Optimization of Environmentally Based Waste Management Strategy in Indonesia Using Machine Learning

Introduction: Waste management is one of the biggest environmental challenges facing Indonesia today. With a population of over 270 million people spread across 34 provinces, the country produces a significant amount of waste every day. Diversity in population density, economic activity, and urban development across provinces results in variations in waste production patterns and composition. Effective waste management is critical not only for environmental sustainability, but also for public health and economic development. However, the lack of waste management strategies tailored to the unique characteristics of each province often results in inefficient waste handling and disposal. Objectives: To classify Indonesian provinces into different clusters based on waste production, volume, and composition using machine learning algorithms. Analyze the characteristics of each cluster to understand the unique challenges and opportunities in waste management they face. Provide recommendations for waste management policies tailored to the specific needs of each cluster. Methods: The study began with data collection from official sources such as the Ministry of Environment and Forestry or the Central Statistics Agency. After the data was collected, data preprocessing was carried out to clean the data from missing values ​​and outliers, and to normalize the data so that all variables have the same scale. Next, a clustering algorithm such as K-Means was chosen to group provinces based on their waste characteristics. The optimal number of clusters was determined using the Elbow Method Results: The clustering results divided the provinces into several groups. Cluster 1 contains provinces with relatively low to medium daily waste volumes. Cluster 2 includes provinces with medium waste volumes, while Cluster 3 consists of provinces with very high waste production. The majority of provinces are in clusters 1 and 2, indicating that only a few regions have major problems in waste management. Conclusions: This study shows that clustering with K-Means can help understand waste production patterns in various provinces in Indonesia. Provinces with similar waste characteristics are grouped into three main clusters, with the majority in the low to medium waste volume category. It was found that organic waste is more dominant than inorganic waste, especially in areas with high waste production. This shows that waste management strategies based on recycling, composting, and renewable energy can be effective solutions. The results of this clustering can be used as a basis for designing more appropriate waste management policies, both in increasing waste processing capacity and encouraging community participation in reducing waste.