Penerapan Model Machine Learning Untuk Prediksi Kekuatan Beton
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Abstract
Developing a concrete strength prediction model using three machine learning approaches, namely Linear Regression, Neural Network, and Multi-Layer Perceptron (MLP). The three models were tested using a dataset containing information about the composition of concrete materials and concrete strength test results. Evaluation is carried out using Precision, Recall, F1-Score, Mean Squared Error (MSE), and R-squared metrics to measure the accuracy of model predictions. The research results show that the MLP model provides the best performance, with very high Precision, Recall and F1-Score, as well as low MSE and R-squared reaching 0.97. Compared with Neural Network and Linear Regression, the MLP model shows better generalization ability on test data. Although other models also gave good results, MLP proved to be more effective in predicting concrete strength with higher accuracy. This research indicates that the use of MLP in concrete strength prediction can increase accuracy and efficiency in construction applications.
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