NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization

Aji Gautama Putrada; Nur Alamsyah; Muhamad Nurkamal Fauzan; Syafrial Fachri Pane

doi:10.30595/juita.v11i1.15140

Authors

Aji Gautama Putrada Telkom University
Nur Alamsyah Telkom University
Muhamad Nurkamal Fauzan Telkom University
Syafrial Fachri Pane Telkom University

DOI:

https://doi.org/10.30595/juita.v11i1.15140

Keywords:

normalization, standardization, chicken egg, prediction, support vector machine, imbalance dataset

Abstract

Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation withÂ ð‘² = ðŸðŸŽÂ to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namelyÂ ð‘¨ð’„ð’„ð’–ð’“ð’‚ð’„ð’š = ðŸŽ. ðŸ—ðŸ—ðŸ”. At the same time, the model with the lowest performance is SVM with RBF, namelyð‘¨ð’„ð’„ð’–ð’“ð’‚ð’„ð’š = ðŸŽ. ðŸ—ðŸ–ðŸ”. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree isÂ ð‘¨ð‘¼ð‘ª = ðŸŽ.ðŸ—ðŸðŸ•Â toÂ ðŸŽ.ðŸ—ðŸ—ðŸ‘.

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