Skripsi

ANALISIS MODEL PREDIKSI UNTUK LAYANAN BUS SEKOLAH DI JAKARTA MENGGUNAKAN PENDEKATAN MACHINE LEARNING BERDASARKAN DATABASE OPEN DATA JAKARTA TAHUN 2017-2019

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This research aims to predict the types of school buses in Jakarta using machine learning methods. Data from 2017 to 2019 includes the number of passengers, the number of schools, and bus types. Exploratory data analysis identified patterns and trends, with feature engineering generating three main variables. We tested seven machine learning models, including SVM, Logistic Regression, KNN, Gaussian Naive Bayes, Decision Tree, AdaBoost, and Gradient Boosting, with a focus on f1-score to handle data imbalance. The evaluation shows that gradient boosting has the best performance with the highest accuracy, precision, recall, and f1-score. The results provide insights into the factors that influence school bus types and offer an effective predictive model to support decision-making in school transportation management in Jakarta. Gradient boosting proved to be the most reliable in predicting school bus types. Therefore, this model can serve as a basis for strategies to improve the safety and efficiency of school transportation.

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Detail Information

Series Title

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Call Number

T1505722024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xi, 52 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Prodi Sistem Komputer
Kecerdasan Buatan/Pembelajaran Mesin

Specific Detail Info

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Statement of Responsibility

KA

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