Predicting Fishermen's Income Based on Weather Factors and Fishing Location Using the Neuro-Fuzzy Algorithm
DOI:
https://doi.org/10.61536/ambidextrous.v5i03.913Keywords:
Adaptive Neuro-Fuzzy Inference System, Fishermen's Income, Machine Learning, Marine Weather, PredictionAbstract
Fishermen's income fluctuates due to weather conditions, fishing location, and vessel characteristics. These conditions necessitate a method capable of predicting income based on various factors that have complex relationships. This study aims to predict fishermen's income using the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. The study uses an applied quantitative approach with computational and experimental methods. The study population consists of data on fishing activities and income of small-scale fishermen in the Belawan area, while the sample consists of data containing information on weather conditions, fishing location, vessel characteristics, and income. The instruments consist of a laptop and Python software along with supporting libraries. Analysis was carried out through preprocessing, training, testing, and evaluation using MAE and RMSE. The results show that ANFIS can produce predictions based on ship load, vessel width, rainfall, and fishing location. In the test example, the prediction of approximately IDR 1,000,000 compared to the actual value of IDR 1,249,572 resulted in an error of IDR 249,572. In conclusion, ANFIS can be used to model the relationship between input variables and fishermen's income and the results can be presented through a dashboard as supporting information before going to sea.
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