Implementation of Fuzzy Inference System for Classification of Dengue Fever on the villages in Malang

Handoyo, Samingun; Kusdarwati, Heni

doi:10.1088/1757-899x/546/5/052026

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…The response variable measurement unit scale has a critical role, namely if the response variable on a numerical scale will lead to regression modeling, whereas if the response variable is on a categorical scale it will lead to a classification model. Modeling the dengue fever status of a village [17] and modeling the baby's weight status at birth [18] are examples of classification modeling. Regression modeling generally aims to determine the magnitude of the influence of the predictor variable on the numerical response variable and also predicts an unknown value of the response variable based on the values of the predictor variables of a certain instance [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Nugroho¹,

Handoyo²,

Hsieh³

et al. 2022

IJACSA

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The multiple regression model is very popular among researchers in both field of social and science because it is easy to interpret and have a well-established theoretical framework. However, the multioutput multiple regression model is actually widely applied in the engineering field because in the industrial world there are many systems with multiple outputs. The ridge regression model and the Multi-Layer Perceptron (MLP) neural network model are representations of the predictive linear regression model and predictive non-linear regression model that are widely applied in the world of practice. This study aims to build multi-output models of a ridge regression model and an MLP neural network whose hyperparameters are determined by a grid search algorithm through the cross-validation method. The hyperparameter that produces the smallest RMSE value in the validation data is chosen as the hyperparameter to train both models on the training data. The hyperparameter in question is a combination of learning algorithms and alpha values (ridge regression), a combination of the number of hidden nodes and gamma values (MLP neural network). In the ridge regression model for alpha in the range between 0.1 and 0.7, the smallest RMSE is obtained for all learning algorithms used. While the MLP neural network model specifically obtained a combination of the number of nodes = 18 and gamma = 0.1 which produces the smallest RMSE. The ridge regression model with selected hyperparameters has better performance (in the RMSE and R2 value) than the MLP neural network model with selected hyperparameters, both on training and testing data.

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Section: Related Workmentioning

confidence: 99%

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Nugroho¹,

Handoyo²,

Hsieh³

et al. 2022

IJACSA

Self Cite

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“…Adding a threshold value to the Bayesian or Naïve Bayes model means adding a constant value to the classification criterion which Eq. ( 5) can be rewritten as the following formula [40].…”

Section: Withmentioning

confidence: 99%

Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold

Widodo¹,

Handoyo²,

Irianto³

et al. 2022

IJETAE

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—Fraud detection is the first step to preventing fraud committed by both individuals and organizations. The development of a high-performance classification model to detect fraud is an interesting topic in machine learning modeling. A finding of the best Bayesian and Naive Bayes classification models is a crucial issue because both models are simple and easily applied models in the fields of life and social sciences. This study aims to obtain the best performance of classification models developed based on probability concepts, namely Bayesian and Naive Bayes models. Adding a threshold value to the decision-making criteria of the two models is an effort expected to can find models that perform superiorly. Data on the auditing of fraudulent firms containing of 775 firms from various business sectors in Australia is used as a case study. The testing data consisting of 100 instances were taken by cluster random sampling with a proportion of 61 non-fraudulent and 39 fraudulent firms and the remaining instances as the training data. The best Bayesian model has an average accuracy of 84% obtained at a threshold value of 0.22. While the best Naive Bayes model has an average accuracy of 94% which is obtained in the 15 threshold values. Adding the threshold value has a significant impact on the performance of the Bayesian model, which can increase the average accuracy from 36% to 84%. On the other hand, the average accuracy of the Naive Bayes model only increased by 1%, from 93% to 94%. Performance measures Sensitivity, Specificity, F1 score, and ROC curve of the Naive Bayes model are also superior to the Bayesian model.

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“…Kusdarwati and Handoyo [5] also implemented the regression technique based on the hybrid Neural Network and wavelet. The classification of villages suffering dengue fewer uses the classification technique based on the fuzzy logic is also done by Handoyo and Kusdarwati [6]. The performance of the regression models based on the fuzzy logic above are very satisfied, but the performances of the classification models based on the fuzzy logic are a reasonable worse which it is not balance on the trade-off between it complicated in computation and it yielded in performance.…”

Section: Introductionmentioning

confidence: 99%

The Varying Threshold Values of Logistic Regression and Linear Discriminant for Classifying Fraudulent Firm

Handoyo¹,

Chen²,

Irianto³

et al. 2021

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Implementation of Fuzzy Inference System for Classification of Dengue Fever on the villages in Malang

Cited by 5 publications

References 5 publications

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold

The Varying Threshold Values of Logistic Regression and Linear Discriminant for Classifying Fraudulent Firm

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