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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 295 Documents
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Multiagent based Power Management for Grid-connected Photovoltaic Source Using the Optimized Network Parameters From Butterworth Inertia Weight Particle Swarm Optimization Olatunde, Oladepo; Okoro, Ugwute Francis; Tola, Awofolaju Tolulope
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.11391

Abstract

An efficient power management technique of a grid-connected renewable source proficiently coordinates the various controllable units necessary in the power system operation. It is achieved by responding to the dynamic load demand through efficient communication and advanced control structures. This paper presents a decentralized multiagent power management technique for a grid-connected photovoltaic/energy storage system using the optimized network parameters from the Butterworth inertial weight particle swarm optimization method. The power network is coordinated by intelligent agents and structured into a zonal generation and load multiagent system to update the load and power injected at different network buses. However, Butterworth inertial weighting function particle swarm optimization determines the optimized network parameters and the capacity of the connected energy sources fed into the multiagent system. The inertial weight of the optimization technique is patterned along the Butterworth filtering curve for holistic space search and improved convergence. Hence, the proposed technique solves the problem of inefficient optimization methods and provides a robust control and management system with agents capable of reorganizing and coping with the system's dynamic changes. The performance analysis of the IEEE 33-Bus distribution system shows an improved network coordinating method. The power loss reduction appreciated significantly from 65.42% to 68.58%, while the voltage deviation improved from 88.19% to 89.95% by integrating a renewable battery system. The voltage is maintained within the operational constraints of daily simulations. The method is targeted at efficient operation of distribution networks.
Advancements in Anode Materials for Cathodic Protection: Nanostructured Alloys, Surface Modifications, and Smart Monitoring Yahaya, Madaniyyu Sulaiman; Nahar, Kapil; Kumar, Dinesh; Usman, Habib Muhammad; Gambo, Abdulhaq Saleh; Umar, Tijjani Aminu; Sulaiman, Mustapha
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11512

Abstract

This review critically examines advancements in anode materials for cathodic protection systems, focusing on overcoming the limitations of traditional materials like magnesium, zinc, aluminum, graphite, lead-silver alloys, and high-silicon cast iron (HSCI). Conventional anode materials, though widely used, face issues such as rapid degradation, high maintenance costs, and environmental harm. Novel materials, including mixed metal oxides (MMO), advanced aluminum-based alloys, nanostructured materials, and conductive polymers, offer superior electrochemical properties, enhanced durability, and improved performance in aggressive environments like seawater. This review also highlights the role of surface modifications and coatings, such as platinum on titanium and ceramic coatings, in boosting corrosion resistance. Moreover, smart monitoring systems, integrated with IoT and SCADA technologies, are explored for their potential to improve the longevity and efficiency of cathodic protection systems. The paper emphasizes the urgent need for sustainable solutions due to the substantial economic and environmental costs of corrosion, particularly in high-risk industries like oil and gas, maritime, and infrastructure. Future research directions, including the development of hybrid systems combining coatings with CP technologies and the application of advanced alloys and nanostructured materials, are proposed to address the long-term performance and ecological impacts of CP systems.
Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks Putra, Rizal Kusuma; Alfarisy, Gusti Ahmad Fanshuri; Nugraha, Faizal Widya; Nuryono, Aninditya Anggari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11619

Abstract

Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images and categorizing the test image accordingly. Thus, SNN has an advantage over classification-based approaches as it can identify various combinations of disease spots on potato plants using a similarity-based approach. This study is divided into two main scenarios: testing with data categories which were previously seen during the training process (traditional testing) and testing with the addition of new data categories that were not seen during training. In the first scenario, SNN showed better accuracy with an accuracy rate of 98.4%, while in the second scenario, SNN achieved an accuracy of 97.1%. That result suggests that SNN can categorize data very well, even recognizing data which never seen during training. These results offer hope that SNN can recognize more disease spots/patterns on potato plants or even identify new diseases by adding these new diseases to the SNN support set without retraining.
Bibliometric Analysis of Explainable AI in Advance Care Planning: Insights, Collaborative Trends, and Future Prospects Futri, Irianna; Muryadi, Elvaro Islami; Saputra, Dimas Chaerul Ekty
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.11641

Abstract

The increasing complexity of healthcare systems has led to a growing need for Advance Care Planning (ACP) to ensure personalized care for patients. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance ACP by providing transparent and interpretable decision-making processes. However, the current landscape of XAI in ACP remains unclear, necessitating a comprehensive bibliometric analysis. This study employed a systematic review of existing literature on XAI in ACP, using a bibliometric approach to analyze publication trends, collaboration patterns, and research themes. One hundred sixty articles were selected from prominent databases, and their metadata were extracted and analyzed using Biblioshiny, the analysis revealed a significant growth in ACP XAI-related publications, focusing on deep learning and natural language processing techniques. The top contributing authors and institutions were identified, and their collaborative networks were visualized. The results also highlighted the prominent themes of patient-centered care, decision support systems, and healthcare analytics. The study's findings have implications for developing more effective XAI-based ACP systems. This bibliometric analysis provides valuable insights into the current state of XAI in ACP, highlighting the need for further research and collaboration to address the complex challenges in healthcare. The study's outcomes can inform policymakers, researchers, and practitioners in developing more effective ACP systems that leverage the potential of XAI.
Neural Network Based Smart Irrigation System with Edge Computing Control for Optimizing Water Use Silaban, Freddy Artadima; Firdausi, Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.11965

Abstract

Efficient irrigation is critical in agriculture, particularly in regions with erratic rainfall. As global water scarcity intensifies, optimizing irrigation processes is essential to ensure sustainable food production. This study proposes a novel smart irrigation system leveraging neural networks and edge computing to enhance water use efficiency and crop yields. The dataset comprises environmental variables, including pH, water level, temperature, and humidity, sourced from reputable open repositories. Preprocessing steps included handling anomalies, encoding categorical variables, and feature standardization. A neural network with optimized architecture was trained using 70% of the data, validated with 15%, and tested on the remaining 15%. The system achieved a testing accuracy of 91.33%, with precision, recall, F1-score, and AUC metrics exceeding industry benchmarks (AUC: Base = 0.99, Ideal = 0.97, Dry = 0.98). The model was deployed on an NVIDIA Jetson Nano using Docker, demonstrating real-time prediction capabilities with minimal latency. The smart irrigation system automates water pump operations based on soil conditions, providing practical benefits such as reduced water waste and improved crop health. With its adaptable design and scalability, this system represents a step forward in sustainable agriculture, contributing to global efforts to address food security challenges.
Improving Dynamic Routing Protocol with Energy-aware Mechanism in Mobile Ad Hoc Network Mekonnen, Atinkut Molla; Munaye, Yirga Yayeh; Chekol, Yenework Belayneh; Bizuayehu, Getenesh Melie; Maghfiroh, Hari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11994

Abstract

A Mobile Ad hoc Network (MANET) is designed for specific communication needs, where nodes dynamically interact. In a MANET, mobile nodes self-configure and frequently adapt to changes in topology due to their ability to move freely. Each node operates as a router, forwarding data to other designated nodes within the network. Since these mobile nodes rely on battery power, energy management becomes critical. This paper addresses the challenges of routing in MANETs by improving the Dynamic Source Routing (DSR) protocol. The proposed enhancement, termed energy-aware DSR, aims to mitigate and reduce packet loss and improve the packet delivery ratio, which often suffers due to node energy depletion. Simulations conducted with the NS-3.26 tool across varying node counts demonstrate that the energy-aware DSR protocol significantly outperforms the traditional DSR in terms of efficiency and reliability.
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.
Comparative Evaluation of Machine Learning Models for UAV Network Performance Identification in Dynamic Environments Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.12409

Abstract

The rapid integration of Unmanned Aerial Vehicles (UAVs) into critical applications such as disaster management, logistics, and communication networks has brought forth significant challenges in optimizing their performance under dynamic and unpredictable conditions. This study addresses these challenges by systematically evaluating the predictive capabilities of multiple machine learning models for UAV network performance identification. Models including RandomForest, GradientBoosting, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), AdaBoost, ExtraTrees, LogisticRegression, and DecisionTree were analyzed using comprehensive metrics such as average accuracy, macro F1-score, macro precision, and macro recall. The results demonstrated the superiority of ensemble methods, with ExtraTrees achieving the highest performance across all metrics, including an accuracy of 0.9941. Other ensemble models, such as RandomForest and GradientBoosting, also showcased strong results, emphasizing their reliability in handling complex UAV datasets. In contrast, non-ensemble approaches such as LogisticRegression and MLP exhibited comparatively lower performance, suggesting their limitations in generalization under dynamic conditions. Preprocessing techniques, including SMOTE for addressing class imbalances, were applied to enhance model reliability. This research highlights the importance of ensemble learning techniques in achieving robust and balanced UAV performance predictions. The findings provide actionable insights into model selection and optimization strategies, bridging the gap between theoretical advancements and real-world UAV deployment. The proposed methodology and results have impact for advancing UAV technologies in critical, network performance-sensitive applications.
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.
Gray Level Co-Occurrence Matrix (GLCM)-based Feature Extraction for Rice Leaf Diseases Classification Nugroho, Herminarto; Pramudito, Wahyu Agung; Laksono, Handoyo Suryo
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.9286

Abstract

In this paper, we propose Gray Level Co-Occurrence Matrix (GLCM) based Feature Extraction to identify and classify rice leaf diseases. An Artificial Neural Network (ANN) algorithm is used to train a classification model. Various statistical features such as energy, contrast, homogeneity, and correlation are extracted from the GLCM matrix to describe the image texture features. After feature removal, an ANN classification model was trained using a dataset consisting of images of healthy and diseased rice leaves. The ANN training process involves optimizing weights and bias using backpropagation to achieve accurate classification. After training, the ANN model is tested using split test data to measure classification performance. The experimental results show that the GLCM method is effective in helping improve accuracy, validation of accuracy, loss, validation of loss, precision, and recall.

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