PENGELOMPOKAN DAERAH BERDASARKAN FAKTOR DAMPAK BENCANA TANAH LONGSOR MENGGUNAKAN ALGORITMA K-MEDOIDS CLUSTERING REGIONS BASED ON LANDSLIDE IMPACT FACTORS USING THE K-MEDOIDS ALGORITHM
Keywords:
K-Medoids, Clustering, Davies Bouldin, RapidminerAbstract
Natural disasters have the potential to damage the environment, harm property and cause loss of life. Landslide disaster mitigation is still not optimal in Balikpapan. Therefore, it is necessary to carry out research to produce a grouping of the impact levels of landslide disasters in 34 sub-districts in Balikapapan. Data processing uses the K-Medoids algorithm with the Rapid Miner tool. In regional grouping, testing is carried out from the number of clusters = 2 to clusters = 5. Then analysis is carried out using the Davies Bouldin Index (DBI) value to find out the best grouping results. From the results of the analysis, it was found that the best DBI value was the grouping of affected areas at K=5, namely the very low impact category with 10 subdistricts (Cluster 4), the low impact category with 7 subdistricts (Cluster 0), the moderately impacted category with 9 subdistricts (Cluster 2). , the highly impacted category is 7 sub-districts (Cluster 3), the very highly impacted category is 1 sub-district (Cluster 1), namely the Sepinggan sub-district. with a DBI value of 0.032.
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Indonesian Society of Applied Science (ISAS)
