@thesis{thesis,
		author={Setiawati  Mira},
		title ={ANALISIS FINANCIAL DISTRESS DENGAN MENGGUNAKAN
MODEL TAFFLER T-SCORE DAN SPRINGATE S-SCORE PADA PERUSAHAAN YANG TERDAMPAK PANDEMI COVID-19
(Studi Keuangan Subsektor Hotel Restoran Dan Pariwisata Yang Terdaftar Di Bursa Efek Indonesia Tahun 2020)},
		year={2022},
		url={https://eprints.ummi.ac.id/3143/},
		abstract={Conducting an analysis on the condition of the previous companies is very necessary in order to get to know the condition of those if they are in a healthy condition or experiencing financial difficulties. For this reason, an analysis is required for predicting a company's financial difficulties, so that when a company is in a state of financial difficulties, its management can immediately overcome these conditions and avoid bankruptcy. 
The research was aimed determining the prediction of financial distress within companies using the Taffler T-score and Springate S-score models, finding out whether there was a difference between the Taffler T-score model and the Springate S-score model in predicting financial distress, as well as finding out the difference in the accuracy of the prediction models of financial distress for hotel, restaurant and tourism sub-sector companies affected by the COVID-19 pandemic in 2020. 
The object of the research was the Taffler T-score model (X1), the Springate Sscore model (X2) and financial distress (Y). The research method applied was descriptive and comparative with quantitative approach. The techniques of analyzing data deployed were the Paired Sample T-test and the Prediction Model Accuracy Test with data processing using SPSS 25 software.
The results showed that the Taffler model predicted 20 data were in good health, 18 data experienced financial distress and two data were in a vulnerable condition (grey area). The Springate model predicted that as many as five data were declared in good health and 35 data were classified in the category of experiencing financial distress. There was a significant difference between the Taffler T-score and Springate S-score models using the Paired Sample T-test. The most accurate prediction model in predicting financial distress conditions in the hotel, restaurant and tourism sub-sector companies affected by the COVID-19 pandemic was the Taffler T-score model with an accuracy rate of 72.5%.}
		}