented a growing challenge facing wastewater treatment plants (WWTPs) operating with activated sludge process (ASP) technology. Enhancing pollution removal efficiency to meet standard wastewater quality limits remains a problematic in water pollution management. Recent progress in modeling and automatic control techniques can significantly improve the hydric pollution removal. In this paper, an effective carbon elimination strategy combining TakagiSugeno (TS) fuzzy modeling and model predictive control (MPC) is proposed to achieve high purification performance in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended solids (TSS) indicators. A fuzzy TS model is established based on the concepts of quasi-linear parameter-varying (LPV) forms and convex polytopic transformations of the system nonlinearities. The concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen as well as the effluent volume are controlled and maintained around their desired references with the aim of increasing pollution removal. Comparisons with the previously most used state-of-the-art parallel distributed compensation (PDC) are performed. High and competitive pollution removal percentages of 91% for COD and BOD5 indicators, and 92% for TSS metric, are achieved with the proposed MPC-based design, thus complying with the normative limits defined in WWTPs.
Unveiling educational enrollment factors in Egypt via ensemble learning Alsheref, Fahad kamal; Mostafa El Misery, Mostafa Sayed; Bahloul, Mahmoud Mohamed; Magdi, Dalia A.; Fattoh, Ibrahim Eldesouky
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp941-952

Abstract

Education plays a vital role in the development of a nation and significantly influences the direction of societies. Understanding the various factors that impact educational enrollment is essential for policymakers and resource allocation strategies. This paper explores the factors impacting educational enrollment in Egypt using predictive modeling and machine learning techniques. The study evaluates six machine learning algorithms and ensemble learning approaches to predict enrollment rates, considering computational efficiency, robustness, and parameter sensitivity. By analyzing socio-economic and demographic indicators from Egyptian educational data, the research examines the interplay of these factors. Results highlight the effectiveness of these methods in elucidating enrollment patterns, with ensemble learning showing promising performance and significant improvements compared to traditional machine learning algorithms. This study offers insights into Egypt's educational landscape that could inform policy formulation and resource allocation strategies.
Fuzzy multi-objective energy optimization of workflow scheduling Chehlaf, Ayoub; Gabli, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp871-882

Abstract

Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.
Optimizing clustering efficiency with weighted k-means: a machine learning-driven approach for enhanced accuracy and scalability Kaushik, Vishal; Aleem, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1121-1128

Abstract

Data analysis unlocks the hidden, latent patterns and structures within datasets. Clustering algorithms, the cornerstone of any data analysis, are usually challenged by high-dimensionality, complexity, or large-scale data. This research proposes a hybrid model that merges neural networks and clustering techniques to handle these problems. Neural networks are used for feature extraction and dimensionality reduction; raw data will be transformed into a robust, low-dimensional representation. With these refined features, the performance of clustering algorithms improves in terms of scalability, efficiency, and accuracy. The proposed model is tested on diversified datasets such as the wisconsin breast cancer dataset (WBCD), GEO Dataset, and image and text data benchmarks for which substantial improvements in clustering metrics such as silhouette score, purity, and computational efficiency are reported. The results demonstrate the efficacy of the hybrid approach in optimizing clustering applications across domains, such as bioinformatics, health care, and image analysis.

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