LESTARI, ANGGI (2026) TRAFFIC SIGN RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK METHOD. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
ABSTRACT The high rate of traffic violations in Yogyakarta indicates low driver compliance with traffic signs. To address this issue, this study developed a Convolutional Neural Network (CNN)-based traffic sign recognition system capable of detecting signs in real time. The dataset comprises 1,500 images of four primary sign types: U-turn, no U-turn, right turn, and left turn, obtained through live image capture and Google Maps. The CNN model was trained on Google Colab for 40 epochs, achieving a training accuracy of 97.49% and a validation accuracy of 96.47%. The trained model was deployed in an Android application using TensorFlow Lite, enabling fast, accurate recognition of signs across various lighting conditions. Test results demonstrate the system’s effectiveness and its potential to enhance traffic safety and order in urban areas. Keywords: Application, Mobile, CNN, ANN, Traffic Signs
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) > T201 Patents. Trademarks |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 05 May 2026 02:07 |
| Last Modified: | 05 May 2026 02:07 |
| URI: | http://eprints.uty.ac.id/id/eprint/19761 |
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