Penelitian ini bertujuan untuk menguji pengaruh ukuran perusahaan, debt default, disclosure, opiniaudit tahun sebelumnya dan pertumbuhan perusahaan terhadap opini audit going concern pada perusahaanmanufaktur dalam bidang industri dasar dan kimia yang terdaftardi Bursa Efek Indonesia (BEI) 2013-2017.Sampel yang digunakan dalam penelitian ini adalah perusahaan manufaktur dalam bidang industri dasar dankimia yang terdaftardi Bursa Efek Indonesia berdasarkan kriteria yang telah ditetapkan. Metode samplingyang digunakan adalah purposive sampling dan 30 data terpilih sebagai sampel, serta pengujian hipotesisdalam penelitian ini menggunakan analisis regresi berganda. Hasil penelitian ini menunjukan bahwa variabelindependen ukuran perusahaandanopini audit tahun sebelumnya secara parsial berpengaruh signifikan, tetapivariabel independen debt default, disclosure dan pertumbuhan perusahaan tidak berpengaruh signifikanterhadap opini audit going concern. Dan hasil uji koefisien determinasi menunjukan angka sebesar 0,441, halini mengindikasikan bahwa secara simultan variabel independen ukuran perusahaan, , debt default,disclosure, opini audit tahun sebelumnya dan pertumbuhan perusahaan mampu menjelaskan variabeldependen opini audit going concern sebanyak 44,1% sedangkan sisanya sebesar 55,9% dijelaskan olehvariabel lain.
In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.
Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.