Study this aim for know Influence Proof Physical, Reliability, Power Response, Guarantee and Empathy in a manner Partial and simultaneous To Satisfaction passenger’s camp. Sangke Palangga in the Harbour Crossing Bira - Jampea -Labuan Bajo PT. ASDP Indonesia Ferry (Persero) Branch Selayar on Period Covid-19 pandemic. Type study is Quantitative. Study this use Approach descriptive. Population study this is whole passenger’s camp. Sangke Palangga in the harbour crossing Bira-Jampea-Labuan Bajo PT. ASDP Indonesia Ferry (Persero) branch Selayar a total of 14,736 people were taken from 2020 to with 2021 on period the covid-19 pandemic. And samples on study this is 100 people. Technique data collection carried out with share questionnaire. The technique of data analysis used is analysis multiple linear regression with use Statistical Product Service Solution (SPSS) version 13. The results of study this show that variable proof physical there is influence positive and significant in a manner Partial to satisfaction passenger. Variable reliability there is influence positive and significant in a manner Partial to satisfaction passenger. Variable power responsive there is influence positive and significant in a manner Partial to satisfaction passenger. Variable guarantee there is influence positive and significant in a manner Partial to satisfaction passenger. Variable empathy there is influence positive and s significant in a manner Partial to satisfaction passenger. Variable proof physical, reliability, power response, guarantee and empathy there is influence positive and significant in a manner simultaneous to satisfaction passenger.

The purpose of this study was to determine the level of dividend policy analysis proxied by the Dividend Payout Ratio (DPR) and debt policy proxied by the Debt To Equity Ratio (DER). This study uses a sample of manufacturing companies in the basic and chemical industry sectors that are listed on the Indonesia Stock Exchange (IDX) for 2020-2021. The type of data used in this research is secondary data. Data analysis was performed by analyzing financial ratios, namely the Dividend Payout Ratio and the Debt To Equity Ratio. The results of this study indicate that: 1). Dividend policy in review from the ratio of market prospects as measured by the DPR's Dividend Payout Ratio, it can be seen that the company's ability to pay dividends is stable. Meanwhile 2). The company's debt policy in terms of the solvency ratio (Leverage/coverage) as measured by the Debt To Equity Ratio (DER) for 2020-2021 can be categorized as high with a low.

The purpose of the study was to determine the effect of product quality and service quality on customer satisfaction at the SLV Room Boutique. The population in this study were consumers of SLV Room Boutique. the sampling in this study was carried out using a purposive sampling technique so that 80 respondents were sampled. The type of research used is quantitative. The data used is primary data obtained by distributing questionnaires to consumers. The results of the study after the data was processed with SPSS.23 show that there is a partially significant effect between the independent variable and the dependent variable which can be proven by the calculated t value of the product quality variable (X1) of 2,938> t table 1.665 with a significant value of 0.004 <0.05 and the calculated t value of the service quality variable (X2) of 4.700> t table 1.665 with a significant value of 0.000 <0.05. Simultaneously there is a positive and significant effect on the dependent variable (X2). Simultaneously, there is a positive and significant influence between the independent variables on the dependent variable as evidenced by the multiple linear regression equation, namely Y = -0.754 + 0.167 X1 + 0.338 X2 + e and a significant effect with a calculated F value of 56.016 > F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. Simultaneously there is a positive and significant effect on the dependent variable (X2). Simultaneously, there is a positive and significant influence between the independent variables on the dependent variable as evidenced by the multiple linear regression equation, namely Y = -0.754 + 0.167 X1 + 0.338 X2 + e and a significant effect with a calculated F value of 56.016 > F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. Simultaneously there is a positive and significant effect on the dependent variable (X2). Simultaneously, there is a positive and significant influence between the independent variables on the dependent variable as evidenced by the multiple linear regression equation, namely Y = -0.754 + 0.167 X1 + 0.338 X2 + e and a significant effect with a calculated F value of 56.016 > F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. there is a positive and significant influence between the independent variables on the dependent variable as evidenced by the multiple linear regression equation, namely Y = -0.754 + 0.167 X1 + 0.338 X2 + e and a significant effect with a calculated F value of 56.016> F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. there is a positive and significant influence between the independent variables on the dependent variable as evidenced by the multiple linear regression equation, namely Y = -0.754 + 0.167 X1 + 0.338 X2 + e and a significant effect with a calculated F value of 56.016> F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. 338 X2 + e and a significant effect with a calculated F value of 56.016> F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study. 338 X2 + e and a significant effect with a calculated F value of 56.016> F table 3.965 with a significant level of 0.00 <0.05. The R Square value or the coefficient of determination of 0.593 indicates that 59.3% of the customer satisfaction variable (Y) is influenced or can be explained by the independent variables of product quality and service quality, while the remaining 40.7% is explained by other variables not included in this study.

This research aims todetermine the potential for Financial Distress in cement manufacturing companies listed on the Indonesia Stock Exchange for the 2018-2021 period. The equation model used is the Altman Z-Score and the Taffler model. The data collection technique used is the documentation method with quantitative descriptive research data types. The results of the study show that cement sub-sector manufacturing companies for the 2018-2021 period have varied analysis results depending on the type of model implementation used. Companies that are in a healthy condition (safe area) for the two equation models are INTP in 2018-2021, and SMBR in 2018 for the Altman Z-Score model, while the SMGR Taffler model in 2018 is in a healthy condition (safe area). SMGR 2018-2020, WSBP 2018-2019, WTON 2018, and SMBR for 2020-2021 is in a gray area position for the Altman Z-Score equation model. While in the taffler model, companies that are in a gray area position are SMGR in 2019-2021, SMCB 2019-2020, WSBP in 2021, and SMBR in 2018 and 2021. Meanwhile companies that are in a bankrupt position are SMGR in 2021, SMCB in 2018- 2021, 2019-2020 WSBP, 2019-2021 WTON and 2019 SMBR for the Altman Z-Score model. In the taffler model, bankrupt companies are 2018 and 2021 SMCB, 2020 WSBP, and 2019-2020 SMBR. 2019-2020 WSBP, 2019-2021 WTON and 2019 SMBR for the Altman Z-Score model. In the taffler model, bankrupt companies are 2018 and 2021 SMCB, 2020 WSBP, and 2019-2020 SMBR. 2019-2020 WSBP, 2019-2021 WTON and 2019 SMBR for the Altman Z-Score model. In the taffler model, bankrupt companies are 2018 and 2021 SMCB, 2020 WSBP, and 2019-2020 SMBR