PERBANDINGAN 1 DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS (1DCNN) DENGAN DECISION TREE DALAM MENDETEKSI PELANGGARAN LALU LINTAS KENDARAAN KOTA PALEMBANG

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)
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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