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IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK MENDETEKSI KEMACETAN LALU LINTAS SERTA MENENTUKAN JALUR TERBAIK MENGGUNAKAN ALGORITMA HEURISTIC SEARCH PADA JALAN RAYA KOTA PALEMBANG

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This study developed the YOLOv8 model to detect five object classes, with training results showing an accuracy of 95%, an f-1 score of 89%, and mAP@0.5 of 93.1%. In the testing phase, the model achieved an accuracy of 93.83%, an f-1 score of 83%, and mAP@0.5 of 86.7%. Vehicle counting using YOLOv8 and DeepSORT on 72 videos showed an average accuracy of 95.51% for motorcycles and 79.02% for cars. Additionally, a CNN model for classifying road conditions using a dataset of 320 data entries achieved an accuracy of 89.06% on testing data. The Heuristic Search (A-Star) algorithm was used to find the best route from Ampera Bridge to Sultan Mahmud Badaruddin II Airport, generating six alternative routes with predictions for 12 different conditions, where the best route varied depending on the conditions.

Availability
Inventory Code Barcode Call Number Location Status
2407005187T154248T1542482024Central Library (References)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1542482024
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiv, 118 hlm.; ilus.; tab, 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
055.407
Content Type
Text
Media Type
unmediated
Carrier Type
other (computer)
Edition
-
Subject(s)
Sistem Pemrograman
Prodi Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related
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