This paper aims to develop the role of internal factors, external factors, and risk management variables on MSMEs’ business performance. This research was conducted in underdeveloped regions of five provinces, which includes 14 cities in Indonesia—East Java, West Sumatra, North Sumatra, West Nusa Tenggara, and East Nusa Tenggara. The Resource-based view and Market-based view methods were chosen to measure 1401 data of MSMEs. The data was collected using offline questionnaires then processed using SPSS. This paper demonstrates a remarkable outcome for MSMEs, showing the significant result of risk management factors that includes risk assessment of marketing and financial management. Other independent variables of internal, external, and risk management factors also show important outcomes on MSMEs performance. This paper offers additional value of the implementation of ERM in MSMEs, which are spread in underdeveloped regions in Indonesia. The findings shown that the activity of the enterprises in identifying and managing risk would bring up the significant effect on operational business performances.

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This article aims to examine the existing current studies in urban community resilience related to the pandemic (COVID-19 Pandemic) and to raise a sense of urgency in future research for the model building of urban community resilience during the next pandemic if any. Our review is based on 47 articles considered eligible following inclusion and exclusion criteria after identifying, screening, and prioritizing the title and abstract of 703 records. The insights are organized by three indicators that affect community resilience. The first is the urban community’s vulnerabilities during a particular pandemic. Second, the types of community organizations in dealing with the impact of the pandemic. Third, key dimensions to build community resilience. We draw some intriguing takes for future research that needs to focus on community resilience after the pandemic cases have hit the ground, as a need to draw lessons for the new normal and future challenges and to examine the social learning and transformation process to establish community resilience.

On the occasion of the Government Bureaucracy Reform implementation, especially the 7th program, Performance Accountability Strengthening, ministries and institutions (K/L) of the government continue to work to build a better performance management system. Recently, in the democratization and globalization era, the measurement of organization performance is an urgent task for a good government. Thus, a modern performance management is needed to provide transparency on performance accountability through a government performance management system mandated in the governance reform. Performance management is the process of developing shared understanding of (1) what will be achieved, (2) how to achieve the performance, and(3) what approach to improve performance achievement. Performance Accountability of Government Institution System (SAKIP) is a reform model to realize the good governance from the issuance of the MPR Decree XI/1998 and Law No. 28/1999 concerning clean and free of corruption state through the principles of good state administration (Law No. 28/1999), one of which is accountability. The Balanced Scorecard (BSC) with four perspectives is a highly effective method of measuring and managing performance to enhance accountability (the seventh principle) through the integration of government planning and budgeting systems, at the central, provincial and municipal levels. In addition, BSC is integrated between internal levels of government organizations from the vision of the government organization's mission as well as Strategic Objectives (SS) and the Key Performance Indicators (IKU). Furthermore, BSC with the cascade and alignment methods can be used as a tool to improve the organizational structure and functions. This study focuses on the measurement of accountability and improving the performance of government organizations in achieving the social impacts of development outcomes. The statistical test results of the significance outcomes of the MMAF (Ministry of Marine Affairs and Fisheries) based on the maximum standard of the accountability value as defined by the Ministry of Administrative and Bureaucratic Reform, is an average of above 0.5 (95%), while the achievement of organizational performance with the BSC stakeholders perspective is 132.51% (2015), 118.3% (2016), and 99.05% (2017).

This study aims to predict customers’ behavior in classifying their reviews as high rated or low rated using associated attributes and topics found in the review. Knowing customer reviewing action better can lead to a successful strategy implementation of the relevant parties related to this study such as policy to manage customer reviews by keeping their satisfaction high. We applied a big data approach on a dataset of 55,377 reviews from Airbnb listings in the top 10 most visited cities in Indonesia (based on foreign arrivals data). We used The Classification and Regression Tree Model, Random Forest Model, Least Absolute Shrinkage and Selection Operation and Logistic Regression Model, Artificial Neural Network as well as Multi-Layer Perceptron to make prediction’s classification. Those models are used to identify a set of attributes and topics that will increase the chance of the review to render a high rate and a different set of attributes and topics that will lead the review to be low rated. This study found; first, attributes and topics that influence customers' odds to classify their review as high rated or low rated adhere to the understanding of Peer to Peer accommodation attributes. Second, successfully proved that customer reviews' attributes and topics could be used to predict the classification of ratings in Peer to Peer accommodation. Where for Topics, we can predict the rating using Random Forest yields 60.09% accuracy, slightly better than Artificial Neural Network (58.33%) and Multi-Layer Perceptron (58.8%). However, it seems better to use Attributes to predict the rating, where the accuracy is yielded better by applying Artificial Neural Network with 84.79% accuracy compared to Multi-Layer Perceptron with only 72.35% of accuracy.

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