PERBANDINGAN MODEL REGRESI LOGISTIK BINER DAN METODE CLASSIFICATION AND REGRESSION TREES (CART) (STUDI KASUS: STATUS PENINGKATAN PENDAPATAN USAHA ONLINE PADA TAHUN 2020 TERHADAP TAHUN 2019 DI DIY)

Faiqotul Muna, NIM.: 21106010040 (2025) PERBANDINGAN MODEL REGRESI LOGISTIK BINER DAN METODE CLASSIFICATION AND REGRESSION TREES (CART) (STUDI KASUS: STATUS PENINGKATAN PENDAPATAN USAHA ONLINE PADA TAHUN 2020 TERHADAP TAHUN 2019 DI DIY). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Binary logistic regression is an analytical technique to see the relationship between predictor variables and response variables. Meanwhile, Classification and Regression Trees (CART) is a flexible decision tree method, capable of handling non-linear relationships and interactions between variables automatically. In 2020, there has been a COVID-19 pandemic that has an impact on the global economy, including MSMEs in Indonesia, where some MSMEs have succeeded in increasing revenue by utilizing digital platforms. This study compares the performance of binary logistic regression and Classification and Regression Trees (CART) in classifying factors that influence the increase in online business income using SUSENAS data from BPS in 2021. The research method used is quantitative analysis with a statistical approach. The results show that binary logistic regression has a classification accuracy of 76.29% with an APER of 23.71%, while the Classification and Regression Trees (CART) have a classification accuracy of 72.16% with an APER of 27.84%. In conclusion, binary logistic regression shows better results in classifying factors that increase online business income, especially if the relationship pattern in the data is relatively linear and the model assumptions are met.

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