PERBANDINGAN ANALISIS KORELASI KANONIK ANTARA METODE KLASIK DAN ROBUST (ESTIMASI M) (STUDI KASUS : PENGARUH HUBUNGAN PERUBAHAN IKLIM DENGAN PENYAKIT MALARIA DAN DBD DI INDONESIA TAHUN 2020)

Frima Aji Umargani, NIM.: 18106010014 (2022) PERBANDINGAN ANALISIS KORELASI KANONIK ANTARA METODE KLASIK DAN ROBUST (ESTIMASI M) (STUDI KASUS : PENGARUH HUBUNGAN PERUBAHAN IKLIM DENGAN PENYAKIT MALARIA DAN DBD DI INDONESIA TAHUN 2020). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Canonical Correlation Analysis is a method used to identify and measure linear relationships, which involve several dependent and independent variables. Classical canonical correlation analysis using the variance covariance matrix does not work well and will make the analysis of a series of data biased, or not reflect the actual phenomenon due to data containing outliers. In this study, we will compare the canonical correlation analysis between the classical and robust methods (Estimation M) in the case of the effect of the relationship between climate change and the emergence of Malaria and DHF in Indonesia in 2020. Estimation M estimator can better replace the covariance variance matrix from the classical method even though there are outliers and will result in a more effective analysis. The canonical correlation analysis method between classical and robust methods (Estimation M) in this study was applied to the effect of the relationship between climate change and the emergence of malaria and dengue in Indonesia in 2020. Based on the results of the robust canonical correlation analysis, it can be interpreted further because it can find a significant function while classical canonical correlation could not find a significant function. From the results of the robust canonical correlation analysis, there are two significant functions but only one function that can be interpreted further, namely 1 = 0.686886 where the function is optimal. The results of the analysis show that the relationship that occurs is the high air temperature, and rainfall, affects the high incidence of Malaria.

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