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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 228 Documents
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Comparative Forecasting of Antam Gold Prices Using LSTM, GRU, and Hybrid LSTM–GRU with Adam and SGD Optimizers Yuris Alkhalifi
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13794

Abstract

People commonly invest in gold to protect their assets because of its role as a stable, safe-haven asset against economic volatility. However, the uncertainty of future gold price movements creates challenges, making the ability to predict gold prices beneficial for analysis. This study proposes a comparative approach to forecasting PT Antam's gold prices, evaluating the performance of three architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Hybrid LSTM-GRU. The study uses historical daily price data from 2010 to early 2025, chronologically split into 80% training and 20% testing sets. Data is processed through a 60-day sliding window framework, and recursive multi-step forecasting is applied for a 30-day horizon. Two optimization techniques, Adam and Stochastic Gradient Descent (SGD), are evaluated. The experimental results show that the Hybrid LSTM-GRU model with Adam optimization achieved the best performance among the evaluated configurations, evidenced by an R-Squared (R²) value of 0.9972, a Mean Square Error (MSE) of 9.553542e+07, and a Root Mean Square Error (RMSE) of 9,774.22. An RMSE of 9,774.22 indicates that the standard deviation of the prediction errors is approximately IDR 9,774. When contextualized with the latest actual gold price, this yields an error ratio of 0.64%. The 30-day projection indicates a downward trend in prices until the end of January 2025. However, due to the univariate nature of the model, it should be viewed as a supplementary analytical tool rather than a sole basis for financial decision-making.
Development of an ESP32-Based Flood Monitoring and Early Warning System Using HC-SR04 Hilda Ashari; Dessy Ana Laila Sari
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13852

Abstract

This study aims to develop and implement an ESP32-based flood monitoring and early warning system capable of monitoring water level changes in real time. The system utilizes an HC-SR04 ultrasonic sensor to measure water levels, an OLED display for data visualization, and a buzzer along with smartphone notifications through Alarm Manager as early warning mechanisms. The research employed an experimental method consisting of hardware and software design, system programming using Arduino IDE, and performance testing using an aquarium model with water level variations ranging from 0 to 21 cm and an initial sensor to water surface distance of 5 cm. Water levels were classified into three conditions: Safe (19-21 cm), Warning (15-18 cm and 11-14 cm), and Danger (7-10 cm and 0-6 cm). The experimental results demonstrate that the ultrasonic sensor was able to detect water level changes consistently and reliably. Although an average measurement difference of 4,92 cm was observed compared to manual measurements, this discrepancy can be minimized through software calibration. Furthermore, the integrated warning system, consisting of a buzzer and smartphone notifications, performed effectively, with warning and danger alerts being delivered approximately one second after water level changes were detected. These findings indicate that the proposed system is capable of monitoring water levels and providing automatic real-time flood early warnings, making it a promising solution for flood risk mitigation and disaster preparedness.
Multi-Trajectory Performance and Run-to-Run Consistency of GWO-Tuned PID Control for a Differential-Drive Mobile Robot Auliya Nabila; Agung Muhamad Toha
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13911

Abstract

Accurate trajectory tracking of differential-drive mobile robots is strongly influenced by proportional–integral–derivative (PID) controller parameters, while a single fixed parameter set may exhibit different performance across trajectories with distinct geometric characteristics. This study investigates the multi-trajectory performance and run-to-run consistency of Grey Wolf Optimizer (GWO)-based PID tuning for a differential-drive mobile robot. A kinematic robot model with linear and angular-velocity PID control is evaluated on four reference trajectories: circle, lemniscate, square, and S-curve. A systematically tuned fixed PID controller is employed as the common baseline, while GWO independently optimizes six PID gains for each trajectory using an objective function combining the integral of time-weighted absolute error (ITAE) and control effort. To account for the stochastic nature of GWO, 30 independent optimization runs are performed for each trajectory and performance is assessed using root mean square error (RMSE), ITAE, standard deviation, median, interquartile range, and coefficient of variation. The simulation results show that GWO-PID reduces the mean RMSE from 0.07767 to 0.04979 m for the circle, from 0.08113 to 0.06393 m for the lemniscate, and from 0.09047 to 0.05095 m for the S-curve, corresponding to improvements of 35.90%, 21.20%, and 43.68%, respectively. The square trajectory exhibits an increase in mean RMSE from 0.07728 to 0.08154 m, indicating that optimization does not provide uniform improvement across all trajectory geometries. Run-to-run analysis further reveals substantial differences in optimization consistency with RMSE coefficients of variation of 54.17% for the circle, 85.33% for the lemniscate, and 12.03% for the square, and 0.75% for S-curve. These findings indicate that the effectiveness and repeatability of GWO-based PID tuning are trajectory-dependent, highlighting the importance of multi-run statistical evaluation when assessing metaheuristic controller tuning for mobile robot trajectory tracking.
IoT-Based Tele-ECG Systems: A Systematic Review of Architecture, Signal Quality, and Transmission Reliability Rahma Ajeng Puspitasari; Dio Alif Pradana; Agoes Santika Hyperastuty
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13930

Abstract

Objective: Cardiovascular disease remains the leading cause of death worldwide, and early arrhythmia detection through electrocardiography (ECG) is critical; however, access to conventional ECG equipment at primary healthcare facilities remains limited. Portable Internet of Things (IoT)-based tele-electrocardiography (tele-ECG) systems have been proposed to close this gap; however, evidence on their architecture, signal quality, and transmission reliability remains scattered, with no integrated synthesis available. This review maps IoT-based Tele-ECG architecture, signal quality methods, and transmission reliability challenges, identifying gaps relative to prior scoping reviews on wearable and home-use ECG devices. Method: A Systematic Literature Review was conducted following PRISMA, searching Scopus and IEEE Xplore on 9 July 2026. Three independent reviewers screened the records and assessed full-text eligibility, with study quality appraised using the Mixed Methods Appraisal Tool. From 55 initial records, 31 studies (2020–2026) met the full-text verified inclusion criteria. Results: Low-cost architectures were dominated by AD8232/AD823X/MAX30003 sensors paired with ESP32/ESP8266/ STM32/nRF-series microcontrollers (48.4% of studies), which transmitted data via Bluetooth Low Energy, Wi-Fi, or cloud platforms. Signal quality evaluation relies on Signal Quality Index metrics or deep learning classifiers (29.0%), while reported challenges span latency, packet loss, power consumption, and data security. Novelty: Unlike prior reviews addressing device technology or clinical validation separately, this review is the first to jointly synthesize architecture, signal quality, and transmission reliability evidence for IoT-based Tele-ECG systems, revealing that no study has evaluated all three dimensions within one integrated protocol under realistic, resource-limited network conditions, which is a gap with direct implications for future low-cost Tele-ECG system design and validation.
Usability, Motivation, and Acceptance in Web- and AR-Based English Pronunciation Learning: A PLS-SEM TAM–SDT Approach Zelli Ghea Mardi Anugrah; Hadziq Fabroyir
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13994

Abstract

This study evaluates user acceptance and motivation in English pronunciation learning across web-based and Augmented Reality (AR) platforms Utilizing a comparative design with a sample of 46 students, a web application (developed with Laravel and Livewire) and an AR application (developed with Unity and Vuforia) were constructed, both integrating the SpeechAce API for real-time pronunciation feedback Usability was measured using the System Usability Scale (SUS), while structural associations between technology acceptance and intrinsic motivation variables were evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Both systems demonstrated excellent usability, yielding average SUS scores of 93.33 for the Web platform and 83.54 for the AR platform. Due to initial instrument instability and construct invalidity in the original measurement model, post-hoc model reconstruction was performed, rendering this structural analysis exploratory in nature. In the reconstructed model, Intrinsic Motivation (IM) exhibited a strong, positive structural association with Perceived Ease of Use (PEOU) across both platforms. However, the association between IM and Perceived Usefulness (PU) was significant only on the AR platform. Conversely, the association between PEOU and PU was significant only on the Web platform. On both platforms, Attitude Toward Using (ATU) was primarily driven by PEOU rather than PU, which had no direct significant association with ATU. These exploratory findings suggest that integrating immersive features in AR can align learners’ subjective enjoyment with their perception of functional utility. This highlights the potential of learner-centered design to enhance engagement in digital pronunciation tools.
Design and Performance Evaluation of an Arduino Uno-Based Motorcycle Security System Using Fingerprint and PIN Authentication Bagus Prasetiyo; Zaenal Abidin; Muh. Rafly Armansyah
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.14025

Abstract

This study aims to design and evaluate the performance of a motorcycle security system based on an Arduino Uno, using an R503 fingerprint sensor as the primary authentication method and a personal identification number (PIN) code as a backup mechanism. The hardware consisted of an Arduino Uno, R503 fingerprint sensor, keypad, relays, DFPlayer Mini, buzzer, speaker, and LM2596 step-down converter. The system performance was evaluated through component and functional testing of the fingerprint sensor under four finger-surface conditions (dry, oily, wet, and dusty), with four trials per condition. Contaminants were applied to the sensor surface using cooking oil (oily), water (wet), and household dust (dusty), which were evenly applied to the fingertip surface of the sensor. The performance was measured based on the recognition success rate and average response time, with a scan considered successful if the sensor returned a positive fingerprint match within the detection time-out. The results show that registered fingerprints were successfully recognized under dry, oily, and dusty conditions with an average recognition success rate of 100% and an average response time of 1.03–1.39 s (dry: 1.03 ± 0.10 s; oily: 1.39 ± 0.14 s; dusty: 1.29 ± 0.08 s), whereas the wet condition significantly reduced the recognition performance to 0% with a response time of 2.43 ± 0.10 s. When fingerprint authentication failed, the PIN successfully activated the manual-ignition function. These findings indicate that the combination of fingerprint and PIN authentication functions reliably as an alternative access mechanism under most tested conditions, although this study is limited to functional prototype testing and does not evaluate the resistance to real security attacks or the actual reduction in motorcycle theft incidents.
Photovoltaic Power Output Prediction Using PCA–KNN: A Comparative Study with SVR and MLP on Historical Operational Data Ilham Maridi; Fiky Anggara; Martati Martati
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.14355

Abstract

The transition toward renewable energy integration in power systems presents challenges in managing photovoltaic (PV) power output owing to variations in operating conditions. This study evaluated the performance of K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), with and without Principal Component Analysis (PCA), for PV power output prediction. The dataset comprised 420 observations recorded at five-minute intervals over seven days of measurements from a PV system. The data were chronologically divided into 80% training and 20% testing sets, while normalization and PCA were exclusively fitted to the training data to prevent information leakage. The model performance was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The results show that KNN and PCA-KNN achieved the best and identical performance, with an MSE of 265.3385, RMSE of 16.2892, and R² of 0.8421. PCA did not improve KNN accuracy but maintained its predictive performance after the dimensional transformation. PCA-MLP showed a slight improvement over MLP, whereas SVR and PCA-SVR yielded lower performances. These findings indicate that the effect of PCA depends on both the dataset characteristics and the learning algorithm. Further studies with longer observation periods, more diverse datasets, and external validation are recommended to assess the generalizability of the models.
Development and Evaluation of an Indonesian Thesis-Title Similarity Detection System Using Hybrid TF-IDF–SBERT Retrieval and LightGBM Reranking Abdul Ma'arief Al Imran; Mursyid Ardiansyah; Ali Asgar Zainal Abidin
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.14414

Abstract

This study developed and evaluated an Indonesian thesis-title similarity detection system to support the early screening of potentially similar research titles. The reference corpus consisted of 28,117 unique thesis titles collected from a publicly accessible institutional repository. The system integrates TF-IDF-based lexical retrieval, SBERT-based semantic retrieval, Union Top-K candidate selection, and machine learning reranking. Logistic Regression, XGBoost, and LightGBM were trained using threshold-derived pseudo-labels, while the final model performance was evaluated on an independent human-validated dataset constructed from a title-disjoint evaluation partition. A total of 900 candidate title pairs were independently assessed by two academic validators, achieving a Cohen’s kappa of 0.877. LightGBM achieved the best performance, with an accuracy of 94.67% and a macro F1-score of 0.9467. ISO/IEC 25010 evaluation showed a 100% functional pass rate, an 84.17% usability score, performance scores of 92.3% on mobile and 96.8% on desktop, while the automated security assessment categorized the staging deployment as Fairly Secure with two medium-risk findings. The system can support academic title-similarity screening; however, its generalizability remains limited by the use of a single-institution corpus and requires further multi-institutional validation.

Page 23 of 23 | Total Record : 228

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