Business Survival Strategy during COVID-19 Pandemic: A Case Study of Kopontren

The COVID-19 pandemic has drastically affected conditions and activities, including at the boarding school kopontren in West Java Province, Indonesia. Business activities carried out to meet daily needs had to be stopped. This study aims to determine the kopontren survival strategy used to survive during the pandemic. The case studies were conducted through in-depth interviews with the heads of the Kopontren business units, business actors, and academics who were directly or indirectly involved in the Kopontren business. This study refers to the theory of survival strategies in the context of Islamic boarding schools called kopontren during the COVID-19 pandemic. The results showed that the efforts made by Kopontren in surviving the pandemic were reducing production capacity, changing business models, exploiting natural resources, and being strengthened by working together. © 2023, GreyNet. All rights reserved.

Predicting Electricity Consumption in Microgrid-based Educational Building using Google Trends, Google Mobility, and COVID-19 Data in the Context of COVID-19 Pandemic

Electricity demand has been disrupted in various countries since many governments imposed comprehensive social restriction policies to control the COVID-19 pandemic. Obtaining accurate electricity consumption predictions in this highly uncertain period is particularly important for building operators to improve the corresponding operational planning efficacy. Nevertheless, developing accurate electricity consumption prediction models for buildings within the COVID-19 context is a nontrivial task. Correspondingly, this research focuses on incorporating publicly available internet data (i.e., Google Trends, Google Mobility, and COVID-19 data) to develop accurate electricity consumption prediction models for microgrid-based buildings during the COVID 19 pandemic. For this purpose, we developed extreme gradient boosting (XGBoost), support vector regression (SVR), and autoregressive integrated moving average with explanatory variable (ARIMAX) models. As a case study, we analyzed a real-life electricity consumption dataset of a six-floor microgrid-designed educational building at a technological university in Bandung, West Java, Indonesia. The findings show that incorporating publicly online data positively impacts prediction accuracy. The accuracy increases, even more when we use the lagged value of the predictors. XGBoost models utilizing lagged historical values of the electricity consumption, Google Trends, and COVID-19 data of the previous days is the best performing model. However, adding more lagged predictors does not necessarily increase SVR models’accuracy. Lastly, the ARIMAX models become the worst-performing models compared to XGBoost and SVR models. Author

Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Purpose: This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data. Design/methodology/approach: To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Findings: Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models. Originality/value: First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand. © 2021, Dinda Thalia Andariesta and Meditya Wasesa.