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Andalas Journal of Electrical and Electronic Engineering Technology
Published by Universitas Andalas
ISSN : -     EISSN : 27770079     DOI : -
Core Subject : Science, Education, Engineering,
Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering
Electrical power and energy: Transmission and distribution, high voltage, electrical energy conversion, power electronics and drive. Telecomunication and Signal Processing: Antenna and wave propagation, network and systems, Modulation and signal processing, Radar and sonar, Radar imaging; Radio, multimedia content, Routing protocols, Wireless communications, Signal Processing, Image Processing, Voice Processing. Control automation and Robotic: Robotics, Automation, Pattern Recognition, Biosignal Engineering, Control Theory, Applied Control, System Design, Optimization, Process Control, Sensor. Research in Electrical and Electronic Engineering Education.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 87 Documents
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Performance Enhancement of Liquid Filling Process Using Feedforward-Feedback PID Control under DCS Environment Dwi Risdhayanti, Anindya; Ayu Permatasari, Dinda; Rifa'i, Muhamad; Sandra Asti, Irfin
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 5 No. 2 (2025): November 2025
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v5i2.185

Abstract

This study presents the implementation of a Feedforward-Feedback Control method on a Distributed Control System (DCS) Siemens SIMATIC PCS7-based liquid filling process to enhance system stability, response speed, and control accuracy. One of the main challenges in industrial process control is disturbances that cause deviations from the desired setpoint. To address this, a control strategy combining feedforward and feedback actions was developed to anticipate and correct process variations in real time. The system adopts a Multi-Input Multi-Output (MIMO) architecture with two main control variables: liquid level and reactor temperature. Flow rate and liquid level measurements were obtained using the Waterflow YF-S401 and Ultrasonic HC-SR04 sensors, both demonstrating a linear relationship between output voltage and the measured physical quantities, with stable real-time responses displayed on the HMI. The PID controller parameters were tuned using the built-in PID Tuner, yielding Kp = 50, Ki = 150.329, and Kd = 0. Experimental results show that the feedforward-feedback approach reduced the settling time from 78 seconds to 50 seconds and decreased the steady-state error from ±3.8% to ±1.2%. In temperature control, the system successfully reached the operating point of 50 °C with less than 1% steady-state error and a settling time of approximately 60 seconds. The system was configured with AI, AO, DI, DO modules and PROFINET communication, programmed using Sequential Function Chart (SFC) and Continuous Function Chart (CFC). The results demonstrate that the feedforward-feedback control significantly improves process performance and offers strong potential for application in larger-scale industrial automation systems.
Comparing Student Performance in Individual and Collaborative Problem-Based Learning in The Basic Power System Course Adrianti; Muhammad Nasir
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.164

Abstract

Collaborative learning is associated with higher achievement, improved social connections, enhanced communication skills, and increased self-esteem. However, challenges such as a lack of collaboration skills, unequal participation, and free riding can hinder its effectiveness. It emphasizes the need for students to develop individual learning skills—such as analysis, synthesis, and reflection—before engaging in collaborative learning to ensure success. The paper aims to analyze and compare students' learning performance in a Basic Power System course over two consecutive years, in which the first year employed collaborative learning strategies and the second year used individual learning strategies, involving fourth-semester students in an Electrical Engineering Bachelor's program. The comparison of student marks between the 2022/2023 and 2023/2024 academic years indicates that individual assignments resulted in better performance, surpassing group assignments by an average of 23.7%. Exam results corroborate these findings: the 2023/2024 cohort surpassed the target scores, while the previous year's results fell significantly short. Despite this improvement, the exam results for both years were unsatisfactory, suggesting a need for more effective learning strategies, particularly those that boost students' self-efficacy in mastering course materials.
Evaluation of 5G NR NSA Deployment on FR1 (Sub-6 GHz) in Bukittinggi Sri Yusnita; Siska Aulia; Dikky Chandra; Zurnawita; Miro Dhino Laskar Ashanto
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.199

Abstract

This study evaluates the deployment of 5G New Radio (NR) Non-Standalone (NSA) operating on Frequency Range 1 (FR1) Sub-6 GHz in Bukittinggi, Indonesia. Field measurements were conducted using drive test methods to assess key performance indicators, including RSRP, RSRQ, SINR, and throughput. The results show that all measured RSRP values exceed –95 dBm, indicating good signal coverage across the test area. Approximately 71% of SINR samples are above 0 dB and 80% of RSRQ values exceed –17 dB, reflecting relatively stable channel conditions. However, 77% of throughput measurements remain below 10 Mbps, suggesting that achievable data rates remain moderate despite good signal quality. The analysis indicates that SINR has a stronger impact on throughput compared to RSRP and RSRQ. Although 5G NR NSA demonstrates superior signal quality compared to LTE, 4G LTE achieves higher data speeds at the measurement location. These findings highlight the influence of NSA architecture, bandwidth limitations in FR1 Sub-6 GHz, and network optimization factors on overall 5G performance.
Development and Evaluation of an HMI-Based Remote Laboratory Arm Robot for Control System Learning Bayu Irawan Nugroho; Moh Khairudin
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.201

Abstract

A Human–Machine Interface (HMI)-based remote laboratory arm robot was developed to provide an accessible, internet-based solution for control system learning in vocational education. The system integrates an Arduino-based robotic arm, ESP8266, ESP32-CAM, MQTT communication, and an Android HMI application, and was developed using Cennamo’s 5D Spiral model (Define, Design, Demonstrate, Develop, Deliver). Product validation involved expert review, black-box testing, latency and speed measurements, as well as quasi-experimental testing with 315 vocational students from five schools using a nonequivalent pretest–posttest control group design. Technical testing showed that all core functions (start/stop, emergency stop, directional control, speed settings, and video streaming) operated reliably, with average communication latency between 44.2 ms (Wi-Fi) and 85 ms (GSM), indicating stable real-time performance. Expert validation placed functionality, usability, portability, and interface quality in the “highly valid” category (>88%). Learning outcome analysis demonstrated that students in the experimental classes achieved higher N-Gain scores (0.31–0.60; moderate to high) compared with the control classes (0.14–0.18; low), supported by statistically significant differences between pre-test and post-test scores (p < 0.05). Student response data also indicated high levels of satisfaction and perceived usability. These findings confirm that the HMI-based remote laboratory arm robot is technically robust, pedagogically feasible, and effective in enhancing control system competencies in vocational education.
Remaining Useful Lifetime Prediction of Distribution Transformer Using Dynamic Multi-Scale Attention-based CNN-LSTM Elvis Tamakloe; Benjamin Kommey; Jerry John Kponyo; Daniel Opoku; Francis Boafo Effah
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.210

Abstract

Oil-immersed transformers are critical assets in the energy industry linking most power utilities to end-users. Their failure results in prolong outages, leading to huge revenue loss incurred during downtimes and replacement cost. In extreme cases, transformers in an unhealthy state poses a significant threat to the safety of grid operators. Interestingly, traditional reactive and preventive methods have been inefficient in determining when legitimate maintenance actions are due, often leading to either early over-maintenance of healthy transformers or late under-maintenance of serviceable and unhealthy transformers. Predictive maintenance based on determining the remaining useful lifetime (RUL) acts as an actionable step that resolves these challenges by delivering exactly the most appropriate time to undertake maintenance whiles ensuring optimal utilization of resources which saves maintenance cost, reduces downtimes and ensures operator safety and grid reliability. This work proposed an advanced Dynamic Multi-Scale Attention (DMSA) model and leverages on multi-modal data fusion from electrical, mechanical, thermal, and environmental sources to provide an improved data-driven solution for accurate prediction of the RUL of distribution transformers. This technique addressed the drawbacks of employing single modality approaches in capturing complex operational interactions. In this work, dynamic scaling model is incorporated to adaptively adjust the attention weights based on the importance of the input features. For short term predictions, the proposed model experimentally achieved an enhanced performance of 0.2300 mean absolute error and 0.9872 coefficient of determination value. Additionally, the DMSA CNN-LSTM model demonstrated accurate prediction, evidenced by a concordance correlation coefficient value of 0.9936. These statistical gains were achieved in a computational time of 587.3387s, demonstrating superior scalability in the event of real time deployment. Furthermore, the long-term prediction was performed using Prophet to fit the data which predicted a RUL of 25 years at 95% confidence interval which corresponded with the reference standard in IEEE STD C57.91.
Sentiment Analysis of Traffic Congestion in Palembang Using Random Forest Sukemi; Ahmad Fali Oklilas; Muhammad Azrell Samudra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.214

Abstract

Traffic congestion is a persistent problem that significantly affects the daily activities of citizens in Palembang City. The public can voice their thoughts and worries about traffic conditions on social media, especially Facebook. This study uses the Random Forest algorithm to examine public opinion regarding traffic congestion in Palembang. The 2,021 Facebook comments in the dataset were gathered by web scraping and subjected to a number of preprocessing steps, such as cleaning, case folding, stemming, tokenization, normalization, and stopword removal. The TF-IDF algorithm was used for term weighting. The Random Forest model was trained and tested to classify sentiments into three categories: positive, neutral, and negative. The model attained good accuracy across training, testing, and validation datasets, according to the evaluation results. This research provides insights into public perceptions of traffic congestion and can serve as a reference for policymakers in developing data-driven strategies to address traffic issues in Palembang City.
Design and Implementation of a Deep Learning-Based Safety Helmet Compliance Detection System Using the Faster R-CNN Method Palman; Sandy Azizi; Muhammad Ilhamdi Rusydi; Rahmadi Kurnia
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.218

Abstract

Workplace accidents remain one of the major issues in industrial environments and are often caused by low compliance with the use of Personal Protective Equipment (PPE), particularly safety helmets. Manual supervision of PPE usage tends to be inefficient and prone to human error. This study aims to develop an intelligent computer-vision-based system capable of automatically and real-time monitoring helmet compliance. The proposed system employs the Faster Region-Convolutional Neural Network (Faster R-CNN) algorithm to detect and classify workers who are wearing and not wearing helmets. The dataset was obtained from CCTV video recordings in industrial areas, which were converted into image frames for training and testing processes. The experimental results show that the system achieved an accuracy of 90% for helmet-wearing workers and 87% for non-helmet-wearing workers during daytime conditions, and 97% and 91% respectively at night. With an average computation time of 0.1 seconds per frame, the system is capable of real-time detection at up to 10 frames per second. These results indicate that the Faster R-CNN method is effective in detecting PPE compliance and has the potential to be implemented as an automated safety-support system in industrial environments.

Page 9 of 9 | Total Record : 87

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