FISHERMEN'S EXCHANGE RATE PREDICTION MODEL USING A NOVEL ALGORITHM BASED ON FUZZY TIME SERIES

Chintya Ermas, Darvi Mailisa Putri, Syarto Musthofa

Abstract


The Fishermen's Exchange Rate (NTN) is an important indicator for assessing the welfare of fishing households, so the ability to accurately predict NTN is a strategic necessity in formulating fisheries sector policies. The tendency of FLR fluctuations to be non-linear requires prediction methods that can capture data uncertainty, while conventional approaches are often not sufficiently adaptive. Fuzzy Time Series (FTS) offers a simpler framework through the use of fuzzy sets, but various studies show that its accuracy still needs to be improved. This study develops a novel algorithm based on Fuzzy Time Series to improve the accuracy of FLR predictions. The Novel algorithm was applied to actual data and produced an accuracy evaluation of 6.57 percent MAPE. This indicates good prediction performance and shows that the development of the Novel algorithm can improve the ability of FTS to model NTN dynamics.


Keywords


Novel algorithm; fuzzy time series; fishermen exchange rate

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DOI: https://doi.org/10.15548/map.v7i2.12710

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