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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 24 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.

Page 3 of 3 | Total Record : 24

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