KHAIRUNNISYA, KHAIRUNNISYA and Oklilas, Ahmad Fali (2024) PERBANDINGAN 1 DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS (1DCNN) DENGAN DECISION TREE DALAM MENDETEKSI PELANGGARAN LALU LINTAS KENDARAAN KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.
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Abstract
This research aims to compare two methods, namely 1 Dimension Convolutional Neural Networks (1DCNN) and Decision Tree, in detecting traffic violation rates in the city of Palembang. This study also uses You Only Look Once version 8 (YOLOv8) to count the number of vehicles to be detected based on video recordings, resulting in a model and obtaining an F-1 confidence score of 82%. The 1 Dimension Convolutional Neural Networks method achieved a good accuracy rate of 85%. Meanwhile, the Decision Tree method achieved an accuracy rate of 90%, indicating very good performance in detecting violation rates. Therefore, it can be concluded that the results of this study show that YOLO can detect objects with an accuracy of 82%, and Decision Tree performs very well in detecting violation rates with an accuracy of 90%, compared to the 1 Dimension Convolutional Neural Networks (1DCNN) method, which has an accuracy of 85%.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | 1 Dimention Convulutional Neural Networks (1DCNN), YOLOv8, Decision Tree,Pelanggaran lalu lintas. |
Subjects: | T Technology > T Technology (General) > T1-995 Technology (General) > T14 Philosophy. Theory. Classification. Methodology Cf. CB478 Technology and civilization T Technology > T Technology (General) > T1-995 Technology (General) > T180.A1 General works T Technology > T Technology (General) > T1-995 Technology (General) > T205 History and discussions of international agreements ("conventions") resulting from the conferences |
Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
Depositing User: | Khairunnisya Khairunnisya |
Date Deposited: | 10 Jul 2024 06:50 |
Last Modified: | 10 Jul 2024 06:50 |
URI: | http://repository.unsri.ac.id/id/eprint/150045 |
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