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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
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Comprehensive Temperature Mapping and Stratification Study of an Ambient Pharmaceutical Warehouse Influenced by Local CTU Heat Sources Pujari Prasantha
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.
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.
Self-Interference Cancellation in In-band Full Duplex Systems (IBFD) Using Radial Basis Function Neural Networks (RBFNN) Khaled Gharaibeh
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.v14i3.8090

Abstract

In-band Full Duplex (IBFD) wireless systems have attracted attention for use in emerging wireless systems as they provide significant enhancement to system capacity over traditional techniques. In IBFD, transmission and reception occur simultaneously and on the same frequency band. Thus, the main challenge in system design is self-interference (SI) where the strong transmit signal leaking to the receiver interferes with the weak received signal. SI typically includes a nonlinear component resulting from the transmitter Power Amplifier (PA); therefore, the resulting SI is highly dependent on transmit power. In this paper, an SI cancellation (SIC) scheme for IBFD systems is presented and verified. The proposed nonlinear SIC scheme is based on using a Radial Basis Function Neural Network (RBFNN) to model the nonlinear SI. A practically motivated feature representation which includes instantaneous power of the transmit signal is introduced, enabling efficient modelling of amplitude-dependent and memory-dependent nonlinearities. The proposed RBFNN canceller offers localized approximation capability and inherently stable training structure, making it suitable for addressing the SI dependence on transmit power. Simulation results demonstrate that the RBFNN-based SI canceller outperforms model-based cancellers such as the Memory Polynomial (MP) cancellers by 10 dB across a transmit power range of 30 dB.
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.
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
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.
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.
SpineTrace: Auditable Video-Based Scoliosis Screening with Temporal Pose Statistics Teddy Surya Gunawan; Siti Aishah Sofea Asri; Mira Kartiwi; Noreha Abdul Malik; Ahmad Hud Mahfoodz; Mohd Ghaddafi Wahab
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.v14i3.8257

Abstract

Video-derived gait representations may support scalable pre-referral screening for scoliosis, but their evaluation must retain borderline cases, quantify model vulnerability, and distinguish measured performance from clinical or deployment readiness. This study evaluates SpineTrace as an auditable compact engineering baseline rather than a novel learning algorithm or state-of-the-art classifier. The public Scoliosis1K release comprised 1,493 gait sequences reportedly obtained from 1,050 adolescents, including 493 positive, 200 neutral, and 800 negative sequences. The supplied partition of 744 training and 749 test sequences was reproduced exactly. Feature scaling and hyperparameter selection were restricted to five stratified folds within the training partition, with three-class classification designated as the primary task. Among the reproduced RBF-SVM configurations, the highest macro-F1 was obtained using 102 temporal pose features derived from keypoint means and standard deviations. The model achieved 0.737 accuracy, 0.740 balanced accuracy, 0.702 macro-F1, and 0.890 macro one-versus-rest ROC-AUC. Recall was 0.917, 0.619, and 0.685 for negative, neutral, and positive sequences, respectively, demonstrating the greater difficulty of borderline and positive cases. Adding three silhouette and five postural descriptors reduced macro-F1 to 0.656, indicating that semantically plausible handcrafted features did not provide complementary predictive information. Independently retuned PAV-SVM and DTW-kNN baselines achieved macro-F1 scores of 0.545 and 0.473, while a compact ST-GCN adaptation achieved 0.645±0.055 across three optimization seeds. Removing keypoint-confidence features from the full model reduced macro-F1 from 0.656 to 0.618, and coordinate jitter equivalent to 2% of torso length reduced it to 0.287, identifying pose quality as a critical vulnerability. Fixed-test uncertainty was estimated using 2,000 class-stratified sequence bootstraps. Because the local release lacks participant mappings and raw RGB videos, participant independence, cross-estimator transfer, end-to-end latency, and cross-dataset validity could not be established. SpineTrace therefore provides a reproducible sequence-level baseline for auditing feature utility and failure boundaries, not a diagnostic or deployment-ready screening system.

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