Classification of Dried Clove Flower Quality using Convolutional Neural Network

F.A.CHALIK

Informasi Dasar

21.04.3231
006.37
Karya Ilmiah - Skripsi (S1) - Reference

Detecting cloves quality in Indonesia still uses manual labor. Therefore, errors often occur in sorting cloves. The quality of cloves depends on good weather. Unpredictable weather will prolong the clove drying process and make the cloves damaged and moldy. In this study, we use the CNN architecture with several combinations, namely the number of convolutions, the number of dense layers, and the size of the layers. The CNN architecture is trained using several variations of the color space and several variations of image segmentation to classify the clove quality. This study used three color spaces (RGB, HSV, and YCbCr) and two segmentation methods (Otsu segmented and HSV color segmentation). The best accuracy is obtained by using the HSV color space, original dataset, and E-5C-64LS-4D architecture that is 96% accuracy, precision, recall, and F1-Score. To get a better model, we use the proper image segmentation method and composing the suitable color space to improve the CNN architectural performance.

Subjek

Computer vision
 

Katalog

Classification of Dried Clove Flower Quality using Convolutional Neural Network
 
 
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

F.A.CHALIK
Perorangan
Wikky Fawwaz Al Maki
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2021

Koleksi

Kompetensi

 

Download / Flippingbook

 

Ulasan

Belum ada ulasan yang diberikan
anda harus sign-in untuk memberikan ulasan ke katalog ini