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All Journal English Language and Literature UNP Journal of Statistics and Data Science
Indah Lestari
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Mathematics Social Sciences

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AFFIRMING POSITION OF WOMEN IN PATRIARCHY SYSTEM IN LYNN NOTTAGE’S PLAY SCRIPT BY THE WAY, MEET VERA STARK (2013) Indah Lestari; Kurnia Ningsih
English Language and Literature Vol 8, No 3 (2019)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.636 KB) | DOI: 10.24036/ell.v8i3.105780

Abstract

The aim of this analysis is to expose the issue about affirming position of women in patriarchy system which is done by patriarchal in order to subordinates the women. It is also intended to find out how the contribution of dramatic elements (character, plot/conflict, setting and stage direction) in revealing the issue of affirming position of women in patriarchy system. This analysis is done through text and contex based interpretation which is related to the concept of patriarchy by Kamla Bhasin and beauty by Naomi Wolf. The result of this analysis shows how patriarchy system strengthens the position of women as subordinate and less important person in patriarchal society that can be seen from limitation of career and its development as well as physical appearance.
Comparison of the C5.0 Algorithm and the CART Algorithm in Stroke Classification Indah Lestari; Dina Fitria; Syafriandi Syafriandi; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss1/144

Abstract

The C5.0 and CART algorithms are similar in terms of velocity and handling of categorical and numeric type data. However, these two algorithms are differences in terms the CART algorithm is binary and classifies categorical, numerical and continuous response variables resulting in classification and regression decision trees. Meanwhile, the C5.0 algorithm is non-binary and classifies categorical response variables resulting in a classification tree. This research aims to classify the Kaggle’s Stroke Prediction Dataset to find out the variables that most influence the risk of stroke, as well as to compare the results of the classification accuracy of the both algorithms. The results of the study showed that CART algorithm has a higher value of accuracy and precision, but its recall value is lower than C5.0. The accuracy value of each algorithm is 77.9% and 77.5%, presision is 89.5% and 83.2%, recall is 67% and 71.4%. Overrall, it can be concluded that there is no difference in classification between the two algorithm. Beside that, in the CART there were 3 variables that most influence on stroke risk, they are age, BMI, and average blood glucose levels. Meanwhile, in C5.0 only 2 variable that most influence, there are age and average blood glucose levels.
Co-Authors Dina Fitria Kurnia Ningsih Salma, Admi Syafriandi Syafriandi