Forecasting passport application demand using the chen average-based FTS method at the Medan immigration office - Laila Agustin Pohan; Aprilia, Rima (Universitas Islam Negeri Raden Intan Lampung, 2025)
The rising demand for passport services in Medan reflects increasing public mobility and highlights the need for accurate forecasting. This study aims to predict the number of passport applications at the Class I Special Immigration Office (TPI) Medan using the Chen Average-Based Fuzzy Time Series method. The research applies a quantitative approach using secondary monthly data from January 2020 to September 2025. The forecasting procedure involves defining the universe of discourse, forming intervals, conducting fuzzification, developing fuzzy logical relationships and groups (FLR/FLRG), and performing defuzzification to produce forecast values. The results indicate that the model effectively captures fluctuations in actual data, achieving a Mean Absolute Percentage Error (MAPE) of 38.61%. These findings classify the model’s accuracy as fairly good for forecasting administrative time series data. Therefore, the Chen Average-Based Fuzzy Time Series method provides a reliable analytical tool for predicting future passport demand and supports improved planning and policy development in immigration services.