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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
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Analysis of solid oxide fuel cell systems for off-grid energy production Mehimmedetsi, Boudjemaa; Draidi, Abdellah; Smaani, Billel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp18-33

Abstract

This work presents a simulation study of a 50-kW solid oxide fuel cell (SOFC) power supply system that provides electricity to residential users. Indeed, many decentralized applications rely on renewable energy sources not connected to the primary power grid. Moreover, fuel cell modelling and simulation are critical for promoting renewable energy as they eliminate the need for physical prototypes, saving time and money. We have also developed a reliable model for simulating self-contained SOFC fuel cells. The elaborated model includes the kinetics of electrochemical processes and accounts for voltage losses in SOFCs. Our fuel cells produce the necessary electrical current to charge the device. Also, our system has fuel cells, a DC/DC converter, and an inverter with LCL filters. These components connect the fuel cell system to other power electronics and the electrical load. Furthermore, a mathematical model of a dual inverter system describes its control method, including the proportional and integral parameters in the voltage and current loops has been derived. The proposed model and system could be helpful for a standalone load.
Interrogative insights into depression detection via social networks and machine learning techniques Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp388-396

Abstract

As users on social networks (SNs) interact with one another by exchanging information, giving feedback, finding new content, and participating in discussions; thus, generating large volumes of data each day. This data includes images, texts, videos and can be used to help the user find out how they have been doing, when they were depressed, how not to be depressed, and other similar insights. Depression is one of the most common chronic illnesses and it has emerged as a global mental health problem. But the lack of these data is incomplete, sparse and sometimes inaccurate, and so the task of diagnosing depression using automated systems is still proving a challenge. Various techniques have been used to detect depression through the years however, machine learning (ML) and deep learning (DL) techniques offer better ways. In the context of that, this study reviews state-of-the-art ML and DL approaches for the detection of depression using systematic literature review (SLR) method as well as highlight fundamental challenges in literature, which future works can focus on. We hope that this survey will provide a better understanding of these strategies for the readers and researchers in the ML and DL fields, when it comes to diagnosis of depression.
A multi-path routing protocol for IoT-based sensor networks Rajkumar Dhamodharan, Udaya Suriya; Karani, Krishna Prasad; Pichandi, Saranya; Palani, Kavitha; Rajendran, Sathiyaraj
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp225-235

Abstract

Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.
Generation of distribution routes with shorter distances and fewer vehicles using the simulated annealing algorithm Cardenas-Mariño, Flor; Papa Quiroz, Erik Alex; Vilca, Rene Calderon; Cahuata, Edwar Ilasaca; Enriquez, Hesmeralda Rojas; Ayquipa Rentería, Ronald A.
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.pp707-718

Abstract

The vehicle routing problem (VRP) is still a persistent challenge in society, and can be considered a combinatorial optimization problem, where a fleet of delivery vehicles must satisfy the demand of customers sharing the same depot, minimizing the transport distance. The objective of this research is to propose a method to generate distribution routes that minimize both the number of vehicles used and the total distance traveled. To this end, an initial solution is used, on which the Greedy algorithm is applied, followed by the simulated annealing (SA) algorithm, manipulating the exchange techniques, insertion methods, parameter adjustments within the algorithm and applying the penalty as a mechanism to avoid the excessive use of trucks or the assignment of routes that exceed the allowed capacity. The proposal was validated using four datasets, as a result, the general averages of the reduction in distance, changes and penalty cost are shown: The Greedy algorithm reduced the distance by 5.71%, in trucks to 16.57%, in penalty cost to 14.71%; then, applying the SA algorithm, a better efficiency was achieved by reducing the distance by 10.36%, 20.08% in trucks and 18.64% in penalty cost. In this way, the use of vehicles in the distribution routes is optimized, which could contribute to the reduction of vehicular traffic and the reduction of CO2 emissions, thus favoring the environment.
Panic detection through facial recognition paradigm using deep learning tools Khlebus, Sameerah Faris; Mahdi, Mohammed Salih; Kherallah, Monji; Douik, Ali
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.pp1001-1010

Abstract

Recently, panic detection has become essential in security, healthcare, and human-computer interaction. Automatic panic detection (APD) systems are designed to monitor physiological signals and behavioral patterns in real-time to detect stress responses. APD is increasingly adopted across many sectors, including disaster preparedness, COVID-19, and terror attacks. Their integration with various applications reduces human efforts and saves costs. However, most studies rely on existing models with fewer new ones or techniques. This study proposes a vision-based panic detection model using MobileNet, ResNet, and convolutional neural network (CNN). The FER2013 dataset is used for the model training and testing. The results indicate that MobileNet is the most effective model for image-based panic detection across ten folds with an accuracy of 90%, recall of 96.9%, and mean accuracy of 0.032. MobileNet also showed a mean absolute error (MAE) between 0.02 and 0.04. This study has been to confirm MobileNet's suitability for image-based panic detection. The findings contribute to developing more reliable and accurate image-based panic detection systems in real-world applications. It offers valuable insights and lays the groundwork for future deep-leaning-based panic detection studies.
Federated learning in edge AI: a systematic review of applications, privacy challenges, and preservation techniques Sajan, Christina Thankam; Sunny, Helanmary M.; Pratap, Anju
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.pp926-940

Abstract

Edge artificial intelligence (Edge AI) involves the implementation of AI algorithms and models directly on local edge devices, such as sensors or internet of things (IoT) devices. This allows for immediate processing and analysis of data without the need for continuous dependence on cloud infrastructure. Concerns about privacy have grown importance in recent years for businesses looking to uphold end-user expectations and safeguard business models. Federated learning (FL) has emerged as a novel approach to enhance privacy. To improve generalization qualities, FL trains local models on local data. These models then collaborate to update a global model. Each edge device (like smartphones, IoT sensors, or autonomous vehicles) trains a local model on its own data. This local training helps in capturing data patterns specific to each device or node. Poisoning, backdoors, and generative adversarial network (GAN)-based attacks are currently the main security risk. Nevertheless, the biggest threat to FL’s privacy is from inference-based assaults such as model inversion attacks, differential privacy shortcomings and FL utilizes blockchain and cryptography technologies to improve privacy on edge devices. This paper presents a thorough examination of the current literature on this subject. In more detail, we study the background of FL and its different existing applications, types, privacy threats and its techniques for privacy preservation.
Generalized domain tutoring framework for AI agents with integrated explainable AI techniques Csépányi-Fürjes, László; Kovács, László
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.pp860-870

Abstract

This paper proposes a novel approach to integrate tutoring functionality into AI systems to counteract the potential decline of human intelligence caused by AI-driven over-automation. Existing explainable AI methods primarily emphasize transparency while lacking inherent educational functionality. Consequently, users are essentially left as passive recipients of AI-driven decisions without any structured learning mechanism in place. To address this, this paper introduces the knowledge-sharing-bridge (KSB), a component designed to transform AI into an active tutor. Unlike traditional intelligent tutoring systems (ITS), which operate separately from AI decision-making processes, the KSB is embedded within AI frameworks, ensuring continuous and context-aware learning opportunities. The proposed framework uses structured knowledge representation tools, such as category maps and word-clouds, to improve the user’s understanding of the decisions made by the AI systems. Prototype implementation demonstrates how these elements work together to provide real-time, interactive learning experiences. The results indicate that integrating KSB into AI enhances both explainability and user learning. This approach promotes a more in-depth interaction with AI insights and enables AI systems to become lifelong learning companions, closing the gap between automation and education.
Adoption of virtual tours for tourism promotion in Tegal Regency: a technology acceptance model analysis Dairoh, Dairoh; Handayani, Sharfina Febbi; Af'idah, Dwi Intan
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.pp1109-1120

Abstract

Tegal Regency has various tourist attractions that have the potential to be increased as a stimulus for the district's economy. So that this potential can have an optimal positive impact, the tourist destination should be promoted to the general public to increase tourism visits. This effort can be carried out by utilizing existing technological developments through virtual tour (VT), but their implementation requires careful consideration. This study explored how perceived usefulness (PU), perceived ease of use (PEU), attitude, behavioral intention (BI), and tourism promotion (TP) relate to each other within the context of virtual tourism. Data were collected from 126 participants via an an online survey developed using the technology acceptance model (TAM) framework. The partial least squares structural equation modeling (PLS-SEM) method was employed for analyzing the data. The result revealed significant relationships between PU and ease of use, user attitudes (AT), and BIs. Furthermore, BI, PU, and PEU were all considerable predictors of TP. However, no significant relationship was found between user AT and BIs.
A comparative study of solar photovoltaic array configurations to optimize power harvesting in a real-world system under various partial shading conditions Balakrishnan, Karthick; Mahalingam, Sudhakaran
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.pp558-566

Abstract

Partial shading (PS) significantly reduces power generation and efficiency in solar photovoltaic (PV) systems. This research presents a novel totalcross-tied (TCT) methodology designed to mitigate shading effects by optimizing array layout while preserving electrical connectivity. The TCT method is compared to three established configurations: series-parallel (S-P), bridge-linked (B-L) and honey-comb (H-C). MATLAB simulations on a (9×9) PV array under variousshading conditions demonstrate TCT’s superior performance in achieving the global maximum power point (GMPP). Key findings indicate that TCT surpasses the other configurations, reaching a maximum power output of 16,650W at GMPP, with a mismatch power loss of 2,600W, a power loss of 13.32%, a fill factor (FF) of 38.27, and an execution ratio (ER) of 0.866.
Development and evaluation of a generalized ontology framework for software requirement specification Kundu, Sourav; Das, Soumay Kanti; Md Jamil, Abu Rafe; Islam, Md Kamrul; Kabir, SK. Shalauddin; Akhond, Mostafijur Rahman
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.pp1050-1064

Abstract

This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.

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