SISTEM IDENTIFIKASI PENYAKIT PADA TANAMAN KUBIS MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jordi Sembung; Luther Alexander  Latumakulita; Hanny Andrea Huibert Komalig

doi:10.35799/ijids.v1i2.50078

Authors

Jordi Sembung Universitas Sam Ratulangi
Luther Alexander Latumakulita Universitas Sam Ratulangi
Hanny Andrea Huibert Komalig Universitas Sam Ratulangi

DOI:

https://doi.org/10.35799/ijids.v1i2.50078

Keywords:

fold, cross-validation, data, cnn, cabbage

Abstract

Cabbage is a type of vegetable that is commonly found in Indonesian society. There are various diseases that are a problem for cabbage farmers such as Plasmodiophora brassicae Wor (club root), Alternaria brassicae (alternaria leaf spot), and Xanthomonas campestris (black rot). This study aims to create a system that can identify types of cabbage diseases. Convolutional Neural Network is used to identify the type of disease in cabbage plants based on the image of the leaves. The first stage is the Training and validation process using 5-Fold Cross-Validation and 3-Fold Cross-Validation, Resulting in 8 classification models. The evaluation Results show that the lowest Accuracy is 91.67%, and the highest is 100%. The next stage is the Testing process using new data. The Results show that the worst model has an Accuracy of 80% while the best model has an Accuracy of 100%, which means that the identification model built is quite good and stable in classifying the types of diseases in cabbage. The last stage is the process of identifying or Testing the best model that has been built. 15 new data were entered in the identification process and all data were correctly identified by the system with varying probability values.

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