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Pengelompokkan Daerah Rawan Bencana di Indonesia Menggunakan Metode Clustering K-Means

Year: 2024

Type: article

Abstract: Indonesia is a country with a high level of disaster vulnerability, influenced by tectonic activity and its tropical climate. This study uses the K-Means clustering method to identify and group disaster-prone areas based on the level of vulnerability. The data used included average temperature (Tavg) and rainfall (RR) which were processed using Python. The analysis process includes data collection, pre-processing, determination of key features, and evaluation of clustering quality using the Elbow and Silhouette Score methods. The results of the grouping show two main patterns, namely flood-prone areas and drought-prone areas. These findings are expected to support the government in more effective and data-based disaster mitigation planning.

Source: Jupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika

DOI: 10.61132/jupiter.v3i1.644

Author: Seprina Aulia Putri

Institution: Universitas Asahan

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Climate action

Open Access status: hybrid

OpenAlex ID: W4405296532

FWCI:

Cites: 11

Cited by:

Related to: 10