Riza, Muhamad Taufiq
(2023)
LIGHTWEIGHT CNN FOR NASKHI AND RIQ’AH KHAT CLASSIFICATION.
S1 Undergraduate
thesis, Universitas Darussalam Gontor.
Abstract
The Arabic script has various types of khat that are complex and different from one
another, thus requiring an appropriate classification to identify the type of khat used. This
study uses the Lightweight Convolutional Neural Network (CNN) classification method to
identify the types of khat Naskhi and Riq’ah in the Arabic script dataset. The evaluation
results show that this classification model has an accuracy of 98.75% on training data and
100% on validation data, with a relatively fast processing time of 2s 375ms/step faster
than the previous study with an accuracy of 91.87% and an average processing time of 3s
465ms/step. so that the model can be implemented properly in systems that require high
data processing speed and also devices that have resource limitations. These results indicate
that the classification model using the Lightweight CNN layer can be used as an effective
alternative in classifying types of Arabic writing, especially in recognizing certain types of
khat such as Naskhi and Riq’ah. Furthermore, this research can be developed using a larger
and more diverse dataset, and evaluated and compared with other classification models to
improve the model’s performance in recognizing more complex types of Arabic writing.
| Item Type: |
Thesis
(
S1 Undergraduate
)
|
| Additional Information: |
Skripsi : Muhamad Taufiq Riza
NIM : 402019611021 |
| Uncontrolled Keywords: |
Lightweight CNN, classification, khat naskhi, khat riq’ah, Arabic script. |
| Subjects: |
Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum
23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum
Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 003 - Sistem-sistem
23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 003 - Sistem-sistem
Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data
23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data |
| Divisions: |
Fakultas Sains dan Teknologi UNIDA Gontor > Teknik Informatika |
| Depositing User: |
44 Muhamad Taufiq Riza
|
| Date Deposited: |
19 Sep 2024 16:39 |
| Last Modified: |
29 Sep 2024 06:12 |
| URI: |
http://repo.unida.gontor.ac.id/id/eprint/3326 |
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