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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 6 Documents
 1 
Search results for , issue "Vol. 6 No. 4 (2024): December" : 6 Documents clear
Bond Graph Dynamic Modeling of Wind Turbine with Singly-feed Induction Generator Kadiman, Sugiarto; Yuliani, Oni; Suwarti, Diah
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.v6i4.9409

Abstract

This paper contributes to a modeling part of singly-fed induction generator (SFIG) systems driven by constant wind turbines of generation capacity of 2.5 kW. As a consequence of some physical domains present in wind turbine, that are aerodynamical, mechanical and electrical, the modeling of wind turbine is challenging; therefore, modeling based on physical techniques has a higher credibility in these conditions. One of these ways is Bond-Graph modeling those representations the systems developed from the law of conservation of mass and energy covering in the systems. Bond graph uses causal analysis which is a process for identifying and addressing the causes and effects of a problem; moreover, the model is presented visually so that they are easier and more user friendly. In this paper, modeling the parts of among blades, tower, gearbox, and induction generator are based on the bond-graph method. The blades are modeled based on aerodynamic force model, both tower and gearbox are modeled based on rigid components model, and generator is model based on hybrid mechanic-electric model. Then, all of parts are connected together to accomplish the entire model of wind turbine for simulation based on 20-Sim software. The proposed wind turbine is the 2.5 KW variable speed with three blades, two-mass gearbox, tower, and a singly-fed induction generator type which is used in small and isolated category power generation systems. The model consists of realistic parameters. Using the Bond-Graph modeling method makes it easier to know what is actually happening in the system, for example the direction of energy movement in the system. Simulation results point out better performance of wind turbine with singly-feed induction generator, namely a more constant output current under constant wind conditions compared to varying wind conditions.
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.
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.
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.
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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