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International Journal of Electrical Engineering, Mathematics and Computer Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481910     EISSN : 30481945     DOI : 10.62951
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The scope of the this Journal covers the fields of Electrical Engineering, Mathematics and Computer Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 34 Documents
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Smart Protection System in Power Distribution Using Internet of Things (IoT) Technology Prasetyo, Yuli; Kumala Mahda H; R. Oktav Yama H; Narava Kansha P
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.312

Abstract

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.
Analysis of the Impact of Load Imbalance on Neutral Current and Power Losses Caused by Neutral Current in Transformer 1, 30 MVA, 70/20 kV at Bungaran Substation Azis, Abdul; Perawati; Yudi Irwansi; Muhammad Rizal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.318

Abstract

Power transformers are crucial in the electrical distribution system, and their operational stability is significantly affected by load imbalance among phases. Load imbalance can lead to the flow of neutral current through the neutral conductor, causing additional power losses in the transformer. This study analyzes the impact of load imbalance on neutral current and power losses at Transformer 1 (30 MVA capacity, 70/20 kV) at the Bungaran Substation. Data such as phase current, neutral current, and power losses were measured at 12:00 and 21:00. At 12:00, the transformer’s full-load current was 839.17 A with a loading of 28.44% and a load imbalance of 0.74%, resulting in a neutral current of 4.36 A (1.83% of load current). The power loss due to neutral current was 12.64 W (4.36×10-5 %), and the loss due to neutral current flowing to the ground was 760 W (2.62×10-3 %). At 21:00, the full-load current decreased to 834.46 A, with a loading of 29.36% and a higher load imbalance of 1.36%. This caused a neutral current of 7.94 A (3.24%), with a power loss of 41.90 W (1.43×10-4 %) and a ground power loss of 2.52 W (8.60×10-3 %). The power losses were minimal compared to the transformer’s capacity, having little effect on system efficiency. However, maintaining load balance is essential for system efficiency and transformer longevity.
Baseline Vapour Pressure Deficit Dynamics in an Emersed Anubias Cultivation Incubator: Sensor Construct Validity and Humidity-Governed VPD Drivers Al Barra Harahap; Vera Khoirunnisa; Aprilia Siregar; Sintya Shety Simanjuntak
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 3 No. 2 (2026): June: International Journal of Electrical Engineering, Mathematics and Computer
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v3i2.390

Abstract

Vapour pressure deficit (VPD) has gained recognition as a physiologically superior microclimate control variable in controlled-environment agriculture, yet no quantitative VPD characterisation exists for emersed cultivation of variegated Anubias barteri var. nana 'Pinto'. This study characterises the VPD microclimate of an ebb-and-flow cyber-physical incubator equipped with an MLX90614 infrared radiometric sensor, two calibrated DHT22 humidity sensors, a DS18B20 nutrient solution sensor, and an ESP32-S3 microcontroller. A 5.36-hour passive commissioning session yielded 1,884 valid data rows at a 10-second logging interval. The mean baseline  was 0.703 ± 0.022 kPa, falling within the upper portion of the reference envelope inferred from available literature on emersed Anubias acclimatisation. Indoor relative humidity was identified as the dominant VPD driver (r = −0.695, R² = 0.483), while air temperature showed negligible association (r = −0.008), supporting mist actuation as the appropriate primary control strategy. The MLX90614 recorded a consistent radiometric offset of ΔT_rad = −1.61 ± 0.10 °C relative to indoor air temperature, reflecting the composite FOV average of cooler evaporative surfaces; the resulting  was 0.327 kPa (32%) lower than the air-temperature-only estimate, demonstrating that the choice of surface temperature reference has a material effect on the computed VPD. A persistent ambient VPD gradient of +0.540 kPa was documented throughout the session, quantifying the continuous moisture gradient from the incubator interior to the ambient environment. These results establish a quantitative VPD baseline for emersed Anubias cultivation, define the construct validity boundaries of the sensor suite, and provide the empirical foundation for a companion closed-loop Fuzzy-PI VPD controller.
A Statistically Validated Comparison of CNN Architectures and Optimizers for Brain Tumor Classification on MRI Im-ages I Made Dharma Yoga Pratama; Syadia Nabilah Binti Mohd Safuan
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 3 No. 2 (2026): June: International Journal of Electrical Engineering, Mathematics and Computer
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v3i2.391

Abstract

Brain tumors are a major global health burden, and Magnetic Resonance Imaging (MRI) is the primary modality for their detection. Manual interpretation of MRI is time-consuming and subject to inter-observer variability, motivating automated classification using Convolutional Neural Networks (CNNs). A recurring weakness in existing studies is the comparison of CNN architectures and optimizers using single-run accuracy without formal statistical testing, leaving it unclear whether reported differences are genuine or due to random training variation. This study presents a statistically validated comparison of four CNN architectures (AlexNet, VGG-16, InceptionV3, and MobileNetV2) for four-class brain tumor classification (glioma, meningioma, pituitary, and no tumor) on the Kaggle Brain Tumor MRI dataset (7,023 images). AlexNet was trained from scratch, whereas the other three used feature-extraction transfer learning with frozen ImageNet-pretrained backbones. All models were evaluated using 5-fold stratified cross-validation, and architecture differences were assessed using one-way ANOVA with Tukey HSD post-hoc testing and Cohen's d effect sizes. The best architecture was then optimized with Adam and Stochastic Gradient Descent with Momentum (SGDM), compared using a paired t-test at the 0.05 level. AlexNet achieved the highest mean test accuracy of 96.63%, significantly outperforming VGG-16 (92.60%), InceptionV3 (88.89%), and MobileNetV2 (88.25%), with large effect sizes. Adam and SGDM produced statistically equivalent accuracy (97.38% versus 96.52%). The results indicate that learning domain-specific features from scratch can surpass non-fine-tuned transfer learning, and that the choice between Adam and SGDM does not significantly affect accuracy.

Page 4 of 4 | Total Record : 34

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