Skripsi

KLASIFIKASI JENIS BUNGA MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

---

ATechnological advances drive the need for accurate automatic classification systems, such as in flower type identification, which is still often done manually. This study aims to measure the performance of the DenseNet201 architecture in classifying five types of flowers (Daisy, Dandelion, Rose, Sunflower, and Tulip) using a Convolutional Neural Network (CNN). The model was developed with a fully connected layer configuration of 512, 128, and 256 units, and a learning rate of 0.001. The training process was carried out for 50 epochs. The test results showed a training accuracy between 98.24% and 99.44%, while the validation accuracy reached 99.63% in the last five epochs. The low loss values on the training and validation data indicate that the model has good generalization capabilities and does not experience significant overfitting. In conclusion, DenseNet201 has proven to be very effective for flower image classification and has the potential to be applied to various other image recognition applications.

---

Availability

Detail Information

Series Title

-

Call Number

T1893802025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 102 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

006.307

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Teknik Informatika
Kecerdasan Buatan, Convolutional Neural Network

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment