Peningkatan Ketepatan Klasifikasi  dengan Metode Bootstrap Aggregating pada Regresi Logistik Ordinal

I Ketut Putu Suniantara; I Gede Eka Wiantara Putra; Gede Suwardika

doi:10.29407/intensif.v3i1.12587

Peningkatan Ketepatan Klasifikasi dengan Metode Bootstrap Aggregating pada Regresi Logistik Ordinal

I Ketut Putu Suniantara STMIK STIKOM Bali http://orcid.org/0000-0002-7003-2126
I Gede Eka Wiantara Putra STMIK STIKOM Bali http://orcid.org/0000-0003-4697-2131
Gede Suwardika Universitas Terbuka http://orcid.org/0000-0002-2307-5182

https://doi.org/10.29407/intensif.v3i1.12587

Abstract views: 810 ,

PDF downloads: 804

Keywords: baby's birth weight, ordinal logistic regression, bagging

Abstract

Baby's birth weight is influenced by characteristics of pregnant women such as age, parity, education level, pregnancy visit, and gestational age. Classification of the birth weight of a baby is grouped into several groups, namely low birth weight babies, normal baby weight and excess baby weight. The classification method with ordinal logistic regression provides an unstable parameter estimation, which means that if there is a change in the data set causes a significant change in the model. So that to obtain a stable parameter estimation in the ordinal logistic regression model is used aggregating (bagging) bootstrap approach. This study aims to improve the classification of ordinal logistic regression by using bagging on a baby's birth weight. The classification results with bagging ordinal logistic regression were able to reduce classification errors by 20.237% with 76.67% classification accuracy

Downloads

Download data is not yet available.

References

D. Kesehatan, “Profil kesehatan,” Profil Kesehat. Kab. Karangasem, pp. 38–74, 2016.

T. Hollingworth, Differential Diagnosis in Obstetrics and Gynecology. Great Britain: Edward Arnold, 2008.

I. A. Adjei and R. Karim, “An Application of Bootstrapping in Logistic Regression Model,” OALib, vol. 03, no. 09, pp. 1–9, 2016.

C. D. Sutton, “Classification and Regression Trees, Bagging, and Boosting,” in Handbook of Statistics, vol. 24, no. 04, 2005, pp. 303–329.

L. Breiman, “Bagging predictors,” in Machine Learning, vol. 24, no. 2, Boston: Kluwer Academic Publishers, 1996, pp. 123–140.

I. K. P. Suniantara, “Penerapan Metode Regresi Berstruktur Pohon Dalam Memprediksi Berat Badan Bayi Lahir, Studi Kasus: Rumah Sakit Umum Daerah Wangaya,” Jurusan Matematika, Universitas Udayana, 2008.

I. Fitrah, S. AF, and T. LP, “Metode Bootstrap Aggregating Regresi Logistik untuk Peningkatan Ketepatan Klasifikasi Regresi Logistik Ordinal (Studi Kasus : Stadium Kanker Serviks di RS. Wahidin Sudirohusodo, 2010),” J. Stat. UNHAS, vol. 0, no. 0, pp. 1–9, 2015.

M. S. Akbar, A. Mukarromah, and L. Paramita, “Klasifikasi Status Gizi Balita Dengan Bagging Regresi Logistik Ordinal (Studi Kasus: Survey Kekurangan Energi Protein Kabupaten Nganjuk),” Media Stat., vol. 3, no. 2, pp. 103–114, 2010.

P. P. Sari, M. Susilawati, and I. G. A. M. Srinadi, “Bootstrap Aggregating ( Bagging ) Regresi Logistik Ordinal Untuk Mengklasifikasikan Status Gizi Balita,” vol. 5, no. 3, pp. 103–110, 2016.

P. Juwita, Sugiman, and P. Hendikawati, “Ketepatan Klasifikasi Metode Regresi Logistik dan CHAID dengan Pembobotan Sampel,” in PRISMA, Prosiding Seminar Nasional Matematika, 2018, vol. 1, no. 1, pp. 684–695.

J. Friedman, T. Hasti, and R. Tibshirani, “Additive Logistic Regression: A Statistical View Of Boosting,” Ann. Stat., vol. 28, no. 2, pp. 337–407, 2000.

D. W. Hosmer and S. Lemeshow, Applied Logistic Regression. New York: John Wiley & Sons, Inc, 2000.

R. A. Johnson and D. . Winchern, Applied Multivariate Statistical Analysis. USA: Prentice Hall. Inc, 2007.

PlumX Metrics

Published

2019-02-01

How to Cite

[1]

I. K. P. Suniantara, I. G. E. W. Putra, and G. Suwardika, “Peningkatan Ketepatan Klasifikasi dengan Metode Bootstrap Aggregating pada Regresi Logistik Ordinal”, intensif, vol. 3, no. 1, pp. 32-42, Feb. 2019.

Download Citation

Issue

Vol. 3 No. 1 (2019): Vol. 3 No. 1 Februari 2019

Section

Article

Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License