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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 30 Documents
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Search results for , issue "Vol 7, No 1 (2025)" : 30 Documents clear
Decision Support System for Electric Vehicles Selection Using Simple Additive Weighting Suwanto, Thomas Christian; Koloay, Steven; Adrian, Angelia Melani
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.v7i1.10909

Abstract

Electric vehicles (EVs) are vehicles entirely powered by electric motors using energy stored in batteries. In Indonesia, interest in electric vehicles is increasing, supported by government initiatives to reduce carbon emissions and improve infrastructure. The main issues faced are potential buyers' hesitation in choosing electric vehicles due to the limited variety of models, high prices, and insufficient information provided to buyers.This research aims to build a decision support system for selecting electric vehicles using the Simple Additive Weighting (SAW) method. The selection of electric vehicles using the SAW method requires criteria derived from sales brochures, official dealer websites, automotive exhibitions, and trusted news sources. The criteria used include price, range, battery capacity, passenger capacity, and vehicle speed. In the application development process, the waterfall method was used. The modeling tools used in this research are Flowcharts, Data Flow Diagrams, and Entity Relationship Diagrams, while the application development uses HTML and JavaScript.Based on the research conducted, all features function well, and out of the five alternatives used in this study, the results show that the Hyundai Ioniq 6 has a score of 0.9, while the Wuling Air EV Long Range has a score of 0.59.
Temperature and Humidity Monitoring System Using Node-Red Based on MQTT Protocol Utomo, Eko Budi; Izzaturrahmani, Nurhaliza
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.v7i1.10658

Abstract

Temperature and humidity monitoring system integrated with the Internet of Things (IoT) is an innovative step in monitoring environmental conditions in real-time. This research develops a monitoring system based on the MQTT (Message Queuing Telemetry Transport) protocol integrated with the Node-RED platform. A temperature sensor is used to read temperature data, which is then sent via the MQTT protocol to the broker server. Node-RED acts as a visual interface to process, analyze, and display the temperature data in the form of an interactive dashboard. The system is designed to support easy integration, lightweight data management, and remote monitoring. Experimental data was obtained by comparing sensor readings with a variety of different environmental conditions. The test results show that this monitoring system is able to provide information related to temperature data with an accuracy above 98.4%. With this system, users can monitor temperature and humidity based on accumulated data or in real-time so that it is more accurate because it has a small average error of 0.4°C. Users can plan actions to be taken related to controlling environmental conditions.
Classification of LPG Gas Usage Satisfaction Level Using The Naïve Bayes Algorithm Adrian, Angelia Melani; Patras, Bella Alisia; Sanger, Junaidy B.
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.v7i1.11064

Abstract

LPG gas is a very important energy source in everyday life for cooking activities. Although the importance of LPG gas in supporting everyday life has been widely recognized, satisfaction with the use of LPG gas is an issue that should not be ignored. Often products or services that do not meet customer expectations can cause dissatisfaction. This can be caused by low quality, prices that do not match the quality received, or not in accordance with user expectations.This study aims to classify the level of satisfaction of LPG gas usage using the Naïve Bayes algorithm. The data obtained from the survey results are 250 data using 5 attributes, namely meeting needs, good quality, affordable prices, repurchasing, and recommending products. And using 2 classes, namely satisfied and dissatisfied.The model achieved an accuracy of 89.3% with a 70:30 training-to-test data split, 91.2% with an 80:20 split, and 94.0% with a 60:40 split, indicating that performance varied based on the proportion of training and test data used.
Lighting System Control for Cataract Maturity Imaging Device Setiawan, Katon Bagus; Somawirata, I Komang; Faradisa, Irmalia Suryani
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.v7i1.10849

Abstract

As technology advances, various innovations have been made to assist in diagnosing and treating cataracts. In cataract detection, artificial intelligence-based methods such as Convolutional Neural Networks (CNN) have proven very effective in detecting cataracts. CNN can classify eyes with an accuracy of up to 87%. In addition to image processing techniques such as CNN, the quality of the resulting images highly depends on the lighting system used during the eye image capture. This research expects the lighting system to be designed to adjust light intensity flexibly. This feature allows for adjusting brightness as needed, ensuring high-quality image results without compromising eye health. From this research, the image quality test results show good quality in the duty cycle range of 23.53% to 62.75% with light intensity of 30-84 lux. This indicates that the light intensity at the medium level produces images with good indicators. However, the light intensity conditions at the medium level begin to vary in terms of visual comfort and are still tolerable by most users. In the final test, an experiment involving respondents and image analysis using image processing was conducted. From the experiment, the respondents felt comfortable with the light intensity emitted by the LED. In the image processing section, the average number of images taken to obtain a good indicator was 3 times. A structured lighting system can ensure that good image results are obtained and patients feel comfortable with the light intensity used.
Implementation of YOLOv7 Model for Human Detection in Difficult Conditions B, Arijal; Sunyoto, Andi; Hanafi, M.
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.v7i1.10662

Abstract

The rapid development of artificial intelligence technology in recent decades has led to the development of highly efficient object detection algorithms, including human detection under difficult conditions. Human detection is one of the major challenges in computer vision as it involves various complex factors such as obstructed human objects, pose variations, small low-resolution human objects, as well as the presence of fake human objects such as statues or images. This research uses the SLR (Systematic Literature Review) method to determine the algorithm used, namely YOLOv7. The three YOLOv7 models tested in this study are YOLOv7x.pt, YOLOv7-w6-person.pt, and YOLOv7-w6-pose.pt. These models were selected based on their excellence in detecting human objects and their relevance for complex scenarios. Tests were conducted using 100 images obtained from the internet and divided into four categories of human objects under difficult conditions, which represent various challenges in human detection. Analysis was performed using convusion matrix to evaluate performance metrics such as accuracy, precision, recall, and F1-score. Based on the test results, the YOLOv7-w6-person.pt model showed the best overall performance, especially in detecting humans in obstructed conditions and complex lighting with a precision of 90.4%, Recall 88.7%, and F1-Score 89.5%. This model has higher accuracy, precision, and F1-score than the other models, making it a reliable choice for human detection in difficult scenarios. These findings not only demonstrate the relevance of YOLOv7 as a reliable human detection algorithm, but also provide a basis for further optimization of YOLOv7-based human detection systems, both through improving the model architecture and adapting to more specific datasets. This research makes an important contribution to the development of human detection technologies for real-world applications, such as surveillance, crowd analysis, and automated safety systems.
Development and Deployment of an IoT-Based Telemedicine System for Infant Warming Devices Fauzi, Ekha Rifki; Doungjan, Kamonthip; Respati, Hanif Budi
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.v7i1.10763

Abstract

Neonatal hypothermia is a serious health concern, especially for premature babies and those with low birth weight, often resulting in complications like metabolic acidosis, hypoxia, and an increased risk of other health issues. To address this, newborn warming technologies have been developed, offering a controlled environment to regulate the baby’s body temperature. This study focuses on creating and implementing an IoT-powered telemedicine system that works with an infant warming device to enhance neonatal care. The system includes sensors to monitor temperature and heart rate in real time, ensuring the baby's safety and promoting the best possible clinical outcomes. With IoT architecture, healthcare professionals can remotely manage and monitor the baby's condition, making quick decisions when necessary. This approach overcomes the limitations of current devices by incorporating fuzzy logic control alongside real-time telemedicine features, all accessible via portable devices. By integrating these technologies, the system offers a solution to managing neonatal hypothermia, especially in settings with limited resources. Additionally, it has the potential to cut down on hospital transfers, improve outcomes for newborns, and ease the burden on caregivers and families. The research suggests that this IoT-enabled infant warming system could significantly boost the effectiveness of neonatal care, making it an invaluable tool for healthcare professionals.
Research Study of ROS2 Library and Interface Development for Industrial Robotic Arm Mitsubishi RV2-SDB Utomo, Eko Budi; Aziz, Mochammad Abdul
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.v7i1.10663

Abstract

This research study is purposed for development the ROS2 library wich provides the ROS2 interface for industrial robotic arm Mitsubishi RV-2SDB. This library for facilitate the important data acces, such as data joint, position, jog mode, error message, and alarm. By utilizing the ROS2, this library designed for integration suport and robot development in simulation of industrial process, as well as wider automation. The use of ROS2 is deployment modularity and scalability, enable to easier integration with existing automation system. Research studies show that this library is able to improve the efficiency and reliability of robot operations, supporting the industrial revolution 4.0 by creating flexible and integrative solutions.
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.

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