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All Journal Indonesian Journal of Applied Statistics INTEK: Informatika dan Teknologi Informasi Prosiding Konferensi Nasional PKM-CSR Pattimura Proceeding : Conference of Science and Technology Prosiding University Research Colloquium Proceeding of International Conference on Humanity Education and Society
Yuliana Susanti
Universitas Sebelas Maret
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Journal : Indonesian Journal of Applied Statistics

 1 
Pemodelan Indeks Keparahan Kemiskinan di Indonesia Menggunakan Analisis Regresi Robust Melva Hilda Stephanie Situmorang; Yuliana Susanti
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.40838

Abstract

Poverty is one indicator to see the success of development in a country. The Poverty Severity Index can be used as one measure of the magnitude of poverty in an area. In the Poverty Severity Index data in Indonesia, in 2018 there were some outliers, so to analyze it used robust regression. The purpose of this study is to determine the significant factors on the Poverty Severity Index in Indonesia using robust regression with the M-estimation method. The results showed that the Poverty Severity Index model in Indonesia using robust regression was influenced by Gini Ratio, Percentage of Poor Population, and Pure Participation Rate with R-square = 94,8%.Keywords: Poverty Severity Index, robust regression.
Analisis Faktor yang Berpengaruh terhadap Waktu Survival Pasien Penyakit Ginjal Kronis menggunakan Uji Asumsi Proportional Hazard Assyifa Lala Pratiwi Hamid; Sri Subanti; Yuliana Susanti
Indonesian Journal of Applied Statistics Vol 5, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i1.48121

Abstract

Chronic kidney disease is a disease whose risk of death is always increasing. This disease was ranked as the 13th leading cause of death in Indonesia in 2017. One of the successful managements of chronic kidney disease can be seen from the possibility of survival of patients with chronic kidney disease. To identify the probability of survival of an object, survival analysis is used. One method of survival analysis that can be used to determine the survival time of patients with chronic kidney disease is Cox regression. Cox regression must satisfy the proportional hazard assumption, where the ratio of the two hazard values must be constant with time. The graphical method, namely the log-log graph, can be used to test the proportional hazard assumption, but the results are only used as a provisional estimate. In this study, the goodness of fit test was used to test the assumptions by calculating the correlation between the Schoenfeld residuals and the survival time rank. In conclusion, the variables of hypertension and haemodialysis frequency meet the proportional hazard assumption.Keywords: chronic kidney disease; Cox regression; goodness of fit; log-log graph; proportional hazard assumption
Co-Authors Assyifa Lala Pratiwi Hamid Handajani Handajani Hasih Pratiwi Irwan Susanto Kiki Ferawati Kristoforus Exelsis Pratama Melva Hilda Stephanie Situmorang Muhammad Bayu Nirwana Niswatul Qona’ah Putri Ayu Pramesti Safira Callisa Sarah Hilyatul Hilwy Setyowati Nurhaningsih Sri Subanti Sri Sulistijowati Handajani