Skripsi

PERBANDINGAN METODE RANDOM FOREST DIOPTIMASI DENGAN GENETIK ALGORITMA DAN METODE RANDOM FOREST DIOPTIMASI DENGAN GRID SEARCH PADA SMART TRANSPORTASI SMART CITY UNTUK MENENTUKAN JALUR TERBAIK

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Penilaian

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Technological advancements provide many capabilities to minimize traffic congestion, allow drivers to find the most efficient route and eliminate congestion on the road. Smart transportation in smart cities is an important milestone in technological advancement. To support smart transportation, it is necessary to utilize machine learning to determine the best route. This research focuses on utilizing CCTV video data trained with YOLO, processed with random forest, and optimized with genetic algorithm and grid search. The best first search algorithm is used to determine the best route based on the optimization results of the genetic algorithm and grid search. This research resulted in two types of accuracy: model accuracy and reading accuracy. The random forest method achieved a model accuracy of 84.38% and a reading accuracy of 92.85%. After optimization with genetic algorithm, the model accuracy increased to 91% and the reading accuracy reached 94.28%. On the other hand, optimization with grid search resulted in model accuracy of 76% and reading accuracy of 94.28%. Based on the research results, the best results were obtained by performing random forest optimization using a genetic algorithm, with an increase in model accuracy from 84.38% to 91%. The best route chosen is Route 5 with a weight of 19.8, including Parameswara Musi II, Parameswara Angkatan 45, Kampus Angkatan 45, Taman Siswa Dolog, Dolog Pasar Kuto, Boom Baru from Pasar Kuto, and Boom Baru Port.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307005477	T124713	T1247132023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1247132023

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvii, 150 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.11 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrograman khusus
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

ANUG

Other version/related

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