Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Rizqy Amalia Putri
Telkom University
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Jenis Buah Jeruk Menggunakan Metode Convolutional Neural Network: Deep Learning Studi Yazid Fauzan Nur Ashfani; Yovi Litanianda; Rizqy Amalia Putri
Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 2 No. 2 (2024): Juni: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v2i2.129

Abstract

This study analyzes the use of deep learning, primarily Convolutional Neural Networks (CNN), to categorize various types of citrus fruits. The study attempts to create an automated system that can accurately categorize citrus fruit kinds using image processing techniques. The collection contains 40 photos of four different citrus fruit types: pomelo, mandarin orange, kaffir lime, and lime. The methodology entails gathering photos, preprocessing them to improve quality, and then training a CNN model to classify the fruit varieties. The results show a high accuracy rate of 95% in classifying fruit types, demonstrating that the CNN model is effective for this task. The findings indicate that increasing the dataset and including other fruit species could significantly boost the system's accuracy.
Co-Authors Yazid Fauzan Nur Ashfani Yovi Litanianda