Pendeteksi Penyakit Daun Kentang Menggunakan Algoritma Convolutional Neural Network (CNN)

Arvi Pramudyantoro; Muhamad Kurniawan; Hendi Hendra Bayu

doi:10.30606/rjti.v5i2.4740

Authors

Arvi Pramudyantoro Universitas Muhammadiyah Bangka Belitung
Muhamad Kurniawan Universitas Pertiba Bangka Belitung
Hendi Hendra Bayu Universitas Muhammadiyah Bangka Belitung

DOI:

https://doi.org/10.30606/rjti.v5i2.4740

Keywords:

Convolutional Neural Network, Daun Kentang, Deep Learning, Klasifikasi Citra, Penyakit Tanaman

Abstract

Potato leaf disease is one of the main problems in potato cultivation because it can reduce plant quality, decrease crop yield, and cause economic losses for farmers. Manual disease detection still has limitations because it depends on farmers’ experience and is prone to errors, especially when disease symptoms have similar visual characteristics. This study aims to apply the Convolutional Neural Network (CNN) algorithm to predict potato leaf diseases based on digital images. The dataset used in this study was obtained from Kaggle and consisted of 1,500 potato leaf images divided into three classes: healthy leaves, early blight, and late blight. The research stages included dataset collection, data splitting into training, testing, and validation data, CNN modeling using Jupyter Notebook, model training with 50 epochs, model evaluation using a Confusion Matrix, and model implementation into a web-based system using Flask. The test results show that the CNN model was able to classify potato leaf diseases with an accuracy of 97%. These results indicate that CNN is effective in recognizing visual patterns in potato leaf images, such as color changes, spots, and leaf damage. This study is expected to serve as a basis for developing an early detection system for potato leaf diseases that is faster, more accurate, and easier for farmers to use.

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Author Biographies

Arvi Pramudyantoro, Universitas Muhammadiyah Bangka Belitung

Program Studi Ilmu Komputer, Fakultas Teknik dan Sains

Muhamad Kurniawan, Universitas Pertiba Bangka Belitung

Program Studi Sains Data

Hendi Hendra Bayu, Universitas Muhammadiyah Bangka Belitung

Program Studi Konservasi Sumber Daya Alam, Fakultas Teknik dan Sains

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