COMPARISON OF DBSCAN AND K-MEANS CLUSTER ANALYSIS WITH PATH-ANOVA IN CLUSTERING WASTE MANAGEMENT BEHAVIOUR PATTERNS

This study aims to compare the effectiveness of DBSCAN and K-Means cluster analysis methods in clustering waste management behaviour patterns in Batu City. The data used is secondary data from previous research with a total of 395 respondents taken using the quota sampling method. DBSCAN classifies data based on density with the main parameters epsilon and MinPts, while K-Means uses the average centroid to determine the cluster. The analysis results show that DBSCAN produces a silhouette index of 0.664, which is higher than K-Means with a value of 0.574. DBSCAN also successfully identified noise as much as 10 data that did not belong to any cluster, while K-Means did not have a similar mechanism. The results of Path-ANOVA show that DBSCAN is the most optimal clustering with a more significant partition difference value. Further tests were conducted to strengthen the validation of clustering results using Path-ANOVA. Both methods produced two main clusters, with the second cluster showing higher quality in terms of maintenance, quality, and ease of use of environmental hygiene facilities. This research emphasises the importance of choosing an appropriate clustering method to ensure optimal clustering results, especially in data with complex characteristics.