Corona Virus Disease (COVID-19) pandemic is causing a health crisis. One of the effective methods against the virus is wearing a face mask. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. The face mask recognition in this study is developed with a machine learning algorithm through the image classification method: MobileNetV2. The steps for building the model are collecting the data, pre-processing, split the data, testing the model, and implement the model. The built model can detect people who are wearing a face mask and not wearing it at an accuracy of 96,85 percent. After the model implemented in 25 cities from various source of image, the percentage of people wearing face mask in the cities has a strong correlation to the vigilance index of COVID-19 which is 0,62.

More than 214,000 cases were reported with 8,650 deaths in all provinces in Indonesia as of September 12, 2020. With there is no COVID-19 vaccine yet, Indonesia and other countries rely on various government policies and programs by prioritizing health protocols to impede the spread of the virus. During this pandemic, the children have various impacts for instance health problems, welfare, development, and future hopes. The various negative impacts of COVID-19 are unequally impacting all children. Children who are in a vulnerable environment are likely to feel a greater impact than others. However, in Indonesia, the mitigation and vulnerability reduction efforts have not been supported by information on the level of social vulnerability, especially for children. Therefore, this study provides the Children Social Vulnerability Index (SoVI) in the case of COVID-19 to the district level. Social vulnerability indices among children were obtained by factor analysis. Four factors that affect social vulnerability among children in Indonesia are found: 'socioeconomic and health status', 'family structure', 'access to health facility', and 'demographic characteristics of household'. The formulation of SoVI that focusing on children can help the government to take early actions during the pandemic to minimize its impacts.

Pandemi memberikan dampak pada berbagai lapisan kehidupan sosial ekonomi termasuk ketenagakerjaan. Dampak berupa pengurangan jam kerja, sementara tidak bekerja, pengangguran, atau menjadi bukan angkatan kerja dirasakan baik oleh tenaga kerja formal maupun informal. Terlebih, tenaga kerja informal memang sudah menjadi tenaga kerja yang rentan. Permasalahan tersebut menjadikan perlunya mengetahui bagaimana kondisi kelayakan pekerjaan di Indonesia di masa pandemi. Penelitian ini menghitung Indeks Pekerjaan Layak (IPL) pada masa pandemi di Indonesia bertujuan untuk mengetahui gambaran provinsi di Indonesia agar pihak terkait dalam melakukan perencanaan hingga evaluasi yang matang. Dengan menggunakan analisis faktor, penelitian ini menghasilkan temuan bahwa beberapa daerah ternyata cenderung masih memiliki IPL yang rendah. Artinya pemerintah pusat maupun daerah di wilayah terkait seharusnya semakin bersifat sensitif dan responsif terhadap kelayakan bekerja penduduknya dimulai dari fokus pada variabel-variabel yang berdampak secara signifikan.

Corona Virus Desease (COVID-19) pandemic is causing health crisis in every region in the world, especially in Indonesia. One of the effective methods against the virus is wearing face mask in public place as the regulation made by the authorities. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. On the other hand, this solution can be used as communication tool to evaluate people’s habit on wearing face mask. The face mask recognition in this study is developed with machine learning algorithm through the image classification method: MobileNetv2. The proposed model can be integrated with surveillance camera to impede the Covid-19 transmission by allowing the detection of people who are not wearing face mask. After the training, validation, and testing phase, the model can provide the percentage of people using face mask in some cities with high accuracy. The data produced also have a strong correlation to the vigilance index of COVID-19.

Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T2 control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts.