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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 
Optimization Of IT Project Management Processes Based on Neuro-Fuzzy Mathematical Modeling Olena Skliarenko; Oleksandr Nikolaievskyi; Yanina Kolodinska; Leonid Lytvynenko; Hanna Tereshchuk
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.v14i3.8112

Abstract

The study addressed adaptive software-effort estimation under multifactor uncertainty by formalizing and evaluating a neuro-fuzzy project management model that integrated interval type-2 fuzzification, local weighted regression, Energy-Efficient BAT parameter optimization, and sequential online updating. Empirical verification used four public software-effort datasets: NASA93, Desharnais, Albrecht, and Kemerer. Dataset-specific predictors were processed independently, while software development effort was used as the target. All preprocessing, fuzzy partitioning, and parameter tuning were fitted only within training folds. Repeated five-fold cross-validation with ten repetitions and predict-then-update online adaptation were applied to prevent target leakage. The corrected model achieved a mean magnitude of relative error of 0.021±0.006, root mean square error of 0.011±0.003, prediction at 25% of 0.989±0.012, correlation of 0.969±0.010, and prediction index of 0.918±0.015. The results showed a balanced accuracy-adaptability trade-off relative to the selected baselines, while external enterprise validation remained necessary
Brain Tumor Analysis Using Multi-Scale CNN and Gated DenseNet-169 Ch Muralikrishna; Shanker Chandre
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.v14i3.7233

Abstract

Brain tumors refer to the uncontrolled growth of cells in the human brain, posing a significant risk to human health. Therefore, early detection of these tumors are crucial. However, existing feature extraction approaches often fail to capture the intricate characteristics of tumors, leading to suboptimal classification performance. To address this limitation, a Multi-scale Convolutional Neural Network and Gated Channel Transformation unit-based DenseNet-169 model (MC-GCT-DenseNet-169) is proposed for efficient brain tumor classification. The multi-scale convolutional neural network captures the nonlinear relationships between texture, shape, and intensity in magnetic resonance images (MRI). The GCT function in the DenseNet-169 model enhances the ability to learn subtle variations in MRI such as tumor texture and boundary features, which are essential for accurate classification. Experimental results show that the MC-GCT-DenseNet-169 method attains accuracy of 98.97% on the Figshare dataset, surpassing previous approaches such as Cross-prior Bayesian attention mechanism with a Correlated Inception and Residual Learning Network (CB-CIRL Net).
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.
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.
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.
Leveraging Self-Supervised Transformers for Heat Pump Consumption Classification Using the HEAPO Dataset Hoang Thi Thu; Phi Dinh Huynh; Ta Trung Kien; Le Mai Nam; Le Hong Duy; Pham Hong Thinh; Nguyen Viet Hung
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.v14i3.7684

Abstract

Heat pumps play a crucial role in reducing carbon emissions in residential heating; however, their electricity consumption varies significantly across households, making large-scale performance assessment challenging. Existing data-driven approaches often rely on fully supervised learning and require extensive labeled data, which are costly to obtain through on-site inspections. This paper investigates whether routinely collected smart meter and weather data can be leveraged to automatically classify household heat pump electricity consumption into three categories: low, normal, and rather high. We propose a self-monitoring transformer framework that combines self-supervised pretraining and supervised fine-tuning for time series classification. A transformer encoder is first pre-trained on multi-day electricity consumption windows from largely unlabeled households using a reconstruction-based objective. The learned representations are then fine-tuned with a lightweight fully connected classifier on a limited labeled subset of the HEAPO dataset. Experimental results demonstrate that the proposed approach consistently outperforms state-of-the-art baselines, including RNN-, CNN-, and transformer-based time series models, achieving approximately 93% classification accuracy, an F1 score above 0.9, and a recovery rate close to 91%. These results indicate that self-supervised representation learning on smart meter data provides an effective and scalable solution for monitoring heat pump electricity consumption and supporting inspection planning.
DNN-Based Phase Prediction for Distributed Multi-RIS Systems under Co-Channel Interference Huu Q. Tran; Phan Tran Truong Dien; Ly Nhan Hau
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.v14i3.7781

Abstract

This paper investigates learning-assisted 2-bit phase control for a distributed multi-reconfigurable-intelligent-surface (multi-RIS) downlink under co-channel interference (CCI). A desired base station serves a single-antenna user while two neighboring base stations reuse the same time–frequency resource. We consider exhaustive RIS activation (ERA), where all surfaces reflect simultaneously, and opportunistic RIS activation (ORA), where the RIS providing the largest instantaneous signal-to- interference-plus-noise ratio (SINR) is selected. The proposed method uses channel-state-information (CSI)-derived desired and interfering cascaded-channel features and an offline discrete coordinate-ascent (DCA) teacher that maximizes the high-SNR signal-to-interference ratio (SIR). For ERA, the DNN learns 2 bit residual corrections relative to nearest-neighbor quantized phase alignment (QPA). Because about 90% of ERA teacher coordinates require no correction, class-balanced cross-entropy is combined with a utility-aware DCA–DNN SIR-gap loss and post-warm-up utility-based checkpoint selection. ORA retains absolute phase-state prediction. Fixed phases, QPA, DCA, and continuous-phase alignment (CPA) are used as common baselines. On an independent test set, the mean DCA–DNN SIR gaps are 0.0435 dB for ERA and 0.0843 dB for ORA. At Ps = 25 dBm, the DNN ergodic-capacity gaps to DCA are only 0.0040 b/s/Hz and 0.0063 b/s/Hz, respectively. High-SNR analysis further establishes nonzero outage floors and finite capacity ceilings when all co-channel transmit powers scale together. The results position the DNN as an iteration-free surrogate for interference-aware discrete phase control while confirming that QPA remains a strong low-complexity baseline.
Solar Drying System Based on Multi-Parameter AI Model Do Van Can; Nguyen Duy Khiem; Nguyen Minh Vuong; Le Thi Ngoc Loan
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.v14i3.7882

Abstract

Agricultural drying strongly affects post-harvest product quality and storage stability. This study develops a smart solar drying system integrating multi-parameter sensing, electrical storage, water-based thermal storage, an Industrial IoT V-box, and a Siemens S7-1200 PLC. Temperature, relative humidity, solar irradiance, airflow, and COâ‚‚ concentration were monitored to characterize the drying environment. A hierarchical Random Forest framework was developed to estimate the chamber temperature, relative humidity, and coffee moisture content. Leave-one-day-out validation yielded pooled R2 values of 0.909 and 0.890 for indoor temperature and relative humidity, respectively. The moisture model achieved R2=0.882, MAE = 0.472 percentage points, and RMSE = 0.740 percentage points against the sensor reference. The Random Forest models were executed on the V-box, while the PLC acquired sensor data and controlled the actuators. The results demonstrate the feasibility of integrating data-driven estimation with PLC-based supervision for solar coffee drying.
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.
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 COâ‚‚ 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 COâ‚‚ 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.

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