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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 9,226 Documents
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Experimental investigation of soil pH Engineering with eco enzyme to improve grounding performance I Wayan Jondra; Zulkurnain Abdul-Malek; I Nengah Sunaya; Made Sudana; I Made Purbhawa
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp23-29

Abstract

The reliability of electric power distribution, in mitigating fault and disturbances, is strongly influenced by the effectiveness of grounding systems. A key factor in achieving low grounding resistance an essential requirement per construction and safety standards is soil condition. High grounding resistance is frequently observed in field implementations and is closely linked to soil resistivity, type, stratification, moisture content, and acidity (pH). This quantitative applied research addresses the persistent challenge of high grounding resistance by experimenting with investigating six grounding system models subjected to varying soil acidity levels. The study introduces the use of eco enzyme as a natural additive to modify soil pH and examines its effect on grounding resistance. Findings reveal that eco enzyme application successfully lowers soil pH, with an optimal reduction in grounding resistance observed at pH 3.8 achieving a drop from 40 ohms to 9 ohms. However, further lowering the pH below 3.8 results in a rise in resistance, indicating a threshold where acidic conditions become counterproductive. This research opens opportunities for broader applications of eco enzyme-treated soil in non-rod electrode systems and across diverse soil types, suggesting promising pathways for enhancing grounding systems in various environmental conditions.
Integrating blind source separation and self-supervised learning for Algerian Arabic connected-digit recognition Mourad Reggab; Mohammed Belkhiri
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp71-80

Abstract

This paper proposes an improvement in Arabic automatic speech recognition (ASR) by combining blind source separation (BSS) with self-supervised acous tic modeling. The study concentrates on the Algerian Arabic connected-digit recognition task and reexamines the classical degenerate unmixing estimation technique (DUET) as a front-end approach for suppressing noise and inter ference. The output of the BSS stage is fed into a Hidden Markov model (HMM) recognizer developed using the HTK toolkit. To contextualize DUET’s performance, it is compared with modern neural separation techniques (Conv TasNet, SepFormer) paired with both traditional and self-supervised ASR back ends (Wav2Vec 2.0 and Whisper). A new corpus of 11,230 utterances from 37 speakers, representing dialectal and gender diversity, was collected. Experimen tal outcomes indicate that DUET enhances word accuracy under stereo mixing conditions; however, neural separation combined with self-supervised ASR re sults in considerably lower word-error rates and stronger robustness in noisy or overlapping-speech scenarios. The study emphasizes practical trade-offs be tween computational cost and accuracy for deploying low-resource Arabic ASR systems.
Language models and deep neural networks for Arabic named entity recognition Somia Khedimi; Abdelghani Bouziane
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp142-148

Abstract

Token type identification lies at the core of named entity recognition, allowing models to distinguish named entities from non-entity tokens and thereby better capture sentence meaning. This paper presents a deep learning approach for the Arabic named entity recognition task, leveraging deep neural networks and pretrained language models. The proposed model is a combination of the AraELECTRA language model with the bidirectional long short-term memory (BiLSTM) neural network. We utilize the WojoodNER dataset, which provides fine-grained annotations of Arabic text across 21 entity types. The results of this approach are encouraging, with an accuracy of 98.29% and an F1-score of 87%.
Multi-model deep ensemble framework for early diagnosis of rare genetic disorders using genomic, Phenotypic, and EHRdata fusion Shafin Mahmood; Sayma Akter Trina; Arpita Saha Sukanna; Sabrina Zaman Esha; Md. Agdam Amin Adib; Md. Sanim Ahmed; Amirul Islam
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp215-224

Abstract

Rare genetic disorders pose significant challenges in diagnosis because of their low prevalence, heterogeneous manifestations, and lack of readily available datasets. This study systematically assesses various supervised and unsuper vised machine learning methods for the early diagnosis of rare genetic disorders based on a multi-center pediatric dataset of 2,434 anonymized records enriched with demographic, clinical, and laboratory variables. In this study, genomic, phenotypic, and EHR variables were integrated into a unified feature matrix, al lowing all modalities to be jointly analyzed within each machine learning (ML) model. Following rigorous pre-processing steps, including the discard of nonin formative identifiers, imputation and encoding of categorical features, and nor malization of numerical predictors, five classification frameworks were imple mented: logistic regression (LR), random forest (RF), one-dimensional convo lutional neural network (CNN), a hybrid CNN long short-term memory (LSTM) model, and a stacked ensemble of RF and XGBoost. Model performances were evaluated on an independent test set via accuracy, precision, recall, and F1-score metrics. While LR and the CNN baseline achieved F1-scores of 0.9090 and 0.8572, respectively, tree-based models substantially outperformed deep learn ing (DL) models: RF achieved an F1-score of 0.9565, and the CNN+LSTM hybrid achieved 0.9611. RF+XGB ensemble achieved the highest diagnostic accuracy (98.77%) with balanced precision (0.9879) and recall (0.9877), illus trating its superior capacity in capturing complicated, non-linear feature interac tions and fighting against data imbalance. The results illustrate that bagging and boosting algorithms in combination provide a strong and interpretable frame work for efficient pre-screening of rare genetic disorders. The use of these ensemble techniques has the potential to enhance clinical practice by flagging high-risk cases for verification and facilitating early therapeutic intervention.
A decentralized call recording in voice over IP based on blockchain using smart contracts Abdelhadi Rachad; Lotfi Gaiz; Khalid Bouragba; Mohammed Ouzzif
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp164-173

Abstract

Although voice over IP (VoIP) has established itself as the new paradigm for universal telecommunications, its massive deployment within businesses and government agencies has paradoxically increased the attack surface for cyber threats: stream injection fraud, identity theft, and, more recently, the emergence of voice deepfakes, rendering traditional security architectures obsolete. At the same time, conventional centralized recording systems raise trust issues, as they are vulnerable to data manipulation, unauthorized access, and single points of failure. This article presents a new architecture that decentralizes the recording and securing of VoIP calls by combining three key technologies: blockchain for immutability; smart contracts to automate communications governance and ensure the transition from a centralized to an algorithmic trust model; and artificial intelligence (AI) agents that analyze audio streams in real time. This approach transforms VoIP recording from a simple passive file into a secure, auditable, and confidential digital asset. By removing centralized control and strengthening identity verification, this architecture provides a concrete response to security requirements.
Dilated residual U-Net for vegetation detection from high resolution drone aerial imagery Mgs. M. Luthfi Ramadhan; Rizal Maulana; Lalu Syamsul Khalid
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp115-122

Abstract

Vegetation plays a vital role in regulating air quality and mitigating climate change by converting carbon dioxide into oxygen. However, ongoing human activity continues to degrade vegetation ecosystems, necessitating scalable and accurate monitoring methods. Traditional field-based statistical approaches are often costly and inefficient. This study proposes a deep learning model, dilated residual U-Net, for semantic segmentation of vegetation from drone-acquired aerial imagery. The model incorporates residual connections to reduce infor mation loss and dilated convolutions to enhance receptive field coverage with out increasing computational cost. Experiments conducted on the DroneDeploy Segmentation dataset demonstrate that the proposed model achieves a Dice co efficient of 0.4451 with an inference speed of 0.0675 seconds per image, outper forming baseline U-Net and Residual U-Net models. These results highlight the potential of lightweight, CNN-based architectures for environmental monitoring in resource-constrained settings.
Perceived enjoyment and peer influence on adoption of virtual reality in higher education Xiaojing Jiang; Md Gapar Md Johar; Jacquline Tham
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp263-271

Abstract

Virtual reality (VR) exhibits substantial educational potential, but its adoption rate among Chinese students in higher education institutions remains low, with a lack of empirical research on influencing mechanisms, especially in regions like Nantong. This study constructed a model based on the unified technology acceptance and use theory 2 (UTAUT2), and collected 402 sample data from students of Nantong higher education institutions. An empirical study was conducted using the structural equation model (SEM). The results showed that perceived enjoyment (intrinsic motivation) and peer influence (extrinsic motivation) were positively correlated with the willingness to use VR and the adoption of VR. The willingness to use played a partial mediating role. This study innovatively proposed the synergistic driving effect of intrinsic motivation and extrinsic motivation in the context of higher education in China, and provided practical guidance for the promotion of VR in higher education.
Agraph neural network framework for vascular streak dieback recognition Slamin Slamin; Rizky Alfanio Atmoko; Antonius Cahya Prihandoko; Muhammad Ariful Furqon; Qurrota A’yuni Ar Ruhimat; Annisa Fitri Maghiroh Harvyanti; Bayu Taruna Widjaja Putra; Roslan Hasni
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp194-204

Abstract

Vascular streak dieback (VSD) is one of the most destructive diseases affecting cocoa production in Southeast Asia, including Indonesia, where early visual symptoms are often subtle and spatially distributed across the leaf sur face. Conventional image-based disease recognition approaches, particularly those relying solely on convolutional neural networks (CNNs), are effective in extracting local visual features but remain limited in modeling long-range structural relationships such as venation disruption and lesion spread. To ad dress this limitation, this study investigates a hybrid CNN-graph neural network (CNN-GNN) framework for automated VSD recognition from cocoa leaf im ages. A primary dataset consisting of 1,000 RGB images collected directly from cocoa plantations in Jember Regency was used to reflect realistic field condi tions. In the proposed approach, CNNs are employedfor local feature extraction, while graph-based representations enable GNNs to capture global relational pat terns through message passing. Experimental results demonstrate stable learning behavior and strong classification performance, achieving a maximum validation accuracy of 95.2% and an area under the curve (AUC) of approximately 0.94. Further analysis shows balanced precision and recall across classes, indicating reliable discrimination between Sehat and VSD-infected leaves. These findings suggest that hybrid CNN-GNN modeling provides an effective strategy for cap turing both local and distributed structural characteristics of VSD symptoms and highlights the potential of graph-based reasoning to complement convolutional feature learning in plant disease diagnostics.
Student activity recognition from classroom video: a survey Phuong-Dung Nguyen; Khanh-Huyen Bui; Thi-Lan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp149-163

Abstract

Student behavior and activity play a crucial role in shaping the classroom atmo sphere and influencing the quality of a learning session. Recently, vision-based student activity recognition has gained significant attention. However, recog nizing student activities from classroom videos presents unique challenges due to the nature of the classroom environment, such as the presence of multiple students and severe occlusions. As a result, research in this area has often over looked these challenges. This study provides a detailed and comprehensive re view of student activity recognition from classroom videos. First, we formalize the problem of student activity recognition from videos and categorize existing methods into three distinct approaches: frame-level, clip-level, and continuous recognition. We then provide a detailed analysis of representative methods for each approach. In addition, we present a comprehensive overview of publicly available datasets for student activity recognition and discuss key open chal lenges, together with potential future research directions. Our analysis reveals that: (1) Most existing studies focus on frame-level recognition, while clip-based and continuous activity recognition remain relatively underexplored; (2) there is still a lack of large-scale, standardized benchmark datasets for vision-based stu dent activity recognition; and (3) existing research primarily emphasizes recog nition accuracy, whereas real-time performance and computational efficiency are rarely addressed.
Fuzzy logic–enhanced LEACH protocol for scalable wireless sensor networks Hayet Termeche; Taous Lechani; Fayçal Rahmoune
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp225-236

Abstract

This study aims to enhance the LEACH protocol by mitigating its intrinsic stochasticity through the use of fuzzy c-means (FCM) clustering. This approach enables the design of WSN protocols with improved energy efficiency, stability, and scalability. To this end, two fuzzy logic–based protocols are proposed: CFFC-LEACH for small-scale deployments and VGFC-LEACH for large-scale environments. CFFC-LEACH employs artificial intelligence to generate optimal clusters by determining the appropriate number of clusters and efficiently partitioning the sensing area. VGFC-LEACH addresses wide-area monitoring challenges by dividing the network field into virtual zones of 100 x 100 m² to reduce communication distances. Within each zone, a leader is selected in every round based on residual energy and distance to the base station (BS). Clustering is performed using FCM, while cluster heads (CH) are selected through an objective function. Compared to LEACH and EDK-LEACH, network lifetime (NL) is extended by 61.26% and 46.59% with CFFC-LEACH, and by 245.81% and 657.44% with VGFC-LEACH, respectively. Which demonstrate that the proposed protocols significantly outperform LEACH and EDK-LEACH.

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