Article Per Year (5 Year)
p-Index From 2020 - 2025
1.463
P-Index
This Author published in this journals
All Journal JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Jurnal Penelitian Pertanian Terapan Jurnal Agrotek Tropika Daun: Jurnal Ilmiah Pertanian dan Kehutanan Agritech: Jurnal Fakultas Pertanian Universitas Muhammadiyah Purwokerto Jurnal Abdidas Jurnal Agrotropika Agricultural Science Indonesian Journal of Innovation Studies Algoritme Jurnal Mahasiswa Teknik Informatika PELS (Procedia of Engineering and Life Science) Nabatia
Sutarman .
Universitas Muhammadiyah Sidoarjo
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Algoritme Jurnal Mahasiswa Teknik Informatika

 1 
Analisis Perbandingan Model CNN Terhadap Klasifikasi Citra Komponen Elektronika Arrosyid, Muhammad Zydane; Hermawan, Arief; ., Sutarman
Jurnal Algoritme Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10259

Abstract

This study compares various Convolutional Neural Network (CNN) models in classifying electronic component images. The background of this research stems from the need to automatically identify and classify components in environments with limited computational resources. The data used in this research was collected through image scraping from the internet, supplemented by direct image acquisition using a camera. The data was then processed and trained using several CNN models, including MobileNet, NASNetLarge, VGG16, and others, as well as a custom CNN model developed by the researcher. The results show that NASNetLarge achieved the highest test accuracy of 79.31%, while MobileNet demonstrated high efficiency in computational resource usage. This study highlights that model size does not always correlate with accuracy, and models with fewer parameters can provide effective solutions for resource-constrained conditions.
Co-Authors ACHMAD . Afifati Cahya Murti Ani Suryani Arief Hermawan Arrosyid, Muhammad Zydane ASEP SAEFUDDIN Fadillah Timur Gumelar Febriana Alfianti Lukman Hudi M Fadil Meilani Fatimah Muhammad Burhanuddin Adenan Mulyadi Mulyadi Nur Adi Santoso Risalatul Hasanah Soetrisno Hadi Sriyono Sriyono Susi Ratnawati Syarifa R N Vera Rimbawani Sushanty Vicky Prasetya Putra Zainur Rochman