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JURNAL NASIONAL TEKNIK ELEKTRO
Published by Universitas Andalas
ISSN : 23022949     EISSN : 24077267     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Jurnal Nasional Teknik Elektro (JNTE) adalah jurnal ilmiah peer-reviewed yang diterbitkan oleh Jurusan Teknik Elektro Universitas Andalas dengan versi cetak (p-ISSN:2302-2949) dan versi elektronik (e-ISSN:2407-7267). JNTE terbit dua kali dalam setahun untuk naskah hasil/bagian penelitian yang berkaitan dengan elektrik, elektronik, telekomunikasi dan informatika.
Arjuna Subject : -
Articles 15 Documents
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Search results for , issue "Vol 12, No 2: July 2023" : 15 Documents clear
Hyperbolic Tangent - Based Adaptive Inertia Weight Particle Swarm Optimization Yaw Opoku Mensah Sekyere; Francis Boafo Effah; Philip Yaw Okyere
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1095.2023

Abstract

This paper presents a study on using adaptive inertia weight (AIW) in particle swarm optimization (PSO) for solving optimization problems. An AIW function based on the hyperbolic tangent function was proposed, with the function parameters adaptively tuned based on the particle best and global best values. The performance of the proposed AIW-PSO was compared with standard PSO and other PSO variations using seven benchmark functions. The results showed that the proposed AIW-PSO outperformed the other variations in terms of minimum cost and mean cost while reducing the standard deviation of cost. The performance of the different PSO variations was also analysed by plotting the best cost against iteration, with the proposed AIW-PSO showing a faster convergence rate. Overall, the study demonstrates the effectiveness of using an adaptive inertia weight function in PSO for optimizing problems.
Perancangan Kendali Operasi Otomatis Terowongan Angin ILST Berbasis HMI Franky Surya Parulian; Munawar Agus Riyadi; Ivransa Zuhdi Pane; Muhamad Muflih
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1098.2023

Abstract

In the application of the Indonesian Low-Speed Tunnel (ILST), the control of wind tunnel operations can determine the validity of the data and the number of tests achieved daily. The current operation control mechanism is still done manually and separately with one series of measurements for one test model configuration, inefficient human resources, acquisition of data that can be different, and the cost of using electric power is quite expensive. Therefore, this research and development activity proposes a wind tunnel automatic operation control system that integrates several plant facilities and ILST data acquisition based on Human Machine Interface (HMI) with the Waterfall method, using SCADA software and PLC. This aims to improve wind tunnel operation in one measurement series for multiple test model configurations with high data acquisition accuracy, faster and easier operation to reduce operating costs. This automatic operation control can increase operation time two times faster and 61% cheaper than manual operation. The design results will be used at the implementation stage in aerodynamic model testing.
Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype Michelle Valerie; Irma Salamah; Lindawati
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1105.2023

Abstract

This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.
Model Berbasis Transfer Learning untuk Deteksi Tumor Otak pada Citra MRI Faiz Rofi Hencya; Satria Mandala; Tong Boon Tang; Mohd Soperi Mohd Zahid
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1123.2023

Abstract

Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.
Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators Abdul-Fatawu Seini Yussif; Elvis Twumasi; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1124.2023

Abstract

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.

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