Analisis Metode DBSCAN (Density-Based Spatial Clustering of Application with Noise) dalam Mendeteksi Data Outlier

 (*)Dedy Armiady Mail (Universitas Almuslim, Bireuen, Indonesia)

(*) Corresponding Author


Data outlier is data that is different from a group of data in a dataset. Data outlier will have an impact on the refraction of data analysis results, if not handled properly. Various approaches can be taken to detect data outlier, one of which is through the clustering method (grouping data). DBSCAN (Density-Based Spatial Clustering of Application with Noise) is a clustering method that is able to find data outlier in a data set. DBSCAN works by determining clusters based on data density, using the parameters epsilon (range) and MinPts (minimum points to form a cluster). This study aims to test several DBSCAN models that have different epsilon and MinPts parameters. The model used consists of 3 models, with details: Model 1 (eps=0,2, MinPts=5), Model 2 (eps=0,3, MinPts=5) and Model 3 (eps=0,4, MinPts =5). The dataset used is a dataset generated through the paint data feature on the Orange Data Mining tool, with 2 variables (x and y), with a total of 1051 data lines of records. The results obtained are that all the tested models found that there is 1 data point that is considered an outlier, namely the data is worth x = 0.370007 and y = 0.410475. In addition, from this research, it can also be concluded that the epsilon value affects the number of clusters formed. The higher the epsilo value, the smaller the number of clusters that may be formed


Data Outlier; DBSCAN; Data Mining; Epsilon; Clustering; MinPts

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