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

Abstract

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

Optimasi Algoritma K-Nearest Neighbors Berdasarkan Perbandingan Analisis Outlier (Berbasis Jarak, Kepadatan, LOF)

“…In terms of classification, outliers are commonly seen as unimportant features, absent data points, or instances that are redundant or inconsistent [6]. Outliers can have adverse impacts on the outcomes of data analysis, thus necessitating special attention [7]. Even though outliers exhibit distinct behavior compared to the majority of data and are frequently seen as noise, they frequently carry valuable information [8].…”