CLUSTERING KUNJUNGAN WISATAWAN MANCANEGARA DI INDONESIA DENGAN METODE DBSCAN

AKBAR, MUHAMAD and Miraswan, Kanda Januar and Rodiah, Desty (2024) CLUSTERING KUNJUNGAN WISATAWAN MANCANEGARA DI INDONESIA DENGAN METODE DBSCAN. Undergraduate thesis, Sriwijaya University.

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Abstract

The tourism sector is one of the sectors that affects approximately 4% of the total economy in Indonesia. One of them is the activity of visits from foreign tourists to Indonesia which always fluctuates every year, especially during the Covid-19 pandemic which caused a drastic decline. This research discusses the Clustering of Foreign Tourist Visits in Indonesia with the DBSCAN Method. The data source of this research is collected from data on the number of foreign tourist visits to BPS (Central Bureau of Statistics) from 2018-2023. Data mining with DBSCAN was chosen because of its ability to detect noise and produce clusters without the need to determine the number of clusters at the beginning, and is effective for data with high diversity. This research aims to identify countries with high or low enthusiasm in visiting Indonesia. The clustering results produced two clusters with the first cluster containing 229 countries with low visitation rates and the second cluster containing 2 countries with high visitation rates, and one noise with the smallest DBI evaluation value of 0.478976 as the result of the optimal number of clusters.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, DBSCAN, Clustering, Wisatawan Mancanegara
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5.A142 Computer science. Information society. Information technology. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.76.I58.A3115 Computer science. Computers. Intelligent agents (Computer software) Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhamad Akbar
Date Deposited:	08 Jul 2024 08:24
Last Modified:	08 Jul 2024 08:24
URI:	http://repository.unsri.ac.id/id/eprint/149799

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