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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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Autonomous trenching robot with intelligent obstacle detection and path optimization for precision cable installation Muhammad Omar; Hamza Ali Nisar; Muhammad Usman; Husnain Siddique; Suffian Zaman; Saad Saleem Khan; Justyna Robinson
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.pp425-438

Abstract

Trenching for underground cable and pipeline installation is typically labor intensive, time-consuming, and potentially hazardous, particularly in environments with buried obstacles. This paper presents a low-cost autonomous trenching robot with intelligent obstacle detection and path optimization to improve excavation efficiency, safety, and accuracy. The proposed system integrates ultrasonic and infrared sensors with an embedded controller for real-time obstacle detection and autonomous navigation. A path optimization algorithm automatically adjusts the trenching route whenever an obstacle is detected, allowing continuous operation while reducing unnecessary movement and energy consumption. The robot employs a tracked mobile platform and an automated trenching mechanism capable of maintaining consistent trench depth and width under different terrain conditions. Experimental results demonstrate that the proposed system accurately detects obstacles, successfully replans its path in real time, and performs reliable autonomous trenching with minimal human intervention. Compared with conventional manual trenching methods, the developed robot improves operational efficiency, enhances excavation accuracy, and reduces safety risks for workers. The proposed system provides a practical and scalable solution for underground cable and pipeline installation and has strong potential for future applications in intelligent construction, infrastructure development, and autonomous civil engineering.
Evaluating AI-powered ChatGPT for intelligent virtual learning environments through conversational intelligence Isaac Asampana; Henry Akwetey Matey; Ben Ocra; Jones Yeboah Nyame
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.pp568-575

Abstract

The rapid advancement of conversational artificial intelligence has transformed virtual learning by enabling intelligent, adaptive, and interactive educational support through natural language communication. Among recent AI technologies, chat generative pre-trained transformer (ChatGPT) has emerged as a powerful conversational intelligence system capable of providing real-time explanations, personalized learning assistance, and contextual academic interactions. This study evaluates the effectiveness of AI-powered ChatGPT in intelligent virtual learning environments through the lens of conversational intelligence. A quantitative survey instrument, developed based on established technology acceptance constructs, was employed to assess users' perceptions of ChatGPT's usefulness, ease of interaction, and educational effectiveness. Using a simple random sampling technique, data were collected from 1,104 participants and analyzed using descriptive statistical methods in SPSS version 25. The results indicate that ChatGPT's conversational capabilities significantly enhance learner engagement, support personalized learning experiences, and improve perceived academic performance through intuitive human-AI interactions. The findings demonstrate that conversational intelligence plays a critical role in improving the effectiveness of intelligent virtual learning environments by facilitating adaptive knowledge delivery and interactive learning support. This study provides valuable insights into the integration of conversational AI technologies for developing next-generation intelligent digital education systems.
The next era of electrification: engineering adaptive energy infrastructures for a decarbonized society Tole Sutikno
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.pp355-362

Abstract

A new era of electrification is reshaping electrical engineering, with adaptive energy infrastructures becoming critical foundations for achieving resilient, low-carbon, and sustainable energy systems. Rapid advances in renewable energy integration, power electronics, distributed energy resources, battery energy storage, electrified transportation, and intelligent energy management are transforming conventional power systems into flexible, interconnected, and resilient infrastructures. This editorial discusses the emerging paradigm of adaptive electrification, in which future electrical networks are expected to dynamically coordinate generation, energy storage, conversion, transmission, distribution, and consumption across increasingly decentralized environments. Beyond traditional objectives of efficiency and reliability, next-generation electrical infrastructures must address growing electricity demand, carbon neutrality, power quality, climate resilience, cyber-physical security, and energy accessibility. The editorial further highlights several promising research directions, including grid-forming power electronics, adaptive microgrids, intelligent battery management, solid-state energy conversion, digitalized power infrastructures, resilient hybrid AC/DC systems, and coordinated human–AI decision-making. Collectively, these technological advances position electrical engineering at the forefront of the global energy transition, emphasizing that future electrification requires not only cleaner energy sources but also adaptive, intelligent, and sustainable infrastructures capable of continuously evolving to meet societal, environmental, and industrial challenges while supporting reliable and equitable access to electricity.
Low-cost real-time campus assistive navigation device for the visually impaired: The University of Ilorin case study Mahmud Hafeez Owolabi; Idajili John Ojochegbe; Ayinla Shehu Lukman; Jimoh-Mahmud Aishat Oladayo; Yusuf Abdulrahman Olalekan; Olaogun Jerry Oluwajomiloju
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.pp555-567

Abstract

Navigating dynamic environments remains a significant challenge for visually impaired individuals due to limited spatial awareness, which restricts their mobility, independence, and safety. Traditional aids such as white canes and guide dogs provide physical support but lack contextual feedback. While recent advances in artificial intelligence (AI) and computer vision (CV) have enabled real-time sensing, many assistive systems remain costly, complex, and dependent on continuous network connectivity. This study introduces a low‑cost, portable campus assistive navigation (CAN) device that delivers real‑time perception and offline guidance. The system employs a custom dataset, an optimized YOLOv11 model, and a Pi Camera for continuous object detection, supported by an HC‑SR04 ultrasonic sensor for obstacle avoidance and auditory alerts. Training and validation confirmed robust convergence, with precision ~0.95, recall near 1.0, and mAP@0.5 exceeding 0.9, while mAP@0.5:0.95 remained above 0.8, demonstrating reliable detection and generalization under strict thresholds. Field tests further reported confidence scores of 0.74–0.84, 98% accuracy in distance measurement, and GPS localization within ±1.5 m. Real‑time auditory and haptic feedback via Bluetooth headphones enhanced mobility and safety. The CAN device offers a scalable, affordable solution for autonomous navigation in campus environments.
Probability density-based quantized spiking neural network for efficient intrusion detection in mobile ad hoc networks Amruth Veerabhadraiah; Devaraj Verma Chitragar
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.pp460-471

Abstract

Mobile ad hoc networks (MANETs) enable infrastructure-free wireless communication through decentralized and self-organizing network architectures, making them well suited for dynamic environments such as disaster recovery, military operations, and intelligent transportation systems. However, their distributed topology and continuously changing network structure expose them to sophisticated routing attacks, particularly blackhole attacks (BHA) and wormhole attacks (WHA), which significantly degrade network reliability and challenge conventional intrusion detection techniques. Existing deep learning approaches often struggle to accurately distinguish malicious from legitimate traffic because of high-dimensional feature spaces and limited computational resources in MANET environments. To address these challenges, this paper proposes a probability density-based quantized spiking neural network (PDF-QSNN) framework for efficient intrusion detection in MANETs. The proposed framework first employs adaptive moth flame optimization (AMFO) to identify the most informative features, thereby reducing data redundancy and computational complexity. Subsequently, probability density-based feature encoding generates a discriminative probabilistic representation of network behavior, while the quantized spiking neural network performs lightweight and energy-efficient intrusion classification. Experimental evaluations using simulated BHA and WHA datasets demonstrate that the proposed framework achieves classification accuracies of 92.86% and 91.56%, respectively, outperforming conventional convolutional neural networks (CNN) and stacked recurrent long short-term memory (SRLSTM) models. These results demonstrate the effectiveness of the proposed framework for accurate and computationally efficient intrusion detection in resource constrained MANET environments.
Geospatial data processing and random forest-based intelligent system for regional investment readiness prediction Yudhinanto Cahyo Nugroho; Desmon Desmon; Hasbullah Hasbullah; Triyugo Winarko
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.pp651-661

Abstract

This paper presents an intelligent system that integrates geospatial data processing and a random forest (RF) classification model to categorize regional investment readiness (IR). Regional investment planning is often constrained by fragmented socio-economic data, unequal infrastructure distribution, and unquantified disaster risk, which reduce the accuracy of decision making. To address this problem, multidimensional data consisting of socio-economic indicators, infrastructure accessibility, and disaster risk factors were collected from a sample of 15 administrative regions in Lampung Province, Indonesia, and processed through data cleaning, normalization, and feature selection. An IR score was first computed for each region using a weighted composite formula, then discretized into readiness classes and used as the target label to train a RF classifier capable of modeling complex nonlinear relationships among the input features. Given the limited sample size, model performance was evaluated using leave-one-out cross-validation, and classification metrics—accuracy, precision, recall, and F1-score—were reported to assess predictive reliability. The results reveal spatial disparities in IR, where regions with higher human development and better infrastructure tend to exhibit greater investment potential, while areas exposed to higher disaster risk tend to show lower readiness levels. The prediction outputs are integrated into a web-based interactive dashboard that enables spatial visualization and exploration of IR patterns. Given the small and single province sample, the proposed system should be regarded as a preliminary decision-support tool for policymakers and investors to help identify priority regions, and further validation on larger, more geographically diverse datasets is recommended to strengthen generalizability.
Production scheduling using a hybrid approach based on genetic algorithm and convergent random search Belbachir Djelloul; Kadri Boufeldja
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.pp534-546

Abstract

The flexible job shop scheduling problem (FJSSP) is a highly complex combinatorial optimization problem widely encountered in modern manufacturing systems. Its complexity arises from the simultaneous determination of operation sequencing and machine assignment, making it significantly more challenging than the classical job shop scheduling problem (JSSP). Recent advances in hybrid metaheuristics and intelligent optimization methods have improved solution quality; however, achieving an effective balance between global exploration and local exploitation remains a critical challenge. In this paper, a novel hybrid metaheuristic approach combining a genetic algorithm (GA) and convergent random search (CRS) is proposed to address the FJSSP. The proposed method exploits the global search capability of GA to explore the solution space, while CRS is employed as an adaptive local refinement mechanism applied to elite individuals. This hybridization strategy enhances convergence speed and avoids premature stagnation. Extensive computational experiments are conducted on well-known benchmark instances, including Brandimarte and Kacem datasets. The results indicate that the proposed GA–CRS approach significantly improves the makespan compared to classical GA and PSO based methods. In addition, the algorithm exhibits faster convergence behavior, reaching high-quality solutions in fewer iterations. Statistical analysis using non-parametric tests confirms the superiority of the proposed method. These findings demonstrate that the proposed hybrid GA–CRS algorithm provides a robust and efficient optimization framework for solving large-scale and complex FJSSP instances, outperforming several state-of the-art approaches.
A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters Raden Sutiadi; Sparisoma Viridi; Giyanto Giyanto; Andarta F. Khoir; Rizkie S. Utama; Elsa D. Aulia; Tri A. Hadi; Ludi Parwadani Aji
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

Abstract

Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
FireDetXplainer: an explainable artificial intelligence framework for wildfire detection Janjhyam Venkata Naga Ramesh; Bhargavi Peddi Reddy; Jillellamoodi Naga Madhuri Rajyalakshmi; Rajyalakshmi Uppada; Gaddam Venu Gopal; Rajesh Tulasi
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.pp576-585

Abstract

Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Factors that influence social media interaction among university students: emotional and mental well-being Nur Anis Mohd Jasmi; Fauziah Redzuan; Jasber Kaur Gian Singh
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.pp607-617

Abstract

Social media usage is becoming popular among students, which is used both for academic and entertainment. However, the excessive use of social media can increase the level of stress, anxiety, and depression. The social media interaction can also lead the students to compare themselves with influencers. This study aims to identify the factors and the significant relationship between the factors of social media interaction and emotional and mental well-being. This research used a quantitative research and hypothesis-driven approach. The existing theories, such as social capital theory and social comparison theory were adapted for the online survey questionnaires. The collected data were analysed using partial least square-structural equation modelling (PLS-SEM) to analyse the two objectives of this research. Based on the findings, twelve (12) constructs were identified as the factors that contribute to social media interaction on university students' emotional and mental well-being by assessing and validating the constructs using PLS-SEM. Then, there are thirteen (13) out of twenty-three (23) hypothesis were significant relationships based on the final model testing using PLS-SEM bootstrapping method. Ten (10) paths of direct effect, two (2) paths of mediating analysis, and one (1) path of moderating analysis were significant relationships and supported the alternative hypothesis. It is recommended for future studies to also explore other analysis techniques, theories, or models, and the changes to other group such as older generations or working adults.

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