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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 858 Documents
 82   83   84   85   86 
Integrating SeqGAN and NLP Augmentation to Improve Generalization in Sentiment Classification Models Shafin Mahmood; Sayma Akter Trina; Md. Easin Khondokar; Nuzhat Tabassum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7934

Abstract

Sentiment analysis is essential for understanding people's experiences and social media trends. However, challenges such as imbalanced datasets, limited data availability, and the informal nature of user-generated content often degrade a model's ability to achieve higher accuracy. This study presents that data augmentation using SeqGAN can effectively addresses these issues and improve prediction performance compared to traditional NLP-based augmentation techniques. A variety of Machine Learning (ML), Deep Learning (DL), hybrid, and GAN-based models were evaluated to predict sentiment from social media comments. After generating synthetic data using both SeqGAN and NLP-based techniques, all models were retrained, and notable improvements in accuracy were observed with the SeqGAN-augmented dataset. The use of synthetically generated data led to a statistically significant increase in classification performance, with a mean absolute accuracy improvement of approximately 4–5% across the full dataset. These findings highlight that GAN-based data augmentation, particularly through tailored synthetic sample generation, can enhance model robustness and boost sentiment prediction accuracy more effectively than conventional augmentation approaches.
Interpretable AI Pipeline for Ranking Influential Depression Factors from the Burns Depression Checklist Tumpa Rani Shaha; Momotaz Begum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7447

Abstract

The Burns Depression Checklist (BDC) measures depression severity by adding the scores of 25 symptom items. However, the total score does not show which items contribute most to the severity category. This study presents an interpretable machine learning pipeline to rank predictive BDC items. We analyzed responses from 654 university students in Bangladesh. We first examined the reliability and relationships of the BDC items. We then tested seven machine learning classifiers. We also applied four feature selection methods—RFE, SKB, UFS, and MIFS—and two explanation methods, SHAP and LIME. The BDC showed strong internal consistency, with a Cronbach’s alpha of 0.91. The full-item stacking model achieved 90.84% test accuracy, 86.03% balanced accuracy, and 82.60% macro-F1. We combined the six item rankings on one common scale. Loss of motivation received the highest rank. Loss of interest in work, loneliness, discouragement, hopelessness, and reduced social interest also ranked highly. These items represent reduced motivation, social withdrawal, negative mood, and negative self-view. The main contribution of this study is an item-level ranking that combines evidence from several analytical methods. However, the target categories come directly from the same 25 BDC items. Therefore, the results show how well the models reconstruct BDC severity categories, not how well they diagnose clinical depression. The rankings also do not show clinical urgency. Any positive response to a suicide related item requires separate professional assessment.
Parameter estimation and optimal control strategies for sustainable management of mangrove forests Nurul Eliana Atika Mohd Idrus; Sie Long Kek
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7371

Abstract

This paper presents an integrated computational framework combining parameter estimation and optimal control for sustainable mangrove forest management. We begin by addressing uncertainty in model parameters using a data-driven solution. A least-squares optimization problem is solved using the Levenberg-Marquardt algorithm to recursively estimate parameters in the dynamic mangrove ecosystem model. Later, we formulate an optimal control problem to drive mangrove populations toward ecological steady states as rapidly and efficiently as possible. To this end, we define the Hamiltonian function and derive the necessary optimality conditions to design optimal control policies. Our key contribution lies in unifying parameter estimation and control synthesis into a single, implementable algorithm tailored for mangrove dynamics. Numerical simulations demonstrate the algorithm’s efficacy: parameter estimates achieve high accuracy, as evidenced by low mean squared error, while the optimal control policy minimizes a defined performance index, ensuring ecological and operational efficiency. This integrated approach offers decision-makers a data-driven, mathematically rigorous tool to guide restoration and conservation efforts. By bridging estimation and control, our methodology enhances adaptive management of mangrove forests, supporting long-term ecological resilience and sustainability in dynamic coastal environments.
Modeling and Simulation of a Renewable Energy-Integrated Distribution Network in Boujdour Boujemaa Hamma; Ahmed Abbou
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.8140

Abstract

This paper presents a system-level modeling and simulation study of the 22 kV distribution network of Boujdour, southern Morocco, integrating photovoltaic (PV) and wind energy. The PV and wind models are validated using real operational SCADA data. Two operating conditions are analyzed: grid-connected operation with PV injection and a main-grid-loss condition with combined PV–wind generation. The results show how renewable intermittency affects power balance and medium-voltage voltage behavior and identify infrastructure constraints that limit further renewable penetration. The study provides a validated operational baseline for future smart-grid extensions involving dynamic inverter models, energy storage, advanced monitoring, and data-driven forecasting.
CLF-LDP Driven CO₂ Emission Forecasting for Smart Electric Vehicles Rabab Hamed M. Aly; Aziza I. Hussein; Naveed Anwar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7950

Abstract

Electric Vehicle (EV) networks play a crucial role in advancing environmental sustainability; however, inefficient charging and energy distribution strategies can contribute to increased CO₂ emissions. This study proposes a hybrid Crow–Lévy Fuzzy Optimization combined with Linear Discriminant Prediction (CLF-LDP) framework to enhance emission forecasting accuracy and support energy-efficient decision-making in EV systems. The proposed approach integrates the global search capability of the Crow Search Algorithm (CSA), enhanced by Lévy flight exploration and fuzzy logic–based feature refinement, followed by discriminant-based predictive modeling. Experimental evaluation demonstrates that the CLF-LDP model achieves approximately 97% predictive accuracy, with a test R² of 0.90 under weighted training and 0.966 after regularization-based comparison. In contrast to high-capacity deep learning architectures that exhibited instability on unseen data, the proposed optimization-driven framework maintains consistent predictive robustness while preserving computational efficiency. Furthermore, integration of the predictive equation into a Hybrid Electric Vehicle (HEV) Simulink environment validates its applicability beyond offline regression analysis. By embedding CO₂ prediction within the vehicle energy management system, the framework enables anticipatory emission-aware control rather than conventional rule-based adjustment. Overall, the CLF-LDP framework provides a scalable and computationally efficient solution that bridges predictive intelligence with real-time energy management, contributing to the development of sustainable and emission-aware smart transportation systems.
Monitoring System using Artificial Vision and YoloV8 for Accident Prevention in Children Johnny Angelino; Andrés Quispe; Ricardo Yauri
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.8043

Abstract

The increase in student density in educational institutions has generated greater risks to child safety, especially in high-traffic scenarios where manual supervision is limited to prevent accidents. In this context, the need arises to implement non-invasive technologies and systems based on artificial vision, to improve monitoring and support traditional surveillance methods. Previous research describes the use of computer vision techniques and models such as YOLO for safety, monitoring, and their integration into low-cost embedded systems, with limitations in real-time response or accident prevention. This research shows the development of a monitoring system based on artificial vision with a YOLOv8 model trained and deployed on a Raspberry Pi with integration with an ESP32 to detect risk events aimed at preventing accidents in minors. The results indicate that risk classes are detected with confidence levels above 70%, with good performance in metrics such as precision (~0.9), recall (~0.8) and mAP50 (~0.9), without overfitting and with hypothesis validation. In conclusion, the system is a suitable solution for accident detection with metric validation, but it needs to be optimized to work in real-world heterogeneous scenarios.
Forecasting Irrigation Shifts in Fruit Cultivation Under Climate Change Scenarios in Tropical Thailand Pattharaporn Thongnim; Jakkrapan Sreekajon; Phaitoon Srinil
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7302

Abstract

Irrigation management in tropical fruit orchards is most difficult during the tran- sition from the dry to the wet season, when atmospheric demand is high and rain- fall is erratic. This study presents a web-based decision-support framework that converts hourly meteorological records into irrigation requirements and evalu- ates how they shift under climate perturbation. Vapor pressure deficit (VPD) and hourly reference evapotranspiration (ETo) are computed with the FAO-56 Penman-Monteith equation, scaled by stage-specific crop coefficients and con- verted to a per-tree volume using canopy radius. The implementation reproduces the worked hourly values of FAO-56 Example 19 exactly. For a representative day in Chanthaburi, Thailand, the net requirement for durian rose from 3.53 to 7.05 liters per day for a tree of 1.0 m canopy radius under a perturbation of +2 ◦C and −20% relative humidity. Because this result depends on how hu- midity is assumed to respond to warming, the calculation was repeated under three assumptions which gave increases in mean VPD of between 12.1 and 69.9 percent and in ETo of between 4.8 and 20.4 percent. The direction of the shift is therefore robust while its magnitude is assumption-dependent and irrigation requirements are reported as a range rather than a single value.
Accuracy and Stability Trade-off in RSSI Window Aggregation for MLP-Based Wi-Fi Indoor Positioning Wahyu Arthanugraha; Abdul Halim Ismail; Shahrul Nizam Yaakob
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.8256

Abstract

Wi-Fi RSSI fingerprinting is practical for indoor positioning, but short-term signal fluctuations can reduce classification consistency. This study evaluates how fixed RSSI window size affects the accuracy, stability, and sample availability of an MLP-based indoor positioning system. RSSI from three Access Points was collected at 45 ordered corridor positions. Non-overlapping windows with n=1,3,5,7,9,11,15,21 and 29 were represented by the mean and standard deviation of three RSSI channels. The MLP was assessed using stratified 5-fold cross-validation with supplementary order-blocked validation. Relative to the n=1 baseline Accuracy of 68.24%, n=7 achieved 90.60 ± 0.71% Accuracy, 95.08 ± 1.31% Top-2 Accuracy, 0.9047 ± 0.0060 Macro-F1, and 0.4319 ± 0.1364 Ordinal Error. Larger windows achieved higher mean Accuracy but sharply reduced window-level samples and increased fold-to-fold variability. Order-blocked validation produced more conservative estimates and shifted the strongest performance region toward n=9 to n=11. These findings show that fixed-window selection should consider predictive performance, evaluation stability, sample availability, and validation design rather than maximum Accuracy alone.
Comprehensive Temperature Mapping and Stratification Study of an Ambient Pharmaceutical Warehouse Influenced by Local CTU Heat Sources Prasantha Pujari
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7998

Abstract

This paper reports a temperature mapping study of a GMP-regulated ambient pharmaceutical warehouse of about 10,000 square feet (930 m²) whose storage space borders an interface zone with 44 controlled temperature units (CTUs). The study followed a risk-based IQ/OQ/PQ sequence with 63 calibrated wireless loggers sampling at 5-minute intervals in three 72-hour sessions: the empty warehouse (OQ, October 2023), the warehouse loaded to about 80 % (PQ, October 2023) and a winter session (February 2024). In the empty warehouse the mean over all channels was 21.87 °C; the global minimum (18.65 °C) was recorded on a wire-rack shelf and the global maximum (27.58 °C) at a logger mounted on top of a refrigeration unit, at 04:20, outside working hours. In the loaded session the sharp equipment-cycle peaks of the empty session were absent and a slow diurnal wave dominated, with isolated peaks of about 30 °C on working days. In winter, regular peaks of 30–35 °C coincided with heating cycles, and one channel started above 40 °C and ran at 28–32 °C for the rest of the session. The paper states the mean kinetic temperature (MKT) equation and its activation-energy convention and explains why MKT cannot replace a duration-based evaluation of excursions. The underlying time series and per-channel statistics could not be released, so the results are presented as a documented case with explicit limitations. The practical outcomes are recommendations on destratification, a buffer zone around heat-rejecting units and the placement of permanent monitoring points.
Controller-Side Reliability of Edge-Deployed Thai Speech and Gesture Smart-Load Control on Raspberry Pi 5 Neres Supalugsanasuksakorn; Sakron Taeanonjiew; Apinun Khonmanee; Saweth Hongprasit; Wachirapunya Punyawong
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.8104

Abstract

This paper presented an edge-deployed smart-load control system on Raspberry Pi 5 using independent Thai speech and vision-based finger-gesture channels mapped to a common ten-command GPIO actuation layer. Rather than proposing a new recognition algorithm, the study evaluated controller-side execution reliability using GPIO-confirmed binary outcomes and supplemented the human-trial evaluation with system-level engineering profiling. Structured trials examined interaction distance and background speech, yielding 90.0% execution success for both voice (54/60) and gesture (63/70), with different condition-specific performance patterns. A separate 10.5-min integrated profiling run measured computational behavior on the Raspberry Pi 5. Median processing latency was 46.90 ms for Vosk audio blocks and 72.15 ms for MediaPipe hand processing, while median GPIO software-update latency was 0.0688 ms. Median system CPU utilization was 32.30%, process memory was 427.55 MB, and CPU temperature was 83.70 °C; non-zero throttling-status values were observed during sustained workload. These results characterized both execution reliability and resource constraints for non-critical smart-load control on Raspberry Pi-class edge hardware.

Page 84 of 86 | Total Record : 858

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