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Journal of Electrical Engineering and Computer (JEECOM)
Published by Universitas Nurul Jadid
ISSN : 27150410     EISSN : 27156427     DOI : -
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy
Journal of Electrical Engineering and Computer (JEECOM) is published by Engineering Faculty of Nurul Jadid University, Probolinggo, East Java, Indonesia. This journal encompasses research articles, original research report, : 1) Power Systems, 2) Signal, System, and Electronics, 3) Communication Systems, 4) Information Technology, etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 223 Documents
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Planning of Solar Power Plant SMA LabSchool UPGRIS with PV*SOL Kusmantoro, Adhi -
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.8543

Abstract

The increase in the use of electrical energy is increasing in the development of technology at this time. At present in Indonesia, power plants still use non-renewable energy sources that will eventually run out. The purpose of this study is to provide a source of electricity with solar energy sources, so that dependence on PLN electricity can be reduced. The method used is planning and simulating using PV*SOL software. The planned location for the installation of Solar Power Plant (SPP) is in the Gayamsari District, Semarang City, Central Java. The location of the SMA Building has an area of 1773 m2 with coordinates of Latitude -6.9830564° N, 110.4494686 ° E. In the planning, the stages are determining the location of the PLTS, identifying solar radiation intensity data, identifying electrical load data, determining solar panel capacity, determining battery capacity, determining inverter capacity, and determining the capacity of the Solar Charge Controller (SCC). The planned SPP operates in an off-grid system. In carrying out this planning with stages. The results of the study showed that the amount of daily electricity consumption was 18,402 Wh and the electricity consumption for one month was 552,060 Wh. The simulation showed that solar panels effectively produced an average of 1300 kWh of electricity. The production of large solar panels occurred from April to October, with an average energy of 130 kWh. The results of the study showed that the amount of electricity consumption was large but could be served by solar power plants.
Classification Of Mustard Leaf Diseases Using Convolutional Neural Network Architecture Hafidurrohman, M.; Kusrini, K
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10779

Abstract

Diseases in mustard leaves can reduce productivity if not detected early. This study aims to develop and evaluate a disease classification system for mustard leaves using Convolutional Neural Network (CNN) architectures, specifically Xception and VGG19, while comparing their performance in terms of accuracy and computational efficiency. The mustard leaf image dataset undergoes preprocessing before being used for model training and testing. Experimental results show that Xception achieves the highest validation accuracy of 99% with better loss stability compared to VGG19, which attains 94.50% accuracy but exhibits greater fluctuation. In terms of time efficiency, VGG19 reaches optimal accuracy faster and completes the training process in 42 seconds, whereas Xception requires more epochs and a training time of 50 seconds. Therefore, Xception is recommended for classification tasks that demand high accuracy and stability, while VGG19 is more suitable for rapid detection with a slight trade-off in accuracy stability.
Expert System for Skin Disease Diagnosis Using the Best First Search Method and Fuzzy Tsukamoto Fahry, Fahry; Adam, M. Awaludin; Hidjah, Khasnur; Azwar, Muhammad; Hairani, Hairani
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10981

Abstract

The skin is the largest organ and is vulnerable to various diseases, which can spread through direct contact or the environment. Skin diseases are among the ten most common conditions in outpatient care in Indonesia, often caused by poor hygiene and environmental exposure. The limited number of dermatologists makes diagnosing and treating skin diseases more challenging. This study develops an expert system for diagnosing skin diseases using the Best First Search method and Fuzzy Tsukamoto, serving as an alternative or complement to medical diagnosis. Best First Search prioritizes diagnoses based on predefined rules, while Fuzzy Tsukamoto adds flexibility in assessing disease severity. Testing shows that the system achieves an accuracy of 83.3%, demonstrating its potential to assist patients and medical professionals in improving diagnostic efficiency and healthcare quality for skin diseases.
Comparative Analysis Of Accelerometer Sensor And Piezoelectric Sensor Capabilities In The Early Warning System Earthquake Disaster Manuel, Yoses; Widodo, Kartiko Ardi; Palevi, Bima Romadhon
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10847

Abstract

An earthquake is a vibration or shock caused by a sudden release of energy from within the earth, which produces seismic waves. The Indonesian region is currently vulnerable to volcanic and tectonic earthquakes due to the Pacific Ring of Fire, where the Indo-Australian tectonic plate collides with the Pacific tectonic plate. So this study aims to conduct a comparative analysis between earthquake disaster early warning systems that use sensors Accelerometers and Piezoelectric sensors. The two types of sensors used are an Accelerometer IMU 9 DOF to measure the acceleration of ground motion and a Piezoelectric sensor to measure vibration. Using the ESP32 which processes sensor data and sends data to Google Sheets using the Wi-FI module in real-time. Sampling data from the readings of each sensor will be used to determine the most accurate use of sensors for detecting earthquakes. In the results of the comparison of the two sensors, the average percentage of errors where the sensor Accelerometer with an error of 0.104% and a piezoelectric sensor with an error of 100% so that it can be concluded that the Accelerometer sensor is more accurate than the piezoelectric.
Evaluation of Reliability and Energy Not Supplied in the 20 kV Distribution System at the Tanjung Api-Api Substation Malini, Regina Septient; Barlian, Taufik; Lestari, Asri Indah
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10780

Abstract

The reliability of the electric power distribution system is essential for life, economy, health and safety. This reliability can be assessed from several indicators, including SAIDI, SAIFI, and ENS which can describe the economic impact of blackouts. The results of the calculation in this study show that the SAIDI and SAIFI values for four Feeders at the Tanjung Api-api Substation are in a reliable state when viewed from the PLN Target that has been set, and are not reliable for Ferry Feeders, namely SAIDI at Ferry Feeders of 6.003570985 hours/year, Cargo Feeders of 0.272152778 hours/year, Pinisi Feeders of 0.771065263 hours/year and Roro Feeders of 1.477902547 hours/year. As for SAIFI on Ferry Feeders amounting to 3.230883689 times/customer/year, Cargo Feeders 0.16666667 times/customer/year, Pinisi Feeders 0.836271676 times/customer/year and Roro Feeders 1.67529189 times/customer/year. For the ENS value index on the four Feeders based on the calculation results, 396,201.8423 Kwh was obtained with a loss of Rp. 536,118,758.6 kWh, in the Ferry Feeder that was not distributed energy of 319,128.4392 kWh with a loss of Rp. 431,461,649.8, in the Cargo Feeder of 4,896.091948 kwh with a loss of Rp. 7,073,384,037, in the Feeder of 7,136.846081 kWh with a loss of Rp. 9,649,015,901 and in the Roro Feeder of 65,040.46513 kWh with a loss of Rp. 87,934,708.86.  The results show that the higher the unchanneled energy, the greater the losses experienced by PT. PLN (Persero).
Corn Leaf Disease Classification Optimization Using Resnet50 Architecture Utilizing Bayesian Optimization Abdillah, Yahya Auliya; Kusrini, Kusrini
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.9809

Abstract

This research aims to optimize the classification of diseases on corn leaves using Convolutional Neural Network (CNN) architecture, ResNet50, combined with hyperparameter optimization techniques using Bayesian Optimization. The dataset used comes from Kaggle, consisting of four classes of corn leaf diseases, namely corn leaf spot, leaf rust, corn leaf blight, and healthy corn leaves. Data pre-processing was done to balance the amount of data between classes and reduce the risk of overfitting. This study tested various scenarios, including the use of the original dataset and a pre-processed dataset. The experimental results show that the use of Bayesian Optimization in hyperparameter search gives better results than manual parameter setting. The scenario with hyperparameter optimization using Bayesian Optimization technique on the pre-processed dataset shows an increase in accuracy by 5% (87.79%) compared to the scenario without optimization (82.82%). This research concludes that hyperparameter optimization techniques and proper data pre-processing can improve the performance of CNN models in corn plant disease classification, providing the potential to assist farmers in detecting diseases earlier and reducing the economic losses incurred.
Design and Construction of IoT-Based Overvoltage and Undervoltage Detection Devices Mahendra, Luki Septya; Putra, Putu Agus Mahadi; Kurniawan, Agus Dinantya Dwi; Suharyanto, Hendik Eko Hadi; Raharja, Lucky Pradigta Setiya; Arif, Yahya Chusna
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10893

Abstract

In this research, designed an overvoltage and undervoltage fault detector based on the Internet of Things (IoT), which aims to monitor the quality of electrical voltage in detecting overvoltage / undervoltage faults that change instantly with real-time monitoring. This system detects voltage disturbances using the PZEM-004t sensor by considering PLN standards. The overvoltage is above +5% normal voltage and for undervoltage below -10% normal voltage. Voltage disturbances are injected through the transformer configuration to generate overvoltage / undervoltage instantly. Furthermore, the voltage that has been read by the sensor is sent to the Blynk platform as a monitoring system. The transformer configuration is simulated first, with an overvoltage result is 251.8 V and an undervoltage is 110.6 V. In the results of the hardware transformer configuration test, the overvoltage injection is 253 V and the undervoltage is 117.3 V. The data displayed by the monitoring system is compared with the Fluke 43B measuring instrument to be calibrated, with an average error of 1.2%. Monitoring data can be accessed via gadgets. So that preventive actions and analysis can be carried out to reduce the risk of losses due to voltage disturbances.
Design and Development of an E-commerce Application Using the Web Information System Development Methodology Hadi, Wahyu Nofiyan; Latifah, Ummi; Susilowati, Umi Diantika; Wilda, Anisa Nurul; Fadel, Moh.
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10745

Abstract

The main objective of this research is to design and build an e-commerce application to enhance the competitiveness of micro, small, and medium enterprises in Sumbertaman, Probolinggo City. In the development process, the Web Information System Development Methodology (WISDM) method was used, which consists of the stages of needs analysis, design, implementation, testing, and evaluation. This research began with data collection through interviews and direct observations of local micro, small, and medium enterprises to understand the specific needs related to the e-commerce system. The research results show that the developed application is capable of meeting the needs of micro, small, and medium enterprises in terms of product management, sales transactions, and real-time data reporting. The implementation of this application is expected to provide convenience for micro, small, and medium enterprises to market their products online, improve operational efficiency, and expand market reach. Thus, this WISDM-based e-commerce application significantly contributes to the digitalization of local businesses in the era of digital transformation.
Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Maulyanda, Maulyanda; Deviani, Rini; Afdhaluzzikri, Afdhaluzzikri
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10875

Abstract

Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions.
Cloud Server Security System Design on Linux Almalinux 8.6 Operating System Based on WHM with DNS Management Using IP Masking Cloudflare Chaedar Fatach, Muhamad Reza; Muhammad, Alva Hendi; Ariatmanto, Dhani
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10766

Abstract

This study aims to design a Cloud Server Security System on the Linux Almalinux Operating System with DNS Management Using a Proxy Server Based on IP Masking Cloudflare using the Traffic Patterns and Packet Internet Gopher (PING) methods. The type of research used is qualitative research where researchers collect primary and secondary data. The results of the study concluded that this design can improve security in Cloud Servers. Before using IP Masking Cloudflare, server performance was low, Data Access and Management was Low, Scalability was Low, Access Security was Low, System Integration was Low, but after using IP Masking Cloudflare Server Performance, Data Access and Management, Scalability, Access Security, and System Integration increased.

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