Prediction of Total Weight of Octopus Cyanea Using Multiple Linear Regression Method
DOI:
https://doi.org/10.23887/janapati.v14i1.83893Keywords:
Fisheries Improvement Program, Octopus Cyanea, Linear RegressionAbstract
Fisheries Improvement Programs (FIPs) rely on data to offer recommendations for sustainable fishing practices. The octopus cyanea FIP in East Nusa Tenggara faces difficulties in data collection, particularly the total weight of the octopus, as the heads are often removed before landing. This is because the head's contents can cause rapid spoilage and blackening due to the ink. However, these contents are also used as bait. Understanding the total weight is crucial for linking it to gonad weight data to determine the octopus's maturity level. In this study, two models were developed to estimate the total weight of an octopus using known data through Multiple Linear Regression. The most accurate model used total length and body weight without the head contents as predictors, with a Mean Absolute Error (MAE) of 27.97 grams, indicating an average error of this amount in the predictions. The model's fit was assessed with an R2-Score of 0. 983, suggesting a strong correlation with the actual data. Additionally, T-test results indicate no significant statistical difference between the predicted and actual weights. This research aims to provide an alternative method for estimating the total weight of octopuses to support the Octopus FIP in Flores, East Nusa Tenggara.
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