Klasifikasi Jenis Buah Jeruk Menggunakan Metode Convolutional Neural Network: Deep Learning Studi

Yazid Fauzan Nur Ashfani (Universitas Muhammadiyah Ponorogo)
Yovi Litanianda (Universitas Muhammadiyah Ponorogo)
Rizqy Amalia Putri (Telkom University)

Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Vol. 2 No. 2 (2024): Juni: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika

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.