Text

PENGEMBANGAN GAME DOODLE DENGAN PENILAIAN GAMBAR OTOMATIS MENGGUNAKAN MODEL CNN

---

Penilaian

---

In the era of artificial intelligence (AI) development, the integration of machine learning technology in games continues to advance. This study aims to develop a Convolutional Neural Network (CNN) model that functions as an automatic evaluation agent for doodle images in a drawing game. The CNN model is trained using the Google Quick Draw dataset to recognize images created by players. Evaluation results show that the developed CNN model with 331 labels achieves an accuracy of 0.83. Additionally, other evaluation metrics indicate precision between 0.6 to 0.96, recall between 0.51 and 0.98, and an F1-score between 0.56 and 0.96. The model's average prediction time is recorded at 0.002836 seconds, allowing for instant evaluation. The developed CNN model effectively provides feedback to players as long as the input images do not differ significantly from the shapes in the dataset.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002783	T173350	T1733502025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1733502025

Publisher

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

Collation

xv, 96 hlm.; ill.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

EM

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment