No abstract

The international expansion process of firms has been the subject of many studies, nonetheless only Western firms have become the most discussed within the literature. Fewer studies have been done with an emphasis on Southeast Asian firms. Following this issue, this study aims to investigate the most suitable strategy as well as the best entrance modes for firms to internationalize and accordingly, to offer a strategy implementation guideline for firms to enter foreign markets successfully. As the research method prerequisite, this study applies the quantitative approach.  The key finding of this study indicates that there is a strong positive relationship between firms’ international expansion and foreign market entrance. Additionally, this study’s finding is also considered a useful guideline for the SME manager to internationalize or enter the foreign market, especially during the hits of the COVID-19 Pandemic. Keywords: COVID-19; entry mode; international expansion; outdoor education;  SMEs.

This research is to answer and offer alternative policy strategies for reducing work stress. Work stress consists of individual level and group level to analyze of effect on employee performance. This type of research is correlational research. Population in this study is PT Centrindo Palmax employees, totaling 150 employees. Sampling in this study used a simple random sampling method with a specified sample of 100 respondents. To answer the hypothesis using multiple linear regression analysis. The results show that individual level has a positive and significant effect on employee performance. Group level has a positive and significant effect on employee performance

This study implements stock index price predictions using the LSTM method, where one of the processes in data management before running with the LSTM method is data split. This study also looks for the most appropriate split data ratio in predicting stock index prices to minimize error rates and differences in forecasted prices and original prices because in previous studies there were several rules of thumb in dividing data, so it is necessary to compare the most appropriate ratios in this research. Based on the evaluation process, the error value was found from nine split data ratios that were run by five ratios which produced a predictive graph line shape that resembled the validation line. Three datasets, namely split data ratios of 80:20, 70:30, and 60:40, are the ratios that get the lowest error values based on the RMSE, MSE, MAPE, and MAE values in the five stock index datasets. The three ratios are then compared again by looking at the average percentage difference between the validation price and the predicted price for the next working day, and it is found that the ratio of 80:20 is the most suitable split data ratio for predicting the stock index price for the next working day, with a level of difference in the average value between the original price and the predicted price on the stock index of 1.3%. While the ratio of 70:30 has an average predicted value of five stock index datasets of 1.9% and a ratio of 60:40 of 1.8%.