Prediksi Jumlah Calon Mahasiswa Baru Menggunakan Metode Fuzzy Time Series dan ARIMA: Studi Kasus: Program Studi Statistika - Aprina Manggarai; Lailany Yahya; Agusyarif Rezka Nuha (Asosiasi Riset Ilmu Matematika dan Sains Indonesia, 2025)
Academic planning is one form of planning the teaching and learning process in state universities, aimed at achieving educational goals based on the standards set. One important aspect of academic planning is forecasting the number of new students. This study compares two forecasting methods, Fuzzy Time Series (FTS) and Autoregressive Integrated Moving Average (ARIMA), in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo. Forecasting the number of new students is crucial for determining various policies, such as resource allocation and providing adequate facilities. The results of the study show that the ARIMA method produces more accurate predictions with a Mean Absolute Percentage Error (MAPE) of 0.35%, which is lower than the FTS method. This indicates that ARIMA is more effective in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo and can serve as a reference to improve academic planning quality in higher education institutions.