Enhancing public service quality in border regions through fuzzy time series forecasting: A case study of the Timor Tengah Utara regional library - Humoen, Oktovianus; Binsasi, Eva; Mada, Grandianus Seda; Blegur, Fried Markus Allung; Bano, Elinora Naikteas (Desimal: Jurnal Matematika, 2025)
Forecasting the demand for public services is often overlooked in border regions, where data-driven management remains limited. However, accurate forecasts are essential for improving service quality and optimizing the use of public facilities. This study aims to predict the number of university student visits to the Regional Library of Timor Tengah Utara (TTU) Regency, an Indonesian border area, using Chen’s fuzzy time series (FTS) model. The dataset consists of monthly records of university student visits from April 2022 to September 2025. The forecasting process involves fuzzification, the establishment of fuzzy logical relationships, and defuzzification to obtain predicted values. The results show that the number of student visits decreased from 240 in April 2022 to 213 in October 2025. The model achieved a Mean Absolute Percentage Error (MAPE) of 35.15%, indicating a fairly good forecasting accuracy. This study extends the application of Chen’s FTS model to library management forecasting in developing and border regions. In the long term, improved forecasting and service planning are expected to enhance library management efficiency and encourage greater student interest in visiting and reading at regional libraries.