Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal EDUMATIC: Jurnal Pendidikan Informatika Informatics and Enginering Dedication
Hujaya, Alvin
Unknown Affiliation
Computer Science & IT Education Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PELATIHAN PENGGUNAAN AI DALAM HAL MENGGAMBAR DI SEKOLAH MAITREYAWIRA Hujaya, Alvin; Levid, Jonathan Felix; Ferdilian, M Lazuardi; Saputra, Adi; Batitusta, Putra Regian; Arman, Molavi
FORDICATE Vol 4 No 3 (2025): November 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i3.12076

Abstract

Kegiatan kepada masyarakat tentang Artificial Intelligence atau AI Dalam Hal Menggambar di Sekolah Maitreyawira adalah kegiatan mengajar yang dilakukan untuk memberikan pemahaman dasar kepada para siswa dan masa depan bangsa, agar dapat mengetahui apa itu AI dan cara mengimplementasikannya dengan contoh sederhana yaitu dalam hal menggambar. Kegiatan ini dibuat bertujuan untuk menjawab berbagai permasalahan terkait pekerjaan dan kehidupan manusia di masa depan yang nantinya berhubungan erat dengan AI. Melalui perlatihan ini, diharapkan para siswa dapat dengan mudah mengetahui apa dan manfaat dari penggunaan AI di kehidupan sehari -hari. Selain itu, dari kegiatan ini diharapkan para siswa dapat menggunakan kegiatan ini sebagai bekal untuk beradaptasi di lingkungan yang penuh dengan teknologi dan artificial intelligence di masa depan.
An Efficient Two Stage Detection Segmentation Framework for Automated Road Crack Assessment Hujaya, Alvin; Pribadi, Muhammad Rizky
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33699

Abstract

Road cracks significantly degrade infrastructure quality and pose a threat to traffic safety. To minimize manual inspection inefficiencies, this study investigates a segmentation model integrating MobileNetV3-Small as a backbone for the U-Net architecture to reduce processing time. The performance of the proposed MobileNetV3-Small-U-Net is benchmarked against a standard U-Net using three public datasets: DeepCrack (537 images), CFD (118 images), and Crack500 (3368 images) sourced from GitHub and Kaggle. This research explores the influence of optimization algorithms on evaluation results across these diverse datasets. Specifically, the study evaluates Adam, RMSprop, and SGD optimizers at an image resolution of 224 x 224 pixels, with a 0.001 learning rate and 0.9 momentum. On-the-fly augmentation techniques, including horizontal flips and brightness adjustments (0.8 to 1.2), were implemented during training. Experimental results demonstrate that MobileNetV3-Small-U-Net enhances computational efficiency by achieving a 9 ms inference time, which is 2 ms faster than the standard U-Net. These findings confirm that a MobileNetV3-Small backbone accelerates inference, despite a slight trade-off in evaluation metrics. Additionally, results reveal that the SGD optimizer is unsuitable for these segmentation tasks due to high error rates and the lack of an adaptive learning rate.
Co-Authors Adi Saputra Batitusta, Putra Regian Ferdilian, M Lazuardi levid, Jonathan Felix Molavi Arman Muhammad Rizky Pribadi