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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains
Caesar Adhityansyah
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Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences

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Literature Review: Klasifikasi Penyakit Daun Tamanan Kelapa Sawit Menggunakan Convolutional Neural Network Caesar Adhityansyah; Jefi Eliel Tigor Tampubolon; Fransiskus Natalis Eduk; Muhamad Razik
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

The palm oil industry plays an important role in Indonesia's economy, but is vulnerable to foliar diseases that can reduce productivity. PT Agri Palma, an oil palm company in West Kalimantan, faces this challenge, especially in the leaf diseases of Anthracnose, Ganoderma, and Leaf Spot. This study uses Convolutional Neural Network (CNN) to classify leaf diseases through image analysis. The dataset consists of 1,000 leaf images of 224x224 pixel resolution in RGB channel, with 800 images for training and 200 for testing, processed on Google Colab platform. The research aims to develop a CNN-based web application to automatically detect oil palm leaf diseases. Results show that the CNN model achieves 92% accuracy, supporting quick action in disease management and reducing the risk of crop loss.
Co-Authors Fransiskus Natalis Eduk Jefi Eliel Tigor Tampubolon Muhamad Razik