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Journal of Electrical Technology UMY
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 25501186     EISSN : 25806823     DOI : 10.18196/jet
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Library & Information Science
The Journal of Electrical Technology UMY (JET-UMY) is a peer-reviewed journal that publishes original theoretical and applied papers on all aspects of Electrical, Electronics, and Computer Engineering.
Arjuna Subject : -
Articles 115 Documents
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Coin-Operated Public Electric Vehicle Charging Station with Solar Panels as the Energy Source
Journal of Electrical Technology UMY Vol 7, No 1 (2023): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v7i1.20658

Abstract

Electric transport is becoming increasingly popular as another option to use environmentally friendly energy in the form of transport. To support the development of electric transport, efficient and accessible charging infrastructure is required. Public Electric Vehicle Charging Stations (SPKLU) have become a major part of supporting long-distance travel and reducing the impact of greenhouse gas emissions on the environment. To increase the availability of SPKLUs and expand accessibility for electric vehicle users, this research aims to design an innovative coin-based SPKLU system that also utilizes solar energy through solar panels. The station can have a smaller environmental effect and lower operating costs by employing solar energy as its primary source. The examination included a review of the economy as well. As a result, those in charge, business owners, and communities who want to promote the usage of electric vehicles find it to be a desirable alternative.  As a result of this research, it was found that a full charge of a 20Ah battery only requires a coin of Rp 7000
The Design of Security Framework for LoRaWAN FUOTA
Journal of Electrical Technology UMY Vol 7, No 2 (2023): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v7i2.22360

Abstract

This research outlines a comprehensive security framework for LoRaWAN Firmware Updates Over-The-Air (FUOTA), which is essential for ensuring the reliability of IoT devices in critical infrastructures. It addresses multiple security threats specific to the wireless transmission of firmware updates, initiating an assessment of the vulnerabilities faced by the LoRaWAN FUOTA process. The framework incorporates several security measures, including secure transmission using lightweight encryption to maintain data confidentiality, robust authentication and authorization strategies to prevent unauthorized access, and digital signatures for integrity verification to ensure only authentic firmware updates are installed. It also includes anti-replay measures like sequence numbers and timestamps to protect against replay attacks and emphasizes efficient resource management to optimize power and computational resources for IoT devices. Additionally, secure multicast management techniques are employed to handle the challenges of simultaneously distributing updates to multiple devices. The framework provides an integrated and detailed approach to enhancing the security and operational efficiency of LoRaWAN FUOTA, making it an invaluable resource for practitioners and researchers in the field.
Design and Implementation of a Candy Color Sorter Device using Microcontroller-Based Color Sensor TCS3200 Vicananda, Ladayan Pradana; Prahasti, Saptiana Nur; Ma’ruf, Khakam; Setiawan, Rizal Justian; Darmono, Darmono
Journal of Electrical Technology UMY Vol 8, No 1 (2024): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i1.20346

Abstract

The purpose of this research is to design and implement a candy color sorter device using a microcontroller-based TCS3200 color sensor. This technology can be applied in the confectionery and fruit businesses for color-based sorting. The method used is an experimental approach that begins with a literature study, followed by hardware and software design, and device testing. The research results show that the candy sorter device was successfully designed and implemented. This device is capable of sorting candies based on red, green, and blue colors with optimal sensor detection levels. The novelty value of this research is the development of an automatic candy sorter device based on the TCS3200 color sensor, which can help reduce human workload and increase sorting process efficiency in the confectionery industry.
Drone Radiation Detection System Information Mapping Design Using Quantum Geographic Information System Dewi, Devina Chandra; Suryaningsih, Fitri
Journal of Electrical Technology UMY Vol 8, No 1 (2024): June
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i1.16716

Abstract

Scan systems using unmanned aircraft or drone are designed to be able predicted when there is radiation exposure from radioactive sources. The drone is equipped with a Geiger Muller detector type J305 tube and a GPS (Global Positioning System) Tracker and other communication tools. Testing of this system was carried out in the field of B.J. Habibie Serpong, National Research and Innovation Agency using a radioactive source of Cs-137. The drone's flight direction has been designed at the Red Waypoint and has a height of 2 meters. Radiation detection mapping was carried out using Quantum GIS (Geographic Information System) software. The classification parameter in Quantum GIS is divided into 3 parts, namely the first class value is 0.00 to 0.10, the second class is 0.11 to 0.20, and the third class is 0.21 to 0.40. In this study, there are 5 coordinates of the highest radiation value with a red round symbol. These points indicate radiation levels of 0.24, 0.24, 0.24, 0.36, and 0.28. Using identification feature, the mapping results can help the user in analyzing and enable quickly find areas with high radiation. Thus, the decontamination or transfer of radioactive sources can be used as a quick and appropriate follow-up.
Early Detection of Diabetes Mellitus in Women via Machine Learning Arrayyan, Ahmad Zaki; Adinandra, Sisdarmanto
Journal of Electrical Technology UMY Vol. 8 No. 2 (2024): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i2.24287

Abstract

Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes.  The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the best-performing model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0.74), resulting in an F1-score of 0.82.

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