Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate.Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.

In recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time used to compare how fast a model can detect object within an image; and mAP also applied to measured detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models.