Implementasi Metode ST-DBSCAN untuk Pengelompokan Pola Penyebaran Petir di Kota Malang

Hanifatul Mufidah, Hisyam Fahmi

Abstract


Lightning is an inescapable natural occurrence in the Earth’s atmosphere. Lightning is extremely harmful since the energy released can reach up to three million volts. Lightning strikes are difficult to forecast in terms of time, position, and intensity, therefore they may result in physical losses as they often result in fatalities. One method that can be used to identify an area and time that is prone to lightning is the clustering technique. The clustering approach utilized in this study is the ST-DBSCAN algorithm (Spatio Temporal-Density Based Spatial Clustering Application with Noise), which groups data based on spatial and temporal aspects. The dataset used in this study is lightning spots in Malang City from 1 January to 31 December 2022, with a total of 16,800 data. The most accurate analysis findings revealed four clusters and 26 contained noise, giving a Silhouette Coefficient value of 0.104 which employs parameters such as spatial distance (Eps1 = 0.2), temporal distance (Eps2 = 7), and minimum parts of spots within the group (MinPts = 7). Lightning strikes in Malang City in 2022 are anticipated to be frequently encountered between January and July in the first cluster, totaling 13.337 spots and the least occurred in August with 110 spots.

Keywords


Petir; Data Mining; Clustering; ST-DBSCAN

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References


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DOI: https://doi.org/10.18860/jrmm.v3i5.27313

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