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All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Emerging Information Science and Technology
Ikhwal, Ikhwal
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Social Sciences

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IMPLEMENTASI TEKNIK ELEKTRO DARI RUMAH KE REVOLUSI INDUSTRI 4.0 PADA SISWA SMP AL-AZHAR MANDIRI PALU Anthonius Onsik, Leonard; Ikhwal, Ikhwal; Sutikno, Tole; Dwi Puriyanto, Riky
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 12 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i12.%p

Abstract

Kegiatan sosialisasi bertema “Teknik Elektro dalam Kehidupan: Dari Rumah ke Revolusi Industri 4.0” dilaksanakan sebagai bagian dari mata kuliah Program Pemberdayaan Umat (PRODAMAT) Universitas Ahmad Dahlan. Kegiatan ini bertujuan untuk mengenalkan konsep dasar teknik elektro kepada siswa SMP melalui pendekatan interaktif dan kontekstual. Pelaksanaan kegiatan dilakukan di SMP Al-Azhar Mandiri Palu pada tanggal 11 Juli 2025, dengan peserta sebanyak 113 siswa kelas 8. Metode kegiatan menggunakan pendekatan Participatory Action Research (PAR), meliputi pre-test, penyampaian materi, demonstrasi alat, dan post-test. Hasil evaluasi menunjukkan adanya peningkatan pemahaman peserta dari skor rata-rata pre-test 78,44 menjadi 89,39 pada post-test, atau peningkatan sebesar 14,11%. Hasil ini menunjukkan bahwa pendekatan edukatif berbasis praktik langsung efektif dalam meningkatkan literasi teknologi siswa tingkat menengah.
Audio Noise Reduction Using a U-Net Convolutional Neural Network Architecture Ikhwal, Ikhwal; Pranolo, Andri; Firdausy, Kartika
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.33075

Abstract

Audio quality can be degraded by noise contamination, while conventional noise reduction methods such as the Wiener Filter may have limitations in preserving audio spectral characteristics. This study develops an audio noise reduction system using a U-Net Convolutional Neural Network (CNN) based on Short-Time Fourier Transform (STFT) spectrograms to reduce white noise. The dataset consisted of seven accompaniment audio files from the Indonesian Robot Dance Art Contest (KRSTI), with five used for training and two for testing. Clean audio was contaminated with white noise at SNR levels of 0, 5, 10, and 15 dB and transformed into STFT spectrograms, while phase information was retained for reconstruction. The U-Net was trained to learn the mapping between noisy and clean spectrograms and compared with the Wiener Filter as a conventional baseline using SNR, MSE, MAE, LSD, and SI-SDR. The results show that U-Net achieves lower average MSE (0.002453), MAE (0.032514), and LSD (9.663 dB), while the Wiener Filter achieves higher average ΔSNR (3.327 dB) and SI-SDR (12.251 dB) than U-Net, which achieves 1.915 dB and 10.073 dB, respectively. These findings indicate that the two methods exhibit different strengths depending on the evaluation metric. Further research should investigate larger and more diverse datasets, particularly real-world noise conditions, to improve model robustness and generalization.
Co-Authors Andri Pranolo Anthonius Onsik, Leonard Dwi Puriyanto, Riky Kartika Firdausy Tole Sutikno