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The Effect Of Audit Rotation, Audit Tenure And Auditor Reputation On Audit Quality With Profitability As Moderating Variable Mia Austina Anggraini; Sigit Sanjaya; Yamasitha Yamasitha; Yulasmi Yulasmi
UPI YPTK Journal of Business and Economics Vol. 7 No. 1 (2022): January 2022
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Putra Indonesia YPTK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jbe.v7i1.28

Abstract

This study aims to determine the effect of Audit Tenure, Audit Committee and Auditor Reputation on Audit Quality with Profitability as a Moderation variable. The sample used is a manufacturing company listed on the Indonesia Stock Exchange (BEI) 2015-2019. In determining the sample using purposive sampling method.The data used are secondary data and the method of analysis used is logistic regression analysis.The results showed that partially Audit Rotation has a positive and significant effect on Audit Quality with a significant value of 0,012. Partially the Audit Tenure has a positive and insignificant effect on Audit Quality with a significant value of 0,346. Partially Auditor Reputation has a positive and significant effect on Audit Quality with a significant value of 0,028. Partially Audit Rotation has a positive and significant effect on Audit Quality with Profitability as a moderating variable with a insignificant value of 0,655. Partially the Audit Tenure has a positive and significant effect on Audit Quality with Profitability as a moderating variable with a insignificant value of 0,720. Partially, Auditor Reputation has a positive and significant effect on Audit Quality with Profitability as a moderating variable with a significant value of 0,000. Simultaneously the Audit Tenure, Audit Committee and Auditor Reputation have a positive and significant effect on Audit Quality with profitability as a moderating variable with a significant value of 0,003.
Implementation multiple linear regresion in neural network predict gold price Musli Yanto; Sigit Sanjaya; Yulasmi Yulasmi; Dodi Guswandi; Syafri Arlis
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1635-1642

Abstract

The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.