The quality of the education system is the foremost factor for the success of the students as well as the academia at large, and therefore, needs the focus of regulators and researchers alike. The current article investigates into the role of human resource (HR) quality and Kaizen on the quality assurance system in the context of the high school environment in Indonesia. The current research also analyzes the moderating role of national higher education policies among the associations of HR quality, Kaizen and the quality assurance system in the high school environment in Indonesia. The primary sources of data collection have been adopted by the researchers using survey questionnaires. The medical students are the respondents targeted by the researchers selected based on purposive sampling. The researchers have adopted primary data analysis tools such as Smart-PLS to test the hypotheses and check the validity and reliability of the items and constructs. The findings indicated that HR quality and Kaizen have a positive association with quality assurance systems within the high school environment in Indonesia. The outcomes also revealed that national higher education policies significantly moderate among the association of HR quality, Kaizen and quality assurance system in the high school environment in Indonesia. These outcomes provide valuable insights for policymaking regarding assessment and improvement of the quality assurance system in the education sector in the country. Keywords: Human resource quality, Kaizen, quality assurance system, national higher education policies, medical student
A watershed is a combination of several rivers and tributaries with certain boundaries that function to drain rainwater into a lake or a sea. One of the hydrological data contained in the watershed is discharge data. If there is incomplete discharge data, it must be extended based on historical data. ARIMA and decomposition are methods that can predict time series data. The purposes of this research are to determine the historical discharge patterns of Brantas Sub-basin, to know the discharge forecasting model of Brantas Sub-basin, to know the results of forecasting Brantas Sub-basin discharge, and to compare the accuracy between ARIMA and decomposition methods. The accuracy is obtained by calculating MSE and RMSE values. The best method is a method that has the smallest MSE and RMSE values. The results of the research showed that Brantas Sub-basin discharge data in 2007-2017 has a seasonal pattern. The best ARIMA model is ARIMA (0,0,3)(1,0,1)12 model, while the best decomposition model is the additive decomposition model. The Decomposition method has better accuracy than the ARIMA method in predicting discharge of Brantas Sub-basin.
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