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JTEIN: Jurnal Teknik Elektro Indonesia
Published by Universitas Negeri Padang
ISSN : -     EISSN : 27230589     DOI : https://doi.org/10.24036/jtein.v1i1.8
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering
JTEIN: Jurnal Teknik Elektro Indonesia dikelola oleh Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Padang, dalam membantu para akademisi, peneliti dan praktisi untuk menyebarkan hasil penelitiannya. Fokus pada bidang yang terkait dengan Teknik Elektro.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 340 Documents
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Implementation of Voltage Control on a Multilevel DC-DC Boost Converter Febi Ariefka Septian Putra; Sofyan Muhammad Ilman; Muhamad Amirul Muminin; Giga Verian Pratama; Dini Hariani Fitri Lubis; Annisa Izaty
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.793

Abstract

A multilevel boost DC-DC converter is a solution for increasing DC voltage levels without requiring additional transformers, making it ideal for efficient power conversion applications and high-voltage systems. However, output voltage stability is often compromised due to load fluctuations and input voltage variations. To address this, this study aims to design and implement a control system based on the Proportional-Integral (PI) method to maintain the output voltage at a constant value of 200V from an initial input of 30V. The system was developed using an Arduino Nano microcontroller that acts as the main controller, regulating the PWM signal based on feedback from the voltage sensor. The converter circuit consists of a multilevel topology with one MOSFET, one inductor, and a cascaded array of capacitors and diodes. Tests were conducted under open-loop and closed-loop conditions, as well as with various resistive loads. The test results show that the PI method is able to provide good voltage stability and respond optimally to load changes, with minimal overshoot and fast steady-state time
Development of an MPS Distribution Station Jobsheet Based on Outseal PLC for Electropneumatic Control Learning Nurafni Fajarna Do Ahmad; Oriza Candra
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.828

Abstract

This study aimed to develop a jobsheet based on the Modular Production System (MPS) Distribution Station using an Outseal PLC for electropneumatic control learning in vocational schools. The study was motivated by the suboptimal use of MPS facilities and the absence of structured practical learning guidelines, which led to students’ difficulties in understanding the relationship between theoretical concepts and industrial applications. This research employed a Research and Development (R&D) method using the ADDIE model, limited to the development stage. The developed jobsheet was validated by three experts consisting of material and media validators. Data analysis was conducted using Aiken’s V to determine the validity level of the product. The results showed that the material aspect obtained a validity value of 0.78, while the media aspect obtained 0.82, both categorized as valid. Therefore, the developed jobsheet is considered feasible to be used as a learning medium for electropneumatic control systems in vocational education
Iris recognition and classification methods Husni Dhiyatri Ulhaq; Riki Mukhaiyar
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.829

Abstract

Conventional authentication methods such as passwords and Personal Identification Numbers (PINs) have proven to be vulnerable to misuse and data breaches, highlighting the urgent need for more reliable identity verification systems. Biometric-based authentication, particularly iris recognition, has emerged as a promising solution due to the unique and physiologically stable nature of iris patterns throughout a person's lifetime. However, previous studies in iris recognition have reported limited accuracy and lacked comprehensive evaluation using metrics such as Precision, Recall, and F1-Score. This study designed and implemented an iris recognition system using Gabor Wavelet as the feature extraction method combined with the K-Nearest Neighbor (K-NN) classification algorithm. The dataset was obtained from the CASIA Iris Database, consisting of 100 images covering 20 identity classes, of which 20 images were used as test data. The system pipeline comprised iris segmentation, normalization, feature extraction using Gabor Wavelet, and classification using K-NN with K=1 and Euclidean distance. System performance was evaluated using accuracy, Precision, Recall, and F1-Score metrics across multiple threshold values. The experimental results showed that the proposed system achieved an accuracy of 95%. At a threshold of 0.5, the system produced the best overall performance with a Precision of 0.90, Recall of 0.95, and F1-Score of 0.92. These findings confirmed that the combination of Gabor Wavelet and K-NN was effective for biometric-based iris recognition systems.
Digital Transformation of Laundry Businesses Through Digital Marketing: A Case Study of Bagas Laundry Business Group Oriza Candra; Helmawati Helmawati; Nurzi Sebrina; Syaiful Islami
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.833

Abstract

This article examines digital transformation strategies in the laundry business sector, focusing on the “Bagas Laundry” business group and the implementation of digital marketing-based strategies. The study highlights significant improvements in business growth and operational performance after adopting digital transformation initiatives. Before implementing digital marketing, the market share of “Bagas Laundry” was only 10%, but it increased substantially to 45% by the end of 2022. The study emphasizes the importance of digital marketing strategies, including website and mobile application development, social media marketing, email campaigns, and Search Engine Optimization (SEO), in addressing the limitations of conventional marketing methods. The findings reveal that website traffic increased by 150% within the first six months of implementation, while customer conversion rates improved by 80%. In addition, the adoption of automation systems and digital payment integration enhanced operational efficiency by 30% and reduced order processing time by 40%. Employee training programs related to digital technology adaptation and online marketing skills also produced positive results, leading to a 25% increase in employee productivity. Customer experience improvements became another major focus, resulting in a 90% increase in customer satisfaction and customer retention rates remaining above 80%. Furthermore, the evaluation of Return on Investment (ROI) showed that the business achieved an ROI of 200% in 2022, accompanied by an annual net revenue growth of 120%. Overall, the case study demonstrates that digital transformation significantly improved revenue, market expansion, and customer satisfaction in the laundry business sector.
Wireless Monitoring System for Off-Grid PV Systems Based on Raspberry Pi Muhammad Dafa Alfaruqj; Ali Basrah Pulungan
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.780

Abstract

The use of renewable energy such as Solar Power Plants (PLTS) is growing in various regions, especially in remote areas that are not reached by conventional power grids. However, off-grid solar systems require periodic monitoring to keep them functioning optimally. This study aims to design a wireless monitoring system for off-grid solar power plants by utilizing Raspberry Pi as a data processing center, monitoring important parameters such as current, voltage, power, temperature, humidity and light intensity at regular intervals. The method used involves ESP8266 as a sensor data transmitter node through the MQTT communication protocol. Node-RED acts as a data bridge between MQTT and Raspberry Pi, which then stores the data into a MySQL Database and is visualized through the Grafana dashboard. The test results show that the system is able to display sensor data periodically, accurately, and informatively, as well as facilitate the process of analyzing the performance of the Off-grid PV system. Thus, this system can be an effective and efficient monitoring solution for the implementation of solar power plants in remote locations.
Analysis of Deep Learning Approach to Improve Learning Effectiveness in Electric Motor Installation for Vocational Students Niswa Afifah; Oriza Candra
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.831

Abstract

This study aims to analyze the implementation of the deep learning approach in improving the effectiveness of learning in the Electrical Motor Installation subject for grade XI TITL students. This research employed a qualitative approach with a descriptive method. Data were collected through observation, interviews, and documentation. The research subjects consisted of teachers and students involved in the learning process. Data analysis was conducted through data reduction, data display, and conclusion drawing. The results showed that the implementation of the deep learning approach, which includes three main aspects mindful learning, meaningful learning, and joyful learning was able to improve the quality of the learning process. In the mindful learning stage, students demonstrated increased focus and learning readiness. In the meaningful learning stage, students found it easier to understand concepts through hands-on practice and were able to relate theory to its application. Meanwhile, in the joyful learning stage, there was an increase in student participation, collaboration, and learning motivation. Overall, students were not only able to complete practical tasks but also understood the working principles of electrical circuits and were capable of analyzing emerging problems. In conclusion, the deep learning approach is effective in improving the quality of learning in Electrical Motor Installation, both in terms of the learning process and student engagement. This approach also contributes to the development of critical thinking skills and problem-solving abilities relevant to vocational education.
Temperature Control Using Labview-Based Fuzzy Logic on a Calorimeter for Physics Experiments Yulian Zetta Maulana; Ivan Muhtarom
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.838

Abstract

In this research, an analysis of temperature regulation was conducted on the calorimeter used for Physics practicum using the Labview controller. The purpose of this study is to obtain a stable calorimeter temperature output so that it makes it easier for the practicum to be carried out. The object to be measured is the output temperature of the calorimeter which is heated and detected by the LM-35 temperature sensor. NI-DAQ USB 6008 is used as the data acquisition device, and is connected to Labview software. The data collection process was carried out by comparing 5 tests, including testing without a Fuzzy controller, testing a Fuzzy controller with 74 percent, 80 percent, 89 percent, 90 percent from PWM maximum defuzzification. The final results show that temperature regulation on the calorimeter with Fuzzy logic is quite effective with 90 percent defuzzification, because the temperature can reach the set point value with a settling time of 40 minutes and has a steady state error of 1.1 percent.
IoT-Based Electrical Protection and Monitoring System for Real-Time Detection of Current and Voltage Faults in Residential Electrical Installations Fenti Amelia Sari; Naufal Akram; Arinda Frismelly; Syaiful Islami
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.832

Abstract

Electrical disturbances such as overcurrent and overvoltage in residential electrical installations can lead to equipment damage, reduced operational efficiency, and increased safety risks if not addressed promptly. Conventional protection mechanisms are generally limited to localized interruption and lack real-time monitoring and remote communication capabilities. This study presents the design and implementation of an IoT-based electrical protection and monitoring system for real-time detection of current and voltage faults in residential electrical installations. The proposed system integrates a NodeMCU ESP8266 microcontroller, a PZEM-004T sensor module for measuring electrical parameters, a relay module for automatic load disconnection, and a mobile-based notification platform for remote monitoring. The system continuously measures voltage, current, power, and energy consumption while executing protective actions when abnormal thresholds are exceeded. An experimental approach was employed, including hardware design, prototype development, and performance evaluation under various load conditions. The results indicate that the system is capable of detecting electrical anomalies in real time with an average response time of 1 second and measurement accuracy reaching 98%. The relay mechanism successfully disconnected the load during fault conditions, while remote notifications enhanced user awareness and system accessibility. The proposed system demonstrates practical applicability as an intelligent protection framework for improving electrical safety and monitoring efficiency in residential environments.
Desain Single Passive Tuned Filter untuk Meredam Harmonisa pada Transformator 1000 kVA Prima Abdi Saputra; Zulfatri Aini Efa; Nanda Putri Miefthawati
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.837

Abstract

The increasing use of non-linear loads in electrical distribution systems has caused harmonic distortion problems that can degrade power quality. Harmonics may increase transformer losses, reduce transformer capacity, decrease system efficiency, and increase operational costs due to power losses. This study aims to design a single passive tuned filter to mitigate harmonics in a 1000 kVA transformer at UIN Suska Riau. Data collection was conducted through direct measurements using a Power Quality Analyzer, while system validation was performed using ETAP simulation software. The measurement results showed that the Total Harmonic Distortion of Current (THDi) in several phases exceeded the IEEE 519-2014 standard limit, with the 3rd-order harmonic identified as the dominant harmonic component. Therefore, a single passive tuned filter was designed by determining the appropriate resistor, inductor, and capacitor parameters based on the dominant harmonic order. The results demonstrated that the proposed filter successfully reduced THDi values below the IEEE 519-2014 standard limit. In addition, the filter reduced transformer derating, minimized transformer losses, and decreased energy loss costs caused by harmonics. The ETAP simulation results were consistent with the analytical calculations, confirming that the designed filter effectively improved power quality and transformer performance in the electrical distribution system.
Face Recognition Systems: Comparation Point of View between CNN and LBPH Methods Aulia Kurniawati; Riki Mukhaiyar
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.839

Abstract

Face recognition systems have become increasingly prevalent in various applications, including security, biometric authentication, and digital identity verification. This article presents a comparative study on the implementation and performance of two face recognition methods: Convolutional Neural Network (CNN) and Local Binary Pattern Histogram (LBPH). The research utilized the Labeled Faces in the Wild (LFW) dataset, which comprises 19 classes of faces, with 760 images for training and 475 images for testing. The system was developed using the Python programming language, incorporating TensorFlow/Keras, OpenCV, and Visual Studio Code, along with a Graphical User Interface (GUI). The primary focus of this study was to implement both face recognition methods and analyze the selectivity of the system in distinguishing between known and unknown faces. Experimental results demonstrated that the CNN method offered superior classification stability and consistent face recognition, whereas the LBPH method provided faster training times and reduced computational complexity. Additionally, the results indicated that threshold settings significantly influenced the system’s ability to classify recognized and unknown faces. In conclusion, the study found that CNN is more suitable for applications requiring robust classification capabilities, while LBPH is better suited for lightweight face recognition systems that prioritize processing speed and efficiency.

Page 34 of 34 | Total Record : 340

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