Peningkatan Ketepatan Klasifikasi dengan Metode Bootstrap Aggregating pada Regresi Logistik Ordinal
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
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.
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