Distance Metrics Comparison on K-Nearest Neighbor for Landslide Susceptibility Mapping

Landslides are natural disasters that often occur in certain areas in Indonesia, which cause great damage to aspects of human life. To reduce or prevent major losses due to landslide disasters, previous research used the kind of study that is called Landslide Susceptibility Map (LSM). Meanwhile the purpose of this study is to compare 6 distance metrics on K-Nearest Neighbor (KNN) with distance weight. It is to find the optimal distance metric to create a landslide susceptibility map from the data which is located in the Kejajar sub-district, Wonosobo regency, Central Java, Indonesia, with 25 factors that cause landslides. Data processing is carried out using information gain as a feature selection which aims to reduce the factors that can cause landslide. The distance metrics that used in this study are: Euclidean, City Block, Chebyshev, Cosine, Hamming, and Jaccard. Every distance metrics were trained with the dataset and compare each performance model. The result is Chebyshev distance has a better performance than other distance metrics with precision 0.8289, recall 0.9763, f1-score 0.8966, and accuracy 0.9874.