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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 799 Documents
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A Low-Complexity Algorithm for Estimation of The P80 Rock Granulometry Indicator Based on Digital Image Processing Aybar, José; Tasayco, Pool; Kemper, Guillermo
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7180

Abstract

This work presents an algorithm that automatically measures rock size distribution during primary crushing in mining, continuously monitoring without interrupting production or causing issues in subsequent crushing stages due to oversized rocks. Existing commercial solutions demand significant investment and infrastructure, making them unfeasible for small mining operations. As a result, these companies typically halt production and send samples to a laboratory to determine the P80 granulometric factor. Most current research focuses on edge detection and rock segmentation, often overlooking the estimation of granulometric parameters. The proposed algorithm uses low-complexity image processing techniques that can run on a small-board computer. Images are captured by a camera positioned above a conveyor belt within an image acquisition enclosure and processed using watershed segmentation and morphological operations like erosion and dilation. The P80 value is estimated through the Rosin-Rammler linearization model. Results show 89% accuracy compared to laboratory measurements and a 91.3% success rate.
Comparing Neural Networks and Linear Regression for Power Prediction in Electrical Motor-Driven Compressors Izzuddin, Tarmizi Ahmad; Nazir, Humam; Zulkafli, Nur Izyan; Bin Sulaima, Mohamad Fani; Jali, Mohd Hafiz; Hashim, Haslenda; Jayiddin, Nur Saleha; Md Lasin, Azmi; Iskandar, M Tarmidzi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7777

Abstract

Electrical motor driven compressors are among the most energy-intensive components in LNG regasification plants, making accurate power consumption prediction is essential for cost reduction and emission control. Traditional methods, such as Multiple Linear Regression (MLR) are limited by their assumption of linearity, while Artificial Neural Networks (ANNs) offer greater flexibility in modelling nonlinear and dynamic compressor behavior. This study compares MLR and ANN models using real-time data from two Boil-off Gas (BOG) compressors and two Regasification Terminal Export Compressor (RGTEC) compressors. The results show that ANN consistently performs better than MLR. It achieved R² values of 98.3%, 99.9%, 99.9%, and 91.7% for the four compressors. In comparison, MLR reached R² values of 97.1%, 98.5%, 99.7%, and 64.1%. The ANN models also produced lower error magnitudes, including MAE and RMSE. This was especially true under unstable operating conditions when linear models failed to fit properly. Unlike previous studies that relied on simulations or single-method analysis, this research offers one of the first direct comparisons between linear and nonlinear models applied to real-time LNG compressor data. It highlights the practical benefits of ANN for data-driven energy forecasting and optimizing operations in the gas industry. The findings emphasize the value of data-driven methods, particularly neural networks, for improving energy forecasting and operational optimization in the gas sector.
Quartz Crystal Microbalance (QCM) Sensor Array with Varying PMMA Coatings for Coffee Roasting Aroma Monitoring Muttaqin, Adharul; Sakti, Setyawan Purnomo; Naba, Agus; Mudjirahardjo, Panca
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7760

Abstract

This study investigates how polymethyl methacrylate (PMMA) coating concentration (3–15 wt%) tunes the response of an eight-channel Quartz Crystal Microbalance (QCM) sensor array to real coffee roasting volatiles at 200–240 °C. One channel was left uncoated as a reference, while seven channels were coated with different PMMA concentrations to introduce controlled response diversity. Baseline-corrected frequency shifts were processed into qualitative features describing response magnitude, kinetics, and early recovery, and principal component analysis (PCA) was used to visualize multichannel pattern structure across repeated roasts. Consistent temperature-dependent response patterns were observed, while run-to-run variability increased at higher temperatures. The first two principal components captured ~75% of the total variance (PC1 dominated by integrated response magnitude and PC2 reflecting kinetic variability). Because chamber humidity increased during roasting, a supplementary robustness check was performed using recorded RH; temperature-dependent structure remained after accounting for humidity effects. Overall, discrimination arises from the collective multichannel response, suggesting potential applicability of PMMA-coated QCM arrays for qualitative coffee roasting monitoring, pending further validation with larger datasets and complementary analytical methods.
A Stacked Classifier Model for Enhanced Student Performance Prediction in E-Learning Environments H, Sajithunisa; J, Jeyachidra
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7414

Abstract

The rapid growth of e-learning platforms has resulted in an enormous amount of student interaction data, creating opportunities to anticipate learning outcomes and implement timely interventions. In this research, a Stacked Classifier Model (SCM) is introduced to predict student performance using e-learning reaction data obtained from a Kaggle repository. The SCM employs a hierarchical ensemble approach by combining several base classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) networks—to capitalize on their respective strengths while compensating for individual limitations. The dataset underwent careful preprocessing, including imputation, encoding, feature normalization, and temporal aggregation, to ensure the classifiers received high-quality input. Evaluation results indicate that the SCM outperforms each base model individually, demonstrating its capability to capture complex behavioral patterns in e-learning contexts. Overall, this study highlights the effectiveness of ensemble learning techniques in educational data mining, offering a solid foundation for adaptive learning, personalized interventions, and enhanced academic performance.
A Critical Review of Fault Detection and Diagnosis in Crystalline Silicon Photovoltaic Systems: From Cell-Level Degradation to Array-Level Failures Tebay, Imane; Abbou, Ahmed; Ait Talount, Hachem
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7890

Abstract

The long-term reliability of photovoltaic (PV) systems depends on the timely detection and diagnosis of faults. Crystalline silicon (c-Si) technology dominates the global PV market, and is susceptible to a wide range of degradation modes. This review provides a structured analysis of these faults, categorizing them into material-level intrinsic defects, environmentally-induced extrinsic faults, and system-level interconnection faults. The review details the underlying mechanisms of key degradation modes, including Light- and Elevated Temperature-Induced Degradation (LeTID), Potential Induced Degradation (PID), and micro-crack propagation. A critical evaluation of corresponding Fault Detection and Diagnostic (FDD) methodologies follows. It encompasses laboratory-grade imaging techniques, field-deployable electrical analysis, and emerging data-driven approaches leveraging machine learning and unmanned aerial vehicles (UAVs). This synthesis reveals a fundamental trade-off between diagnostic resolution and operational scalability. To navigate this trade-off, the study analyzes the evolution towards integrated, tiered monitoring strategies and hybrid data-fusion techniques. Furthermore, the review identifies persistent research gaps, such as the need for explainable artificial intelligence (XAI), standardized datasets, robust transfer learning models, and cyber-secure FDD architectures. By bridging the fundamental science of cell degradation with the system-level engineering, this article serves as a roadmap for advancing predictive maintenance and ensuring the sustainability of large-scale PV infrastructure.
Ensemble Machine Learning for Marker-Free GAIT-Based Human Authentication B, Amogha; Deshpande, Rohini; Kumari N, Prameela
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7044

Abstract

attern of walking is called as GAIT. This pattern can be used to authenticate persons from distance. I-GAIT is a method where Indian people are identified and authenticated using walking pattern. Biometric-based authentication systems are designed to authenticate people using their behavioural and physical patterns. After pandemic COVID-19, people adopting to contactless authentication systems. GAIT is in great demand as authentication is contactless. This paper proposes methodology to authenticate 240 persons who walked in indoors and outdoors in a complex environment. Each person is made to walk in three different situation such as normal walking (NW), holding bag (HG), and wearing a coat (WC). Authentication is achieved by calculating distance between two, four, five and crossbody human joints and morphological feature. Distance of human body is determined by extracting the landmarks from the color image of person using mediapipe. Features are trained using five machine learning methods such as random forest, XGboost, LightGBM, gradient boosting and logistic regression. Human recognition is performed by using hard voting, where the majority voted human class is provided as final predicated class. Authentication accuracy to indoor database is 83% and for outdoor database is 86%.
AI-Driven Intrusion Detection and Threat Mitigation in Zero Trust Architectures Egho-Promise, Ehigiator Iyobor; Ekereuke, Udoh; Gashi, Edita; Asante, George
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7706

Abstract

This study explores the role of blending Artificial Intelligence (AI) with Zero Trust Architecture (ZTA) to boost cybersecurity through a solid Intrusion Detection System (IDS). Using a design science and postpositivist approach, the research crafts and assesses AI-driven IDS models such as Decision Tree, Random Forest, Long Short-Term Memory (LSTM), and Transformer by leveraging the University of New South Wales - Network Benchmark 2015 ( UNSW-NB15) dataset within a simulated ZTA environment. The study evaluates how well these models perform based on metrics like accuracy, precision, recall, and F1-score to pinpoint the best architecture for spotting both insider and external threats. The results show that the Transformer-based IDS outshines the others with an impressive 99.2% accuracy rate, proving its exceptional ability in real-time anomaly detection and threat classification. These findings highlight that merging deep learning with ZTA’s identity-based access control significantly boosts network resilience and helps reduce lateral movement threats. This research presents an innovative AI-ZTA integrated model designed for proactive, adaptive, and scalable cybersecurity defense. It also offers valuable insights for organizations aiming to strengthen digital trust and policy frameworks in the face of increasingly sophisticated cyber threats.
Design And Control Of A Standalone Photovoltaic Power System For Telecommunications In Isolated Regions Of Algeria Hamza, Sahraoui; Moussa Mohamed, Ali; Mellah, Hacene; Maafa, Amar; Yahiou, Abdelghani; Taieb, Bessad; Mechnane, Farouk; Kamal, Baazouzi; Said, Drid; Chrifi-Alaou, Larbi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7679

Abstract

In this paper, we describe the design, control, and management of a photovoltaic (PV) power supply system for a remote telecommunications facility situated in Bab Ezzouar, Algiers, Algeria, that maintains Base Transceiver Stations (BTS) in remote areas where the intermittent grid is either unavailable or non-existent. The proposal is developed and modeled in MATLAB/Simulink for a freestanding PV-battery hybrid system that utilizes a DC-DC boost converter regulated by a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The proposed system endeavour’s to maximize solar energy harvesting while maintaining a constant energy supply for remotely-situated telecommunication BTS without grid connection in a variety of environmental conditions. The simulation results indicate effective MPPT and battery charge-discharge management performance, ensuring BTS autonomy and serviceability twenty-four hours a day, while demonstrating both technical feasibility and operational efficiency associated with the use of PV-based systems to meet the energy needs of isolated telecommunication infrastructure in North African regions, which have considerable solar potential.
Green Machine Learning for Smart Grid Stability Prediction: Performance and Energy-Efficient Evaluation Hadji, Atmane; Boumaza, Farid; Hadji, Fatah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7377

Abstract

The increasing complexity of modern smart grids necessitates intelligent and sustainable predictive models that ensure system stability while minimizing computational energy consumption. This study explores the concept of Green Machine Learning (GML), which integrates high predictive accuracy with energy-efficient computation to promote sustainability in smart grid systems. Unlike conventional benchmarking studies, we propose a sustainability-oriented evaluation framework based on a dual-metric approach (GreenScore and GreenScore*), enabling the joint assessment of predictive accuracy and computational efficiency. This framework serves as a decision-support tool for selecting models under energy and operational constraints. The results demonstrate that MLP has reached the highest level predictive performance (F1 = 0.9736, AUC = 0.9957), while LightGBM offered the best compromise between accuracy and computational efficiency (F1 = 0.9685, AUC = 0.9941). Although Logistic Regression exhibited minimal energy consumption (execution time = 0.03 s), its accuracy was relatively low (0.8027). According to GreenScore and GreenScore*, LightGBM (GreenScore = 0.66, GreenScore* = 0.2646) and Extra Trees (GreenScore = 1.24, GreenScore* = 0.9449) demonstrate superior energy sustainability, while MLP (GreenScore* = 0.0161) and CatBoost (GreenScore* = 0.2171) reflect lower efficiency. Logistic Regression, despite very low computational cost, has a high GreenScore* (533.8422) due to its extremely low execution time but poor predictive performance. Overall, the study confirms that Green Machine Learning enables a multi-objective optimization between predictive performance and energy efficiency, advancing the development of sustainable smart grid management systems.

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