Prediction of Protein Content of Shredded Goldfish Based on Physical Characteristics and Processing Process Using Random Forest Regression Method

Abstract

Shredded goldfish is a processed fishery product that has high nutritional value, especially in its protein content. This study aims to predict the protein content in shredded goldfish based on the physical characteristics of the ingredients (moisture, ash, fat, and crude fiber content) and processing parameters (temperature and frying time) using the Random Forest method. The data used consisted of 10 samples of proximate analysis results and were divided into training data (67%) and test data (33%). The model was evaluated using MAE, MSE, RMSE, and R-squared metrics. The evaluation results showed that the model produced an MAE of 0.5649, MSE of 0.5409, RMSE of 0.7354, and R² of 0.0898. The low R² value indicates that the model is still not optimal in explaining variations in the target data. The prediction of protein levels for new data with certain characteristics resulted in a value of 20.16%, which is still within the range of actual values. This research shows the potential of using machine learning methods in predicting the nutritional value of food products, although increased accuracy is still needed through additional data and exploration of other models. It is recommended that the frying temperature is 155°C to 160°C and the frying time is 11 minutes to 13 minutes to maintain optimal protein levels.