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Nurhayati
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Departement of Electrical Engineering Faculty of Engineering Universitas Negeri Surabaya
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INDONESIA
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 26142589     DOI : 10.26740/inajeee
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
INAJEEE or Indonesian Journal of Electrical and Eletronics Engineering (E-ISSN 2614-2589) is a scientific peer-reviewed journal issued by The Department of Electronics, Faculty of Engineering, Universitas Negeri Surabaya (UNESA). Accepted articles will be published online and the article can be downloaded for free (free of charge). INAJEEE is published periodically (2 issues per volume/year) with 5 articles each time published (10 articles per year). INAJEEE is free (open source) all to access and download. The journal includes developments and research in the field of Electronic Engineering, both theoretical studies, experiments, and applications, including: 1. Electronics Engineering 2. Power system Engineering 3. Telematics 4. Control System Engineering
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 7 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2024)" : 7 Documents clear
Body Temperature Classification System Based on Fuzzy Logic and The Internet of Things Fadli, M. Najmul; Alfiansyah, Muhammad Wisnu; Sirojul Hadi
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p35-43

Abstract

Health technology has become more efficient thanks to telecommunications and information technology. One of the utilizations is IoT detecting a person's body temperature condition. This research aims to produce a tool that can classify human body temperature by implementing fuzzy logic methods and the Internet of Things so that early prevention can be carried out against hyperthermia and hypothermia sufferers and can be used as advice for further examination to doctors. The phases in this study are analysis requirement, system design, implementation, and testing. The tools incorporated in this system are intended to measure body temperature and can classify the measured body temperature into specific categories. This research utilizes the Sensor Proximity E18-D80nK to detect the presence of an object concerning the sensor. Additionally, the system employs MLX90614 sensors for temperature measurement. The results of this research are that the use of the DHT22 sensor for measuring room temperature and the MLX80614 sensor for non-contact body temperature respectively produces a high level of accuracy, namely 98.22% and 98.29%. In addition, the implementation of the fuzzy logic algorithm succeeded in achieving 100% accuracy in classifying body temperature data, showing the effectiveness of this method in detecting human body temperature.
Portable Solar Water Pump Design And Forecasting Very Short-Term Sun Light Radiation Using Feed Forward Neural Network Method Adiwana, Moch. Nur
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p30-34

Abstract

Portable Solar Water Pump is an alternative to renewable energy that can convert solar radiation into the form of electrical energy used to pump rice field irrigation water. Solar radiation promotes increased photovoltaic absorption, leading to improved battery life time performance. Solar cells have a high efficiency value if photons from sunlight can be absorbed as much as possible. This research aims to design a Portable Solar Water Pump. After that analyze the forecasting of solar radiation Watt/  in the next hour starting from 08.00-13.00 then predicted at 14.00 using the Feed-Forward Neural Network (FFNN) method. And get an error value of 2.6. With the highest radiation value of 1285,6 Watt/ . Keywords: Photovoltaic, Portable Solar Water Pump, Solar Radiation, Feed Forward Neural Network
Design And Development Of A Pond Water Quality Monitoring Device Using The GSM SIM-800L Module Burhanudin, M. Dean
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p44-49

Abstract

The current pond water monitoring systems are still lacking, necessitating innovations in efficient and effective monitoring technology. This study aims to design and develop a pond water monitoring system using SIM800L and ESP32 modules. The method used in this research is development research, which includes the stages of design, creation, and testing of the device. This monitoring system is equipped with TDS sensors, temperature sensors, and pH sensors to measure pond water quality in real-time. The test results show that the developed device can accurately monitor pond water quality and transmit data to a server via the GSM network. In conclusion, this device is effective and can be used by users to practically and efficiently monitor pond water, thereby helping to maintain pond water quality and support better aquaculture
Prediction of Air Temperature on Runway 10 Juanda Airport Using Hybrid LSTM Anjani, Jasmine
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p50-58

Abstract

Abstract – Global climate change increases the frequency of extreme weather, impacting various sectors, including aviation. Accurate weather prediction becomes crucial to ensure the safety of human activities, including aviation operations. This study aims to predict air temperature variables on Runway 10 at Juanda Airport using a Long Short Term Memory (LSTM) architecture stacked with a Gated Recurrent Unit (GRU) architecture, named Hybrid LSTM. The data used in this study is air temperature (per minute) obtained from the Automatic Weather Observing System (AWOS) in (.csv) format. Testing was conducted for short-term predictions and comparisons were made between Hybrid and non-Hybrid models. The test results show that the LSTM-GRU architecture produced the lowest evaluation values for predicting temperature with an MSE of 0.0181, MAE of 0.0814, RMSE of 0.1345, and MAPE of 0.29% using a batch size of 64 and 20 epochs. This indicates that the developed model is suitable for predicting the next short-term period (5 minutes). For future research, model development is needed by adding features or adjusting hyperparameters to accurately predict the long term.Keywords: Predict, Air Temperature, Automatic Weather Observing System (AWOS), LSTM, GRU
Juanda Airport Runway Visibility Modeling Using Gan Based on Imbalanced Dataset Al Rahmat, Nusaibatul Zahra
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p59-69

Abstract

Abstract – Aviation safety and security are heavily influenced by airport visibility, as pilots require clear visual references for landing. However, poor weather conditions can reduce visibility and increase the risk of accidents. Therefore, an automated system is needed to classify visibility levels quickly and accurately, even when faced with the challenge of imbalanced datasets. This study employs a Generative Adversarial Network (GAN) approach, focusing on Vanilla GAN, DCGAN, and StyleGAN models. The data used is sourced from CCTV AWOS at Runway 10 of Juanda Airport, encompassing 14,458 images from the period of August 13 to 31, 2023. The models are evaluated using SSIM scores and feature extraction of color, texture, and HOG at various epochs. The results indicate that the Vanilla GAN model at 60 epochs is the most suitable for the minority class compared to the other models, based on feature evaluation, SSIM scores, synthetic image quality, and loss pattern outcomes. Its simple architecture aids in capturing low variation in the dataset, making it superior to more complex architectures like DCGAN and StyleGAN. Further optimization and architectural adjustments could enhance the results, especially for datasets with low variation like the one used in this study.Keywords: Aviation, GAN, Dataset
Forecasting Battery Capacity and Feasibility Using the Gaussian Process Regression Method Nugraha, Lardiansyah Adhwa; Wrahatnolo, Tri
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p70-75

Abstract

The 110VDC batteries at the 150kV South Surabaya Substation have a shortage in the number of units. Therefore, they require extra supervision to ensure that protection and control equipment relying on DC power sources can operate normally during rectifier system outages, preventing potentially severe disruptions at the substation. The objective of this study is to use Matlab's forecasting degradation method for battery performance using Regression Learner, aimed at facilitating operators at the 150kV South Surabaya Substation. The research focuses on forecasting battery performance degradation using Gaussian Process Regression (GPR) with datasets obtained from observed discharging and charging tests compiled in Excel format. Data analysis techniques involve building a GPR model using Matlab software and comparing forecasted results with discharging test data over two years from PT. PLN (Persero). The study concludes that a 71% battery efficiency qualifies as sufficiently reliable backup power during AC or rectifier disruptions. This ensures continuous operation of protection and control equipment during blackouts, thereby preventing operational disruptions and serious safety issues.
Prediction of Transformer Aging Loss in 150kV Waru Substation Using GRU-LSTM Method Based on Temperature and Load Mustafa, Ahmad
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n2.p76-82

Abstract

The increasing demand for electricity in Indonesia highlights transformers' crucial role in the electrical system. This study utilizes GRU (Gated Recurrent Unit) and LSTM (Long Short-Term Memory) in a deep learning framework to predict aging losses of transformer unit 5 at the 150 kV Waru Substation. The aim is to enhance grid reliability, and efficiency, and prevent disruptions like power outages. Conducted at the 150 kV Waru Substation, the research focuses on transformer loading and temperature data. Data preprocessing involves normalizing load, oil temperature, and winding temperature data. The model architecture combines GRU and LSTM to capture short-term and long-term patterns in time series data. Training employs the Adam optimizer with customized learning rates, and performance evaluation uses metrics such as MSE, RMSE, MAPE, and MAE. Results indicate the GRU-LSTM model trained with a batch size of 64 and 75 epochs achieves superior performance: MSE of 0.0000129008474202634, RMSE of 0.00359177496793207, MAPE of 0.3943965%, and MAE of 0.00000832911556912471. This model forecasts transformer 5's aging loss over the next 30 days with an average daily deterioration rate of 0.001378178 pu/day, peaking at 0.0030481415 pu. Keywords: Aging Loss Prediction, GRU, LSTM, Transformer

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