ANALISIS HUBUNGAN JUMLAH PENUMPANG DAN BUS SEKOLAH DI KOTA JAKARTA PADA TAHUN 2017 HINGGA 2019 MENGGUNAKAN PENDEKATAN UNSUPERVISED LEARNING

SINAGA, AURELL OCTAVIONA and Passarella, Rossi (2025) ANALISIS HUBUNGAN JUMLAH PENUMPANG DAN BUS SEKOLAH DI KOTA JAKARTA PADA TAHUN 2017 HINGGA 2019 MENGGUNAKAN PENDEKATAN UNSUPERVISED LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

This study aims to identify patterns in the relationship between the number of school buses and the number of school passengers in Jakarta during the 2017–2019 period using an unsupervised learning approach. Four clustering algorithms K-Means, Fuzzy C-Means, Gaussian Mixture Model, and Spectral Clustering were applied to group the data, all of which produced two optimal clusters. Among these algorithms, K-Means outperformed the others based on evaluation metrics and was selected as the primary method for analyzing the clustering results. The first cluster represents a high ratio of buses to passengers, while the second shows a lower ratio. These patterns serve as a foundation for more efficient school transportation planning. The clustering results were then used as labels to train a Decision Tree Classifier model, which was applied to data from 2021. The model successfully classified the new data into the two clusters consistent with the previously identified patterns.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Unsupervised Learning, Clustering, K-Means, Fuzzy C-Means, Gaussian Mixture Model, Spectral Clustering
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science T Technology > TJ Mechanical engineering and machinery > TJ1125-1345 Machine shops and machine shop practice > TJ1180 Machining, Ceramic materials--Machining-Strength of materials-Machine tools-Design and construction > TJ1180.I34 Machining-Machine tools-Numerical control-Computer integrated manufacturing systems-Artificial intelligence
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Aurell Octaviona Sinaga
Date Deposited:	13 Jun 2025 05:41
Last Modified:	13 Jun 2025 05:41
URI:	http://repository.unsri.ac.id/id/eprint/175441

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