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All Journal Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
M.B. Gigih Baskoro Ashari
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Implementasi Algoritma Convolutional Neural Network untuk Meningkatkan Identifikasi Penyakit Tanaman Durian M.B. Gigih Baskoro Ashari
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 4 (2024): Juli : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i4.418

Abstract

Detecting diseases in durian plants is a significant challenge in the agricultural sector, impacting yield and quality. This research aims to improve disease identification in durian plants by applying the Convolutional Neural Network (CNN) algorithm. The method involves collecting images of durian leaves, stems, and fruits infected by various diseases and using data augmentation techniques to expand the dataset and reduce overfitting. With the CNN model trained using this dataset, the accuracy reached 62% on validation data, with the highest accuracy for the “Healthy” class at 83%. The research results show that CNN is effective in recognizing diseases in durian plants, although there is still room for improvement through model optimization and the addition of training data. The implications of this research include the development of an AI-based disease detection system that can help farmers care for durian plants more efficiently and timely.
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