Clustering Daerah Penyumbang Sampah Berdasarkan Provinsi di Indonesia Menggunakan Algoritma K-Means - Desi Kristina; Betha Nurina Sari; Iqbal Maulana (Peneliti.net, 2022)
The problem of waste is still one of the big problems that occur in Indonesia today. Along with the development of rapid population growth, it directly contributes negatively which results in wider and denser residential areas as well as a sharp increase in waste production. Many people in Indonesia are not able to keep the environment clean, so the current waste problem cannot be handled properly. The losses obtained from this waste problem must be minimized. The step that can be taken is to determine the area of ​​​​waste contributors in Indonesia as an initial effort in dealing with waste problems in Indonesia. In this study, clustering of waste contributor areas based on provinces in Indonesia was carried out using the K-Means algorithm and mapping was carried out using QGIS. Clusters are divided into 2, namely clusters of areas that contribute to high and low levels of waste. The results of grouping waste contributor areas based on provinces in Indonesia using the K-Means algorithm were found that 30 provinces were low clusters, and 4 provinces were high clusters. The modeling results were evaluated using the Davies Bouldin Index to determine the quality of the cluster. The results of the cluster test obtained an index value of 0.329 for k = 2, this is the cluster with the best DBI value. This study is expected to provide a presentation of clustering data on waste contributor areas based on provinces in Indonesia which can then be used to assist the government in improving the quality of the solid waste management system.