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All Journal Indonesian Journal of Statistics and Its Applications Xplore: Journal of Statistics
Saskya Mary Soemartojo
Department of Mathematics, Universitas Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Other

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Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation Titin Siswantining; Kathan Gerry Vivaldi; Devvi Sarwinda; Saskya Mary Soemartojo; Ika Mattasari; Herley Al-Ash
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p1-12

Abstract

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.
Pengaruh Karakteristik Pasien 4 Diagnosis Penyakit Rawat Inap dengan Biaya Tertinggi di PT Asuransi ABC Terhadap Biaya Rawat Inap Berdasarkan Data Klaim Saskya Mary Soemartojo; Titin Siswantining; Darayani Putri; Mariam Rahmania
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (739.483 KB) | DOI: 10.29244/xplore.v10i1.740

Abstract

PT Asuransi ABC in collaboration with 68 companies, consists of 34960 participants, of which there are 1731 participants who filed claims. This study uses secondary data period July 1, 2013 - 30 September 2014. This study focused on inpatient claims, where there are 4 burdensome disease diagnosis PT Asuransi ABC at a high cost, those are coronary atrial diseases, chronic renal failure, typhoid fever, dengue haemorrhagic fever. Multiple correspondence analysis method is used to find the characteristics of each patient's disease diagnosis as well as the tendency of the characteristics of the patients in the cost of hospitalization . From the research, there are differences in patient characteristics between the disease and also the trend in the cost of hospitalization . Furthermore, the multiple linear regression analysis of patient characteristics influence on the cost of hospitalization . From the results of research only typhoid disease hospitalization costs are influenced by patient characteristics .
Co-Authors Darayani Putri Devvi Sarwinda Herley Al-Ash Ika Mattasari Kathan Gerry Vivaldi Mariam Rahmania Titin Siswantining