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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 12 Documents
 1   2 
Search results for , issue "Vol 14, No 3: November 2025" : 12 Documents clear
A Hybrid Wavelet Scattering and Mel Spectrogram Feature with Deep Convolution Neural Network for Robust Spoken Digit Recognition irmawan, Irmawan; Dwijayanti, Suci; Suprapto, Bhakti Yudho
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1310.2025

Abstract

Spoken digit recognition (SDR) plays a critical role in biometric authentication and human–computer interaction, yet existing approaches often rely on small datasets, limited feature representations, or architectures prone to overfitting. To address these limitations, this study proposes a robust end-to-end pipeline that integrates Wavelet Time Scattering (WTS), Mel-Frequency Cepstral Coefficients (MFCC), and a 2D Deep Convolutional Neural Network (2D-CNN) to enhance the accuracy and generalization of SDR systems in realistic environments. The Free-Spoken Digit Dataset (FSDD), consisting of 3000 audio samples from speakers with diverse accents, was pre-processed using zero-padding normalization and transformed into high-resolution time–frequency spectrograms via WTS. The proposed CNN architecture, optimized through systematic experimentation on batch size and learning rate, demonstrated stable convergence and superior discriminative capability. Using a learning rate of 0.001 and a batch size of 50, the model achieved the highest performance with 99.2% accuracy, outperforming established methods including SVM, MFCC-LSTM, and Multiple RNN architectures. Comparative evaluations further revealed that the combined WTS–MFCC feature extraction significantly enhances spectral–temporal representation quality, contributing to improved classification precision across all digit classes. These findings demonstrate that the proposed WTS-MFCC-CNN framework not only advances SDR accuracy but also provides a scalable and computationally efficient approach suitable for real-world biometric, financial, and voice-controlled applications. The results highlight the potential of hybrid time–frequency representations integrated with deep architectures to set a new benchmark for robust spoken digit recognition.
Optimasi Titik Daya Maksimum Global dan Distorsi Harmonik Arus pada Sistem PV-Inverter menggunakan Algoritma Migrasi Lebah (QHBM) Muhammad Cahyo Bagaskoro; Aripriharta; sujito; Saodah Omar
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1331.2025

Abstract

This paper investigates the optimization of the Global Maximum Power Point (GMPP) and the simulation of Total Harmonic Distortion of Current (THDI) from an inverter connected to a nonlinear load. THDI variations are analyzed with respect to ambient temperature (T) and solar irradiance (G). The study also highlights how harmonic components negatively affect steady-state voltage stability in photovoltaic (PV) systems. The Queen Honey Bee Migration (QHBM) algorithm is applied to optimize GMPP while minimizing THDI. An off-grid PV-inverter system is modeled in MATLAB/Simulink. The model extracts THDI as a function of temperature and irradiance. Simulations cover irradiance from 794.8 to 994.2 W/m² and temperature from 20.0°C to 32.3°C, based on daily measurements from 08:25 to 16:50. The QHBM algorithm tracks GMPP effectively under fluctuating irradiance. Results show a 17.3% improvement in power extraction efficiency and a 32.8% reduction in THDI compared to conventional methods. The highest THDI occurs during low irradiance, particularly in the early morning and late afternoon. The algorithm converges in 0.18 seconds, outperforming other techniques. THDI increases during rapid irradiance and temperature changes. The proposed method ensures stable performance and lower THDI. Combining QHBM with active harmonic filters under low irradiance conditions is recommended to improve power quality and enhance system protection.

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