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Journal of Electronics Technology Exploration (JOeTEx)
Published by shm publisher
ISSN : 30253470     EISSN : 30261066     DOI : https://doi.org/10.52465/joetex.v1i2
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Journal of Electronics Technology Exploration (JoETEX) p-ISSSN: 3025-3470, e-ISSSN: 3026-1066 is a peer-review and open-access journal published in every six months, namely in June and December. The Journal of Electronics Technology Exploration (JoETEX), published by SHM Publisher. The Journal aims to offer a digital platform for academics and specialists to submit novel concepts and critical reviews that consider past successes and upcoming difficulties in electronics and sustainable electrical engineering. The scope of the journal comprises the state-of-the-art developments in electronics and electrical engineering related fields. The scope includes, but is not limited to the following topics: Electronic Circuits and Systems Embedded Systems Electronics Analogue Circuits Microelectronics Power Electronics Digital Electronics Medical Electronics Semiconductor Devices Electrical and Autonomous Vehicles Electronic Materials and Devices Systems and Control Engineering Cyber Security, Artificial Intelligence and Internet of Things Circuits for Communication Systems Realization of Microwave, Antenna, and Radar Systems Flexible AC Transmission Systems Modern Power Systems (Microgrids and Smart Grids) Renewable Energy and Energy Storage Systems Electric Power Sustainable Technologies Power Equipment Planning & Asset Management Energy Efficiency and Low Carbon Emission Substation Automation Systems Faults Identification and Quantification Online Condition Monitoring and Self-Healing Technologies
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 29 Documents
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Performance Analysis of Long Short-term Memory (LSTM) Model for Remaining Useful Life Prediction on Turbofan Engine Syuhada, Themy Sabri
Journal of Electronics Technology Exploration Vol. 3 No. 1 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i1.585

Abstract

Accurate Remaining Useful Life (RUL) prediction is critical for the predictive maintenance and operational safety of aircraft turbofan engines. This research develops and evaluates a stacked Long Short-Term Memory (LSTM) network for RUL prediction using the NASA C-MAPSS FD001 dataset as a fundamental case study. A systematic data preprocessing pipeline was employed, including sensor selection, RUL value clipping at 130 cycles, and feature normalization to prepare the data for modeling. The LSTM model was trained with regularization techniques and an EarlyStopping callback to ensure robustness and prevent overfitting. Evaluation results on the unseen test data show the final model achieved a solid and competitive performance with a Root Mean Squared Error (RMSE) of 15.22 and a PHM08 Score of 311.20. These results demonstrate that a well-configured LSTM architecture provides a reliable baseline for engine prognostic tasks, exhibiting strong generalization capabilities on new data.
Implementation of Retrieval-Augmented Generation (RAG) and Large Language Models (LLM) for a Document and Tabular-Based Chatbot System Rafidhul Haque, Imam Chalish
Journal of Electronics Technology Exploration Vol. 3 No. 1 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i1.588

Abstract

The challenge of accessing information from disparate sources—unstructured documents and structured tabular data—hinders efficiency in enterprise information systems. This study addresses this challenge by presenting the design, implementation, and validation of a unified chatbot system powered by Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). For unstructured documents, the system implements a RAG pipeline utilizing ChromaDB for vector indexing and OpenAI embeddings. Meanwhile, for structured data, it leverages a Text-to-SQL engine to translate natural language queries into SQL commands, with results visualized via QuickChart. The architecture is built upon a modular FastAPI backend with role-based access control and was rigorously validated through blackbox functional testing. Results demonstrate 100% functional success across all endpoints, confirming the architecture's reliability. This study confirms the viability of a unified RAG and Text-to-SQL architecture, offering a practical blueprint for creating more intelligent and integrated data interaction systems in enterprise environments.
Programming the 8031 Minimum System in Proteus Simulator using the C: Issues and Solutions son maria, putut; Susianti, Elva
Journal of Electronics Technology Exploration Vol. 3 No. 1 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i1.592

Abstract

An essential required course in electrical engineering, computer science, and informatics is the microprocessor. Students may consider using Proteus software in cases wherein microprocessor trainers are unavailable. Yet, the simulation of the 8031 microprocessor-based minimum system circuit that Proteus executes fails to operate correctly, despite the fact that the source code and circuit wiring comply to programming and circuit theory standards. This is in contrast to other microcontroller-based minimum system circuits that it can be simulated successfully and as intended. This research aims to get hints in programming the 8031 minimum system circuit simulated using Proteus. The problem was investigated and analyzed by observing the parameters that become the properties of each element in the circuit, especially the RAM, then comparing them with the specifications of the microprocessor. The experimental results showed that some adjustments on the program code were necessary either written using assembly language or C program code.
Ev Battery Controller Tuning For Efficient Thermal Management Based On Grasshopper Algorithm And Particle Swarm Optimization Algorithm Allif Nazmie; Hanafi, Dirman
Journal of Electronics Technology Exploration Vol. 3 No. 1 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i1.602

Abstract

Electric Vehicles (EVs) offer low emissions and reduced fossil fuel dependence but require efficient battery thermal management to ensure performance and safety. This research aims for tuning proportional-derivative(PD), proportional-integral(PI) and proportional-integral-derivative (PID) controller for Electrical Vehicle (EV) Thermal Management System using Particle Swarm Optimization (PSO) and Grasshopper Optimization Algorithm method (GOA) method to optimize the compressor power consumption to contribute to the development of better EV battery thermal management systems. By minimizing and maximizing the factors involved in the challenges, optimization is the process of identifying the best way to make something as useful and effective as feasible. Simulation results show that GOA outperforms PSO for all controllers. Objective function values for GOA are lower, 1.6783 (PD), 0.8517 (PI), and 0.8114 (PID), compared to PSO, 1.7578, 0.8665, and 0.8254, respectively. Improvement percentages of GOA over PSO are 4.73% (PD), 1.70% (PI), and 1.65% (PID). The PID controller achieved the best performance overall, showing 51.65% improvement over PD and 4.91% over PI. The findings confirm that GOA is more effective than PSO in optimizing controller performance, and that PID is the most suitable for stable and efficient EV battery thermal management.
Integrating Renewable Energy Solutions through Collaborative Research: A Pathway to Sustainable Urban Development wijaya, yusmar; rafiq, arif ainur
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.572

Abstract

The rapid pace of global urbanization has intensified the demand for sustainable energy solutions, prompting the integration of renewable energy into urban development plans. This paper explores the critical role of collaborative research in overcoming the challenges associated with transitioning to renewable energy in urban areas, focusing on the contributions of bibliometric analysis. This study uses a bibliometric approach to analyze global research trends, key publications, and influential authors in renewable energy and urban planning. The analysis highlights the growing body of literature, revealing significant clusters of research activity centered around sustainable development, urban planning, and renewable energy technologies. Case studies of cities like Freiburg and San Francisco, which have successfully integrated renewable energy technologies, demonstrate how collaborative approaches can lead to more resilient and sustainable urban environments. Despite significant progress, challenges such as infrastructural investments, retrofitting complexities, and regulatory hurdles remain. The paper emphasizes the importance of continued collaborative research to address these barriers and ensure that the benefits of renewable energy are equitably distributed among urban populations. In addition, the study underscores the necessity of interdisciplinary collaboration and public engagement in fostering broader adoption and support of renewable energy technologies. The findings advocate for strengthened partnerships and continued innovation, supported by bibliometric insights, to facilitate the global transition towards sustainable urban energy systems. This research provides a comprehensive understanding of the evolving landscape of renewable energy research and its critical role in shaping sustainable urban development.
Agrivoltaics in Japan: A Review of Current Practices, Challenges, and Future Directions Aditama, Kevin Muhammad Tegar; Al Wafi, Ahmad Zein
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.642

Abstract

This review examines agrivoltaics in Japan integrating solar photovoltaic (PV) systems with agricultural production as a dual-use land strategy to address constrained arable land, decarbonization goals, and energy security. Using a thematic synthesis of published studies and documented Japanese cases, the paper maps current deployment practices, reported agronomic and energy outcomes, and the main constraints shaping adoption. The literature indicates that well-designed agrivoltaic configurations can maintain crop production while adding renewable electricity generation, with outcomes strongly influenced by site conditions, crop type, shading design, and farm management. Evidence also points to potential co-benefits such as reduced heat stress and improved microclimate stability, but trade-offs may emerge for light-sensitive crops or under suboptimal PV spacing and height. Key barriers in Japan include high upfront investment, complex permitting and compliance requirements, and concerns over land-use integrity and long-term agricultural continuity. Future research should prioritize longitudinal field data on crop yield and quality, soil and water dynamics, and ecosystem effects, alongside standardized performance metrics and policy/financing mechanisms that align farmer incentives with grid and climate objectives.
Malaria Disease Detection System in Humans Using Convolutional Neural Network (CNN) Yana, Natasya Siska Fitri; Shabaha, Achmad Rozin; Unjung, Jumanto
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.646

Abstract

Malaria is a deadly disease transmitted by the Plasmodium parasite. Detection is performed by trained microscopists who analyze microscopic images of blood smears. This analysis can be done automatically using modern deep learning techniques. The need for skilled labor can be significantly reduced by developing accurate and efficient automated models. In this article, we propose a fully automated convolutional neural network (CNN)-based model for diagnosing malaria from microscopic images of blood smears. Various techniques including knowledge distillation, data augmentation, autoencoder, feature extraction with CNN model to optimize and improve model accuracy and reasoning performance. Our deep learning model can detect malaria parasites from microscopic images with 95% accuracy requiring more than 27,600 images. This shows that the mode is able to provide more accurate predictions compared to malaria disease detection models using other algorithms such as in previous studies with an accuracy of 90%. By using CNN algorithm, this article can contribute novelty in the development of effective malaria detection methods for malaria disease.
Milkfish Freshness Detection Based On Eye Images Using Convolutional Neural Network (CNN) With Mobilenetv3 Architecture On A Mobile Application Musaadah, Khalimah; Afuan, Lasmedi; Permadi, Ipung
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.649

Abstract

Indonesia has abundant fishery resources, making it one of the world's largest producers and consumers of fish. One of the most commonly consumed types is milkfish (Chanos chanos). Before consumption, it is important to determine the freshness level of the fish. This freshness can be identified using a Convolutional Neural Network (CNN) model with the MobileNetV3 architecture, which is efficient and suitable for mobile application implementation. This study aims to detect the freshness level of milkfish based on eye images using the MobileNetV3 CNN architecture implemented in a mobile application. The dataset used consists of 500 images, divided into training, validation, and testing sets with proportions of 70%, 20%, and 10%, respectively. The data underwent preprocessing, including resizing and image augmentation, to increase data variation. The model was developed using hyperparameter tuning with both random search and grid search methods. The results show that random search achieved better performance with a training accuracy of 92.88%, validation accuracy of 89.90%, and an overall test accuracy of 91%. The trained model was successfully implemented into a mobile application named ScanBang, which can classify the freshness level of milkfish and display its confidence score in a practical and user-friendly manner.
Classification of Pancreatic Cancer Diagnosis with CatBoost Using Urine Biomarker Combination Tanga, Yulizchia Malica Pinkan; Utami, Putri; Darmawan, Aditya Yoga; Unjung, Jumanto
Journal of Electronics Technology Exploration Vol. 4 No. 1 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v4i1.651

Abstract

Uncontrolled cell growth in the pancreatic gland, is one of the most aggressive types of cancer with a high mortality rate, called pancreatic cancer. This research focuses on improving early diagnosis methods for pancreatic cancer by using CatBoost. Urine biomarker datasets were collected and subjected to pre-processing, including label coding, standardized scaling, and balancing via the Synthetic Minority Oversampling Technique (SMOTE). The CatBoost model achieved an accuracy of 98.89%, specificity of 99.35%, sensitivity of 98.71%, and Area Under the Curve (AUC) of 0.9951. These results show that the CatBoost model significantly outperforms the diagnosis models in previous studies, overcoming the challenges of early detection and classification of pancreatic cancer. This study shows that CatBoost is effective for diagnosing pancreatic cancer and suggests that future research explore other models on larger and more diverse datasets.

Page 3 of 3 | Total Record : 29

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