The rapid growth of motor vehicles in urban areas has led to an increasing demand for parking facilities and requires tariff policies that are more adaptive to real field conditions. This study aims to develop a parking revenue potential classification model to support the formulation of progressive parking tariff policies using a data-driven and spatial analysis approach. The dataset includes vehicle attributes, parking volume, and temporal parking usage patterns. Parking revenue potential is categorized into low, medium, and high classes using a quantile-based approach. Unlike most previous studies that focus on parking occupancy prediction, this study proposes a revenue-oriented spatiotemporal classification model integrating spatial coordinates and temporal parking patterns to support adaptive tariff policy formulation. The Random Forest algorithm is applied to classify parking revenue potential into low, medium, and high categories, achieving an accuracy of 90.24% for two-wheeled vehicles and 89.02% for four-wheeled vehicles. The classification results are integrated into an interactive Streamlit-based dashboard that visualizes the spatial distribution of parking revenue potential and enables simulations of progressive tariff adjustments based on spatial zones and temporal conditions. The proposed system functions as a decision support tool for parking management, aiming to improve operational efficiency and sustainably increase regional parking retribution revenue.

The water distribution sector in Indonesia still faces challenges in detecting leaks early due to manual data checks that are time-consuming and labor-intensive. PDAM (Regional Water Company) Tirta Wijaya Cilacap, Indonesia, faces similar problems. This study aims to implement a spatial customer prediction model to detect customer water usage and support data-driven operational decision-making. K-Means clustering groups customers by consumption patterns and geographic location, achieving a Silhouette Score of 0.4473 and a Davies–Bouldin Index of 0.7658, which indicates reasonably well-separated clusters in real-world data. In addition, water consumption forecasting was carried out with Seasonal–Trend Decomposition using Loess–Long Short-Term Memory (STL–LSTM) to predict trends and seasonality of water usage for each Customer Connection ID (CCID). The forecasting performance varies across CCIDs; the best case achieves an R2 of up to 0.95, while low-performing cases are discussed to clarify conditions where STL–LSTM is less reliable. The forecasting and clustering outputs are presented through a spatial visualization (map) of water-consumption categories and model results to support identifying areas that may require closer inspection for potential leakage and waste. This research contributes to strengthening technology-based public infrastructure, in line with SDG 9: Industry, Innovation, and Infrastructure, to promote sustainable water management.