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All Journal Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Informatika Dan Tekonologi Komputer
Agus Rahmad Hidayah
Universitas indo global mandiri
Computer Science & IT Control & Systems Engineering Education Other

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CLUSTERING K-MEANS UNTUK ANALISIS POLA PERSEBARAN BENCANA ALAM DI INDONESIA M Aditya Yoga Pratama; Agus Rahmad Hidayah; Tertia Avini
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 3 No. 2 (2023): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v3i2.1506

Abstract

Data clustering plays a crucial role in data analysis for identifying hidden patterns, trends, and structures within the data. The K-Means algorithm has gained popularity as a widely used method for data clustering due to its efficiency and ease of implementation. Clustering is a data analysis technique utilized to group similar objects together. The K-Means algorithm stands out as one of the most renowned and frequently employed clustering methods across various fields, including data science, pattern recognition, and artificial intelligence. In this research, we collected data on natural disasters from different regions in Indonesia and employed it as input for the K-Means clustering algorithm. K-Means was utilized to cluster the similarity patterns within the occurring natural disasters. The clustering results provide information about groups that may exhibit similar characteristics and disaster risks.
CLUSTERING K-MEANS UNTUK ANALISIS POLA PERSEBARAN BENCANA ALAM DI INDONESIA M Aditya Yoga Pratama; Agus Rahmad Hidayah; Tertia Avini
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 3 No. 2 (2023): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v3i2.1506

Abstract

Data clustering plays a crucial role in data analysis for identifying hidden patterns, trends, and structures within the data. The K-Means algorithm has gained popularity as a widely used method for data clustering due to its efficiency and ease of implementation. Clustering is a data analysis technique utilized to group similar objects together. The K-Means algorithm stands out as one of the most renowned and frequently employed clustering methods across various fields, including data science, pattern recognition, and artificial intelligence. In this research, we collected data on natural disasters from different regions in Indonesia and employed it as input for the K-Means clustering algorithm. K-Means was utilized to cluster the similarity patterns within the occurring natural disasters. The clustering results provide information about groups that may exhibit similar characteristics and disaster risks.
Co-Authors M Aditya Yoga Pratama Tertia Avini