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Muhammad Yusuf Mappeasse
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Media Elektrik
Published by Universitas Negeri Makassar
ISSN : 19071728     EISSN : 27219100     DOI : 10.59562/metrik
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Publications in the areas of Electrical Engineering, Information and Computer Engineering, and Control include research articles and reviews of the literature.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 198 Documents
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Optimization of Environmental Conditions for Hydroponics Using Light and Temperature Sensors Based on IoT Amri, Ariani; Amir, Baso; Binalopa, Thitin
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.9850

Abstract

Precise environmental regulation is a critical component of hydroponic systems, particularly in urban agriculture, where land availability and resources are limited. This study aimed to examine the impact of an Internet of Things (IoT)-based hydroponic system with automated control of light intensity and temperature on the growth of Lactuca sativa (lettuce). A controlled experimental design was employed, involving 30 lettuce plants divided into control and experimental groups. Both groups were cultivated using a Nutrient Film Technique (NFT) system within a mini greenhouse over three weeks. Environmental parameters were recorded at 30-minute intervals using calibrated sensors, and plant growth was assessed by measuring stem height and leaf number. The results indicated growth increases of 52.44% in stem height and 48.89% in leaf count in the experimental group, suggesting that automated regulation of light and temperature contributes to a more stable growing environment and enhanced plant development. Nevertheless, this study is limited by its relatively short observation period and restricted range of growth indicators evaluated. Further investigations with extended experimental durations and more comprehensive growth parameters are required to assess the long-term effectiveness of IoT-based hydroponic systems.
Deep Learning-Based Electromyography (EMG) Signal Classification for Robotic Hand Control Using Convolutional Neural Networks Mudarris, Mudarris; Rahman, Muhammad Haristo; Rahmah, Aulia; Munzir, Munzir
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10264

Abstract

Electromyography (EMG) is one of the most essential bio signals for developing human–machine interfaces capable of translating muscle activity into motion commands, particularly in prosthetic and assistive robotic systems. However, the nonlinear characteristics of EMG, its susceptibility to noise, and its strong dependence on electrode placement make gesture classification a challenging task. This study aims to classify EMG signals for robotic hand control using a deep learning approach based on Convolutional Neural Networks (CNNs). The dataset consisted of 11,678 samples recorded from eight EMG channels across four hand gestures, preprocessed using a Butterworth filter and normalization prior to training with a lightweight CNN architecture. The model performance was evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved an accuracy of 93%, outperforming Support Vector Machines (SVM), k-nearest neighbors (k-NN), and random forests under identical experimental conditions. The novelty of this study lies in the application of an efficient CNN architecture capable of extracting spatial–temporal features end-to-end from raw EMG signals for real-time robotic control. Despite its promising results, this study is limited to four gesture classes and is sensitive to electrode placement variability. These findings provide a foundational contribution to the development of more responsive, adaptive, and easily deployable prosthetic and robotic control systems.
Development of an Integrated CMS for the MBKM Student Exchange Program Through the Makassar State University Learning Management System (LMS) Platform Wahid, M Syahid Nur; Hartoto, Hartoto; Rais, Muhammad; Fadil, A.; Mahdinul, Muhammad
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10471

Abstract

The Merdeka Belajar Kampus Merdeka (MBKM) program, specifically the Independent Student Exchange (PMM) program at Universitas Negeri Makassar (UNM), faces challenges regarding inbound student synchronization, course equivalencies, and academic monitoring. This study aims to develop a Content Management System (CMS) integrated with Syam-OK and SIM-MBKM. This study uses the Research and Development (R&D) methodology, emphasizing the system testing process conducted through black box Testing and User Acceptance Testing (UAT). The developed CMS successfully integrated the PMM process end-to-end. The test results showed that the user acceptance rate (UAT) reached 91.5% (Very Good category). Furthermore, the implementation of this system resulted in a 45% increase in PMM administration processing time efficiency and reduced the faculty workload related to student enrollment. This CMS can automatically synchronize inbound student data with the Syam-OK LMS and provide comprehensive academic monitoring features. This integrated CMS application significantly improves PMM academic administration efficiency and learning service quality at the UNM. This system is recommended for the development of more effective MBKM information systems at UNM and other higher education institutions.
Development of Sentence Similarity Detection Application with Semantic Similarity and Machine Learning Approaches (Case Study: Student Thesis Title) Suhardi rahman, Edi; Iswal Burhan, Muhammad; T Mangesa, Riana
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10503

Abstract

This study aims to develop an intelligent application for detecting the semantic similarity of undergraduate thesis titles using Natural Language Processing and machine learning techniques. The need for this system arises from the growing number of thesis title submissions in Indonesian universities, which increases the risk of duplication and challenges the effectiveness of manual novelty-verification processes. The development follows a Research and Development (R&D) approach consisting of needs analysis, NLP model development, implementation, and evaluation. A dataset of 114 thesis titles was collected from official academic archives, with 87 titles remaining after data cleaning for the model benchmarking. The Sentence-BERT (IndoSBERT) model is used as the core of the semantic similarity engine, achieving an accuracy of 93% and an F1-score of 0.90, outperforming traditional approaches such as TF-IDF and LSA. System evaluation was conducted based on ISO/IEC 25010, showing strong performance in functional suitability, time behavior (average response time 1.82 s), reliability (100% uptime/24 h), and usability evaluated by 25 respondents using the SUS instrument (score = 80, excellent). The results indicate that the proposed system can significantly assist study programs in identifying potential topic duplications and strengthening academic governance. However, the limited dataset size and single-domain scope (engineering and informatics education) restrict the model’s generalizability. Future development may include larger multi-domain datasets and broader novelty evaluation coverage, such as proposals and abstracts. This study contributes to practical automation support and technological innovation for academic quality assurance.
Reliability Analysis of Silsafe4000 Railway Signaling Equipment at Labakkang Station, Pangkajene and Kepulauan Azis, Asmawaty; Lukman Hakim, Mohamad; Harun Rasyid, Kurniawan
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10511

Abstract

The SILSafe4000 electrical signaling system was implemented at the Labakkang Station on the Makassar-Parepare railway line as part of a newly developed operational corridor. This study aimed to assess the reliability and safety performance of SILSafe4000 using a quantitative analytical approach, combining technical performance standard matching and operational capacity evaluation. Field data will be obtained through direct observation, measurement of key technical parameters, and analysis of operational records from April to September 2025. The results indicate that the system generally operates within acceptable performance thresholds and is capable of supporting up to 52 train movements per day compared with the actual six daily operations. Deviations were identified in the grounding resistances and detection voltages of certain devices, which may affect long-term reliability if not corrected. This study proposes targeted technical improvements, including ground relocation, voltage stabilization, and detection circuit adjustments. This study is limited to a short-term technical evaluation of a low-density rail corridor and does not account for environmental variability, human factors, or long-term reliability indicators. The findings provide practical recommendations for reliability enhancement and can serve as a reference for signaling system optimization in the early operational stages of new railway networks. Overall, the SILSafe4000 system is considered reliable for current operational use, with the need for proactive maintenance and continuous performance monitoring to ensure its reliability.
Automated Meter Reader (AMR) PAMSIMAS Using OCR Technology and Smartphone Utilization Wibowo, Budi Cahyo; Susanto, Arief; Solekhan, Solekhan; Slamet, Sugeng
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10954

Abstract

Manual recording of water meters in the Community-Based Drinking Water and Sanitation Program (PAMSIMAS) is still vulnerable to human error, reporting delays, and operational inefficiencies. This study proposes a smartphone-based Automated Meter Reader (AMR) system that utilizes Optical Character Recognition (OCR) as a low-cost digital solution for rural environments. The system uses a smartphone camera to capture an analog water meter image and processes it through a computer vision pipeline that includes grayscale conversion, bilateral filtering, Canny edge detection, contour-based segmentation, and a full-image OCR fallback mechanism. An experimental evaluation was conducted on 120 analog water meter images with variations in lighting, capture angle, blur level, and meter surface conditions. Digit extraction was performed using Google Vision OCR (online) and Tesseract OCR (offline). The OCR accuracy was calculated based on the compatibility of the digit value of the recognition result with the ground truth value and complemented by confidence score analysis. The test results showed an average OCR accuracy of 91%, a confidence score of 0.87, and an average processing time of 1.27 s per image. Although the system showed stable performance in most test scenarios, the accuracy declined in strong glare conditions and with faulty meters, indicating the limitations of the contour-based segmentation approach. Overall, this smartphone-based AMR system has proven to be feasible and practical for supporting the digitization of community-based water management, with the potential for further development through deep learning-based segmentation.
Development of a Real Time Three Phase Power Parameter Monitoring System Using Programmable Logic Controller (PLC) and Internet of Think (IoT) Putra, Nur Azhary Iriawan Eka; Fitriati, Andi; Muchtar, Akhyar; Fajar, Nurhikmah
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.11044

Abstract

This study presents the development of a real-time three-phase electrical power monitoring system based on a digital power meter, Programmable Logic Controller (PLC), Human–Machine Interface (HMI), and Internet of Things (IoT) technology. The proposed system was designed to acquire key electrical parameters, including voltage, current, and frequency, and to display the measured data consistently for both local monitoring via an HMI and remote monitoring through IoT-enabled devices. Data acquisition from the power meter was performed using Modbus RTU communication, with the PLC acting as the central data processing and control unit, and the HMI and IoT platforms provided visualisation and remote access. The system implementation and testing were performed under different load conditions to evaluate the functionality, data consistency, and communication reliability. The experimental results show that the measurement values displayed on the HMI and IoT platforms are identical, indicating stable data communication and correct register mapping. Minor differences between the power meter readings and the monitoring system were observed, with a maximum error of 0.6195 %, which was attributed to the display resolution and rounding limitations of the power meter rather than data processing errors. The results demonstrate that the developed system is reliable and accurate for real-time three-phase power-monitoring applications. The integration of industrial devices with IoT technology enhances data accessibility, improves the visibility of electrical system conditions, and supports modern monitoring requirements, making the proposed system suitable for industrial and technical facility applications.
Performance Comparison of FLC, P&O, and IC Algorithms for MPPT Optimization in DB-Converter Under Dynamic Partial Shading Hermansyah, Hermansyah; Murdianto, Farid Dwi; Achmad, Alamsyah
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.11091

Abstract

Solar energy can be converted into electrical energy using photovoltaic (PV) panels. The maximum output power of a PV system is achieved when solar irradiance falls directly on the panel's surface without obstruction. However, in practical conditions, solar irradiance is often disturbed by moving or static objects, which causes more than one maximum power point to appear on the P-V characteristic curve. This condition cannot be accurately addressed by conventional MPPT algorithms, thereby requiring advanced methods for partial shading conditions. Various partial shading algorithms have been developed, ranging from traditional methods to artificial intelligence-based approaches. This study presents a comparison between the FLC, P&O, and IC methods in the application of MPPT in the Double Boost Converter under dynamic partial shading conditions. The accuracy of the three methods is evaluated through simulation. The results indicate that all three methods are capable of addressing the effects of partial shading and can maintain high tracking accuracy. Moreover, the FLC method shows better performance in minimizing output oscillations, while the P and O method demonstrates superior tracking precision in reaching the global maximum power point.

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