Forecasting building permit submissions with fuzzy time series at DPMPTSP Medan - Dasopang, Buyung Satrio; Kurniawan, Rakhmat (Institute of Computer Science (IOCS), 2025)
Public service is a vital part of government performance, including how the Investment and One-Stop Integrated Services Agency (DPMPTSP) handles building permit applications (IMB). This study aims to estimate the number of IMB applications in Medan City using a method called Fuzzy Time Series (FTS). The forecast is intended as a preliminary step to support better spatial planning, especially as urban building density continues to rise. The FTS method was chosen for its ability to process time series data containing uncertainty. The forecasting process involves several stages: identifying the dataset, setting interval ranges, performing fuzzification, forming fuzzy logical relationships (FLR), grouping fuzzy logical relationship groups (FLRG), applying defuzzification, and measuring accuracy using Mean Absolute Percentage Error (MAPE). The data used include IMB applications from 2022 to 2023, with predictions made for 12 months in 2024. The results show that the FTS model closely follows historical data patterns, evidenced by a MAPE value of 1.99%, which indicates excellent accuracy as it is well below the 10% threshold. A comparative graph between actual and predicted data further supports this, revealing similar trends. In conclusion, the Fuzzy Time Series method is effective for forecasting IMB application volumes and can serve as a valuable reference for urban planning decisions and future time series-based forecasting research involving uncertainty.