Penerapan Algoritma K-Means Clustering untuk Daerah Penyebaran Sampah Kelurahan
- Yantria Gusta Nugraha Universitas Muhammadiyah Magelang, Magelang, Indonesia
-
Maimunah Maimunah
*
Universitas Muhammadiyah Magelang, Magelang,
Indonesia
- Pristi Sukmasetya Universitas Muhammadiyah Magelang, Magelang, Indonesia (*) Corresponding Author
Abstract
Waste in Indonesia, especially in Magelang City, has become a serious problem due to rapid population growth. Waste management issues, including landfills and collection, need effective handling. Data mining methods, such as K-Means clustering, can help identify areas with the highest levels of waste generation. This approach provides insights for the development of a more focused and efficient waste management strategy, a significant contribution to the improvement of Magelang City. By identifying the areas with the highest waste generation, waste management measures can be directed more efficiently and effectively. This includes increasing the transparency, capacity, and role of waste banks, as well as other efforts to reduce the negative impact of waste on the environment and human health. After clustering, the waste in Magelang City was grouped into 3 clusters according to the supplier area and the volume of waste. Then after the evaluation stage with the silhouette score displays a value of 0.79 which is a good value because it is close to the value of 1.0. With this method, it is expected that the city government in handling waste in Magelang city can be done optimally, efficiently, and on target
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