The Effect of Organizational Citizenship Behaviors on Nurses Performance through Nurse's Work Satisfaction in Hospital in Kediri, East Java, Indonesia

Utami, Sasi; Sanusi, Anwar; Respati, Harianto

doi:10.31695/ijasre.2020.33933

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Unsupervised learning includes the method to make instances rank [20] and to make instances groups by minimizing the variance within groups and maximizing the variance between groups [21]. Supervised learning models are also known as predictive models which are regression models if the target feature has a numerical scale [22] and classification models if the target feature has a categorical scale [23]. The decision tree model is built by using the repeating of divide and conquer steps until the stopping criteria are fulfilled.…”

Section: Literature Reviewmentioning

confidence: 99%

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

Mursityo,

Rupiwardani,

Putra

et al. 2024

JAIT

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The input of predictive models has an important role in directing the classification model to have a satisfactory performance in predicting an unknown label of the instance class. The predictor features should be not only relevant to the target feature but also should be independent of each other. The research objective is to obtain the predictor features that are relevant and independent through feature selection using the Chi-square test, one-way Analysis of Variance (ANOVA), and Pearson's correlation test, and to show important features of the decision tree are different from the features of relevant and independent. The evaluation of irrelevant features yields 44 of 67 relevant features which as many as 18 discrete types with two labels are dropped. The dataset with 44 relevant features is used to train the first decision tree. The relevant features mean that the target feature depends on them. The best predictor features should not only be relevant but also independent of each other. The evaluation of independent among features yields 11 of 44 independent features where the features of numeric and discrete with 2 labels are represented by 1 and three features respectively. The dataset with 11 relevant and independent features is used to train the second decision tree. The important features of both models are very different and the second model has better performance than the first one for the metrics of accuracy, recall, and F1-Score.

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Section: Literature Reviewmentioning

confidence: 99%

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

Mursityo,

Rupiwardani,

Putra

et al. 2024

JAIT

View full text Add to dashboard Cite

show abstract

“…Descriptive models do not have a standard evaluation metric so they are impossible to obtain the best one. On the other hand, if a feature target is available, predictive models can be developed whether regression models such as in [22,23] or classification models such as in [24] depending on the scale measure of the target feature. Complex classification models including decision trees and neural networks have proven very satisfactory performance when they were compared with most other machine learning models [25,26].…”

Section: Related Workmentioning

confidence: 99%

The Performance Comparison between C4.5 Tree and One-Dimensional Convolutional Neural Networks (CNN1D) with Tuning Hyperparameters for the Classification of Imbalanced Medical Data

2023

IJIES

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The implementation of a CNN1D model is a big challenge because it involves many hyperparameters which have to be tuned well. The research has goals to compare the classification performance of the C4.5 tree and CNN1D models applied in the imbalanced medical dataset in six performance metrics. The oversampling method is applied to the training dataset to form the oversampling training data which is divided into 5 folds of cross-validation data for tuning hyperparameters of both models. By using the grid search method, there are obtained the optimal CNN1D hyperparameters are [256,4] for the filter and kernel sizes, and the optimal C4.5 hyperparameters are [25,7] for the minimum number of instances in the splitting node and the depth of C4.5 tree. The models with optimal hyperparameters are trained using the oversampling training data and evaluated their performance in both of original training and testing datasets. In the training dataset, the CNN1D outperforms the C4.5 tree in all six metrics with a value of 99% except in the matthews correlation coefficient (MCC) metric with 73% besides the performance gaps of both models being very large. In the testing dataset, the CNN1D also outperforms the C4.5 tree in four metrics with a value of around 99%, but in the MCC and the area under curve (AUC) metrics, the C4.5 tree outperforms the CNN1D although their performance gaps are narrow enough. An interesting future work is to use the MCC or AUC criteria in the tuning hyperparameters of models for the classification of imbalanced class datasets.

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“…Good OCB behavior by members of the organization, in this case, especially nurses, can have a tangible impact on a hospital. (Utami et al, 2020). The success of implementing OCB is influenced by many variables, such as negative feelings towards work, emotional exhaustion, and doubts about work which will lead to a decrease in the success rate of OCB.…”

Section: Ocb Implications For Scientific Development and Hospital Imagementioning

confidence: 99%

Model Hospital Company Image Pride-Based Organizational Behavior

et al. 2023

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analysis using SEM PLS. Results: The results showed that (1) the determinants of OCB were influenced by employee factors, company factors, and customer factors, (2) employee factors had the most influence on OCB which had a positive impact on HCI, (3) OCB was a mediating variable between employee and HCI factors, ( 4) OCB had a strong influence on HCI. Conclusion: The OCBbased HCI model is a model that is influenced by employee factors through good nurse interpersonal communication to improve nurses' OCB behavior through tolerance behavior (sportsmanship) to create a positive hospital image. Plantation hospitals in Indonesia that are members of the Indonesia Healthcare Corporation need to start thinking about efforts to improve employee factors through the competence of interpersonal communication of nurses in providing services to patients.This open access article is under the CC-BY-SA license.

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The Effect of Organizational Citizenship Behaviors on Nurses Performance through Nurse's Work Satisfaction in Hospital in Kediri, East Java, Indonesia

Cited by 4 publications

References 14 publications

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

The Performance Comparison between C4.5 Tree and One-Dimensional Convolutional Neural Networks (CNN1D) with Tuning Hyperparameters for the Classification of Imbalanced Medical Data

Model Hospital Company Image Pride-Based Organizational Behavior

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