This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic.

This study updates Humanitarian Logistics Digital Business Ecosystem framework coupled with the development of a proposed integrated CCSD-CoCoSo MCDM method to rank factors used in assessing humanitarian and business logistics actor’s propensity to use, diffuse, and adopt a collaborative digital business ecosystem platform for their future operational use. Employing nine criteria derived from technology innovation theories and institutional theory, and 28 experts comprising our decision matrix. The findings report perceived relative advantage, perceived safety and security, and infrastructure and expertise as the top three vital criteria that experts believe when addressed in an ecosystem platform for humanitarian and business logistics actors it would encourage a collaboration for their sustainable future operations. With organisational culture and structure as the least prioritised criteria. The study concludes that the CCSD-CoCoSo obtained results are objective, validating, and that this model is useful and suitable for MCDM analysis and policy making.

Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.

The purpose of this study is to analyse the simultaneous spatial relationship between economic growth and e-commerce as well as the spillover effect between the two variables in East Java at the beginning of the Covid-19 pandemic in 2020. To answer the research objectives, spatial simultaneous modeling is used with the Spatial Autoregressive Generalized Spatial Three Model. Stage Least Square (SAR-GS3SLS) using rook contiguity. Based on the results of the SAR-GS3SLS, it can be concluded that at the beginning of the Covid-19 pandemic in 2020 in East Java, economic growth and e-commerce were simultaneously spatially interconnected. Variables that affect East Java's economic growth are e-commerce activities, the number of villages that have Base Transceiver Stations (BTS) and the spatial lag of economic growth (ρ1) while the open unemployment rate (TPT) and the Gini ratio have no significant effect on growth. economy. Variables that affect e-commerce are economic growth, internet banking users, percentage of population who have cellphones, number of millennials, number of villages that have ATMs and spatial lag of e-commerce (ρ2) while the number of villages with 4G/LTE signals has no effect on e-commerce. commerce. Regencies/cities that provide the highest spillover of economic growth and e-commerce in East Java are Malang, Mojokerto and Madiun Regencies. The three districts were able to provide a positive net spillover.