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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro AUSTENIT Sosio Konsepsia JURNAL NASIONAL TEKNIK ELEKTRO Astonjadro Jurnal Kesehatan Indonesia Penentuan harga obligasi dan teori tingkat bunga ACROPORA: Jurnal Ilmu Kelautan dan Perikanan Papua Jurnal Pengabdian Tri Bhakti SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Irmawan Irmawan
Program Studi Teknik Keselamatan & Kesehatan Kerja
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Dinamika Keuntungan Usaha Sero: Tinjauan Dari Sisi Biaya dan Harga Pasar di Desa Tapulaga, Konawe Irmawan, Irmawan; Annaastasia, Nurhuda; Siang, Roslindah Daeng
ACROPORA: Jurnal Ilmu Kelautan dan Perikanan Papua Vol 8 No 2 (2025): ACROPORA: Jurnal Ilmu Kelautan dan Perikanan Papua Edisi November 2025
Publisher : Cenderawasih University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31957/acr.v8i2.5116

Abstract

Sero merupakan alat tangkap pasif yang bersifat menetap, vital sebagai sumber mata pencaharian nelayan tradisional. Penelitian ini bertujuan menganalisis struktur biaya, selisih harga, serta profitabilitas usaha perikanan tangkap sero oleh nelayan skala kecil di Desa Tapulaga, Kabupaten Konawe, Sulawesi Tenggara. enelitian deskriptif kuantitatif ini menggunakan metode sensus (total sampling) terhadap seluruh unit usaha sero yang beroperasi. Hasil analisis menunjukkan bahwa usaha sero memiliki efisiensi biaya yang luar biasa dengan rata-rata Harga Pokok Penjualan (HPP) yang sangat rendah, yaitu Rp5.902,14/kg. Total biaya produksi bervariasi antar lokasi, dengan sero di habitat Mangrove mencatat pengeluaran tertinggi (Rp1.234.139/bulan) dan Padang Lamun terendah (Rp1.056.236/bulan). Meskipun demikian, profitabilitas dijamin oleh margin bruto yang sangat lebar, terutama untuk ikan komoditas bernilai tinggi seperti Baronang dan Kuwe, yang selisih harganya mencapai hingga Rp44.496,97/kg di zona Terumbu Karang. Secara finansial, Padang Lamun memberikan keuntungan bulanan per unit sero tertinggi (Rp5.885.430,56), diikuti oleh Mangrove (Rp5.645.861,11). Total keuntungan bersih bulanan mencapai Rp16.265.263,89, menegaskan bahwa usaha sero merupakan solusi ekonomi yang ideal dan berkelanjutan bagi peningkatan kesejahteraan nelayan tradisional.
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.
Co-Authors Amahan, Gelaris Nurul Annaastasia, Nurhuda Bhakti Yudho Suprapto Desi, Natsar Fitria, Syarifa Gustini, Gustini Hadi, Sukma Halimatusa’diah, Ima Harahap, S. A. Hasan Basri Hera Hikmarika, Hera Herlina Herlina Hidayatulloh, A. Nururrochman Husada Borneo, STIKES Irsyadi Yani Juliawan, Tri Kaprawi Kaprawi Kristina, Sabar Lestari, Puji Budi Mariam, Popon marthen Tapilouw Nugrahaningsih, Lidia Nuranti, Dewi Sri Nursopiah, Dian Paramita Eka Utami, Nurhabibah Qorbaniati, Nita Rendiansyah, Rendiansyah Rendyansyah Retnaningrum, Elly Reviandari Widyatiningtyas, Reviandari Roslindah Daeng Siang Sakti, Kurniasih Eka Siregar, Satjun Sri Agustina, Sri Sri Wahyuni Sry Gusty, Sry STIKES Husada Borneo, Alumni Suci Dwijayanti Suryati Suryati Syaban, Mumun Wicaksono, Erwin Catur Yeti Nurhayati, Yeti