CLASSIFICATION OF HERBAL PLANTS USING THE CONVOLUTION NEURAL NETWORK (CNN) ALGORITHM

ROSYIDAH, WADHIFATUR (2024) CLASSIFICATION OF HERBAL PLANTS USING THE CONVOLUTION NEURAL NETWORK (CNN) ALGORITHM. Tugas Akhir thesis, Informatics.

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Abstract

ABSTRACT Herbal plants are a type of plant that is often used by various groups of people, one of which is to prevent and treat various diseases. For example, betel leaves can be used to relieve coughs. Apart from leaves, other parts such as roots, fruit, flowers and skin of herbal plants also have benefits. The use of herbal plants as medicine has been known in Indonesia since ancient times and has been passed down from generation to generation. However, with the advent of new modern medicines and the lack of knowledge about herbal plants, the use of herbal medicines has decreased. Therefore, classifying plants using the Convolutional Neural Network (CNN) algorithm can help people recognize plant species based on images of their leaves. In this research, researchers created a Convolutional Neural Network (CNN) model using a dataset of herbal plants consisting of five types, namely soursop leaves, guava leaves, aloe vera, papaya leaves and basil leaves, with an accuracy level of 90%. Keywords: Plants, Herbal Plants, Deep Learning, Convolution Neural Network, Classification.

Item Type:	Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User:	Kaprodi S1 Informatika UTY
Date Deposited:	22 May 2024 07:11
Last Modified:	22 May 2024 07:11
URI:	http://eprints.uty.ac.id/id/eprint/15604

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