SISTEM INFORMASI GEOGRAFIS PEMETAAN DAERAH POTENSI RAWAN BANJIR DENGAN METODE K-MEANS CLUSTERING (Studi Kasus : Kabupaten Jombang)
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Institusion
Universitas Bhayangkara Surabaya
Author
Kurniawan, Rizky Andrea
Subject
Datestamp
2023-02-28 03:45:50
Abstract :
El Nino and La Nina are one of the extreme climate change phenomena that are influenced by global warming. In the La Nina event, the surface temperature in the western Pacific region warms and causes high rainfall in Southeast Asia and Australia. The high rainfall has resulted in floods in several areas in Indonesia, one of which is in Jombang Regency, East Java. Many cases of flooding that occurred in Jombang Regency occurred due to the lack of river capacity to accommodate the large discharge of rainwater which then overflowed into residential areas. The high number of flood disasters in Jombang Regency requires a geographic information system. Geographic Information System mapping potential flood-prone areas with the K-Means clustering method to solve these problems. By using data processing parameters of soil type, land slope, land use, rainfall to get the results of clustering mapping of flood-prone potential and the results obtained are 11 sub-districts according to a total of 21 sub-districts with an accuracy of 52.38%. With this system, it is hoped that it can help map flood-prone areas so that the risk of losses caused by floods can be minimized.
El Nino and La Nina are one of the extreme climate change phenomena that are influenced by global warming. In the La Nina event, the surface temperature in the western Pacific region warms and causes high rainfall in Southeast Asia and Australia. The high rainfall has resulted in floods in several areas in Indonesia, one of which is in Jombang Regency, East Java. Many cases of flooding that occurred in Jombang Regency occurred due to the lack of river capacity to accommodate the large discharge of rainwater which then overflowed into residential areas. The high number of flood disasters in Jombang Regency requires a geographic information system. Geographic Information System mapping potential flood-prone areas with the K-Means clustering method to solve these problems. By using data processing parameters of soil type, land slope, land use, rainfall to get the results of clustering mapping of flood-prone potential and the results obtained are 11 sub-districts according to a total of 21 sub-districts with an accuracy of 52.38%. With this system, it is hoped that it can help map flood-prone areas so that the risk of losses caused by floods can be minimized.
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