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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
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Articles 5 Documents
 1 
Search results for , issue "Vol. 7 No. 1 (2025): March" : 5 Documents clear
Exploring IoT Applications for Transforming University Education: Smart Classrooms, Student Engagement, and Innovations in Teacher and Student-focused Technologies Ţălu, Mircea
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12361

Abstract

This review examines the integration of smart management systems in universities through the Internet of Things (IoT), emphasizing its transformative potential to enhance administrative efficiency, improve student engagement, and address critical challenges such as data security and ethical concerns. Using a structured review methodology, we analyzed studies focused on IoT-driven innovations in areas such as energy management, personalized learning environments, and attendance systems. Insights from global case studies, including detailed examples from The Technical University of Cluj-Napoca, Romania, were synthesized to explore the generalizability and applicability of these solutions across diverse institutional contexts. The review followed a systematic approach, selecting studies from reputable academic databases and adhering to predefined criteria for examining IoT integration within university environments. While the findings highlight the significant benefits of IoT for educational management and teaching practices, challenges such as data privacy, system interoperability, and cost barriers remain critical considerations. This comprehensive review aims to guide future research and support the practical implementation of IoT solutions in higher education.
Deep Learning Approaches for Water Quality Prediction in Aquaponics Systems: A Comparative Study of Recurrent and Feedforward Architectures Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12411

Abstract

Accurate prediction of water quality parameters is critical for the effective management and sustainability of aquaponics systems. This study evaluates the performance of four deep learning architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (SimpleRNN), and Dense Neural Network (DenseNN) for forecasting key water quality parameters, including temperature, turbidity, dissolved oxygen, pH, ammonia, and nitrate. A significant research gap is addressed by analyzing how these models perform on noisy and minimally preprocessed datasets, advancing prior studies that lack robust preprocessing techniques tailored for aquaponics systems. A ten-fold cross-validation framework was employed to rigorously assess the models, with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as evaluation metrics. The results demonstrate that LSTM and GRU models outperform other architectures, achieving average validation losses of 0.0028 and 0.0028, respectively, and mean absolute errors of 0.0473 and 0.0478. These models effectively capture the temporal dependencies inherent in time-series data, making them highly suitable for the complex dynamics of aquaponics systems. Unlike previous studies, this research highlights the trade-offs between computational efficiency and predictive accuracy in these models. In contrast, the SimpleRNN model exhibited higher error rates due to its inability to model long-term dependencies, while the DenseNN model, lacking temporal processing mechanisms, showed the lowest performance with an average validation loss of 0.0075 and MAE of 0.0797. This study underscores the importance of selecting appropriate model architectures for time-series forecasting tasks and provides a foundation for deploying predictive systems to optimize aquaponics operations. Future work includes exploring hybrid models with attention mechanisms and real-time data integration for enhanced operational efficiency.
Overload Monitoring and Warning System for 3-Phase Electric Motorcycle based on IoT Adhimanata, Yogi; Sulistiyowati, Indah; Wisaksono, Arief; Ayuni, Shazana Dhiya; Nasar, Muhammad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12262

Abstract

This research aims to develop and test a Telegram-based monitoring and overload warning system for three-phase motors, utilizing the PZEM-004T sensor and ESP8266 WiFi microcontroller, which is able to measure electrical parameters such as current, voltage, and power in real-time. The method used is a quantitative approach with an experimental method, where this system provides automatic notifications via the Telegram application in the event of an overload with a current limit of 20A, and is able to automatically cut off electricity to prevent more serious damage. Given the risk of overload that can cause production damage and costly downtime to the industry, the results show the effectiveness of the system in recording data and detecting overload conditions, although there are power measurement discrepancies that indicate the need for further calibration. Recommendations from this research include developments to improve measurement accuracy, standardization of procedures, and integration with relevant industry standards, as well as additional trials with different load variations to validate the system's performance in various scenarios.
Dielectric Characterization of Breast Cancer Cells using Split-Rectangular Ring Resonator Sensor Jabire, Adamu Halilu; Saminu, Sani; Adamu, Muhammed Jajere; Mohammed, Abubakar Saddiq; Aminu, Sha'awanatu; Sadiq, Abubakar Muhammad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12680

Abstract

Exploring a universal method to enhance the performance of metamaterials by quantifying the impact of gap capacitance is an intriguing topic for many researchers. However, achieving this through conventional methods is extremely challenging. In this paper, we present a microwave sensor designed to characterize cancerous cells based on their electrical properties. The proposed design features a split rectangular ring resonator placed on a flame-retardant four (FR-4) substrate. The sensor aims to achieve high sensitivity and quality factors through the unique characteristics of the metamaterial structure in the GHz frequency range. Through simulations and experimental measurements, we demonstrate the sensor's effective capabilities in detecting cancer. The high sensitivity for both simulation and measurement, is estimated at 10 %. The simulations and validation confirm that this biosensor exhibits significant frequency shifts and high sensitivity. Our proposed configurations highlight the microwave sensor's potential for detecting six different breast cancer cell types: HSS-2, HS578-T_nm, MCF-2, MCF-10A_nm, T-47D, and T-47D_nm. Based on the existing literatures, the sensitivity of the proposed sensor is determined to be greater.
Discount Factor Parametrization for Deep Reinforcement Learning for Inverted Pendulum Swing-up Control Surriani, Atikah; Maghfiroh, Hari; Wahyunggoro, Oyas; Cahyadi, Adha Imam; Fajrin, Hanifah Rahmi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.10268

Abstract

This study explores the application of deep reinforcement learning (DRL) to solve the control problem of a single swing-up inverted pendulum. The primary focus is on investigating the impact of discount factor parameterization within the DRL framework. Specifically, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed due to its effectiveness in handling continuous action spaces. A range of discount factor values is tested to evaluate their influence on training performance and stability. The results indicate that a discount factor of 0.99 yields the best overall performance, enabling the DDPG agent to successfully learn a stable swing-up strategy and maximize cumulative rewards. These findings highlight the critical role of the discount factor in DRL-based control systems and offer insights for optimizing learning performance in similar nonlinear control problems.

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