Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan

Sidik, Diky Djafar; Sen, Tjong Wan

doi:10.33021/itfs.v4i1.1180

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…Stacking or Stacked Generalization is one of the ensemble techniques where the output results from the first-level model set will be used as input to the secondlevel model [12]. Stacking differs from other ensemble techniques such as Bagging and Boosting.…”

Section: Stacking Methodsmentioning

confidence: 99%

“…Stacking approach can also overcome the imbalance in the data set used [16]. There are two stages of Stacking learning, namely each model will be trained using the same dataset to produce a base classifier for each model, this first stage is called a base learner or level 0 [12]. Base classifier or output obtained in stage one is used as input to create a new dataset in determining the predictions from the test data and providing the result.…”

Section: Stacking Methodsmentioning

confidence: 99%

“…Base classifier or output obtained in stage one is used as input to create a new dataset in determining the predictions from the test data and providing the result. This second stage is known as meta-learners or level 1 [12].…”

Section: Stacking Methodsmentioning

confidence: 99%

“…Accuracy is the ability of the model to predict the correct class, the accuracy formula is computable using an equation (11). Precision is the ratio of the class of rainfall that is predicted correctly, and the precision formulation can be calculated using equation (12). Recall is the ability of the model to predict the probability that a positive class will become positive, and the recall formula can be calculated using equation (13).…”

Section: Model Evaluationmentioning

confidence: 99%

“…However, not many studies have discussed the combination of classifications used in the case of rainfall. D. Sidik and T. sen in research [12] has proposed a stacking method on rainfall data by combining two machine learning methods, namely Naive Bayes and C4. Research [13] and [14] proposed a combination stacking method used in the medical field.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification.

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Section: Stacking Methodsmentioning

confidence: 99%

Section: Stacking Methodsmentioning

confidence: 99%

Section: Stacking Methodsmentioning

confidence: 99%