Model Adaptive Fuzzy Time Series to Forecasting Enrollments of New Student

DOI

10.2991/ahe.k.210205.068How to use a DOI?

Keywords

Forecasting, Adaptive FTS, Interval, Twice-divided, weighting, MAPE

Abstract

The estimated enrollments of new student is required in the academic planning of a higher education institution. That can be done by forecasting using the fuzzy time series (FTS) technique. FTS method is an artificial intelligence computation technique that can capture patterns from previous data to predict future event. The implementation of this method is easier to used. In this study, the Adaptive model to FTS is applied to forecast new student, where the interval division is done twice (twice-divided) and weighting is carried out for the prediction process. The prediction results obtained a deviation value (error) of 11.66% which is measured using MAPE. These results indicate that this model can be used for long-term predictions even with a limited sample of data.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Ica Admirani
AU  - Ikhthison Mekongga
AU  - Isnaini Azro
AU  - Hidayati Ami
AU  - Rian Rahmanda Putra
PY  - 2021
DA  - 2021/02/09
TI  - Model Adaptive Fuzzy Time Series to Forecasting Enrollments of New Student
BT  - Proceedings of the 4th Forum in Research, Science, and Technology (FIRST-T1-T2-2020)
PB  - Atlantis Press
SP  - 406
EP  - 410
SN  - 2589-4943
UR  - https://doi.org/10.2991/ahe.k.210205.068
DO  - 10.2991/ahe.k.210205.068
ID  - Admirani2021
ER  -