The COVID-19 pandemic led to a worldwide lockdown and school closures, which have placed a substantial mental health burden on children and college students. Through a systematic search of the literature on PubMed and Collabovid of studies published January 2020–July 2021, our findings of five studies on children and 16 studies on college students found that both groups reported feeling more anxious, depressed, fatigued, and distressed than prior to the pandemic. Several risk factors such as living in rural areas, low family socioeconomic status, and being a family member or friend to a healthcare worker were strongly associated with worse mental health outcomes. As schools and researchers discuss future strategies on how to combine on-site teaching with online courses, our results indicate the importance of considering social contacts in students’ mental health to support students at higher risk of social isolation during the COVID-19 pandemic.

This study aims to identify and analyze the impact of the Covid-19 on the home industry and SMEs sector. The research method used was a literature study. Literature study is method research conducted by examining ten previous studies on strategies to survive the SMEs during the pandemic. Small and medium enterprises (SMEs) are on the edge of a cliff. The Covid-19 pandemic caused the economic downturn. The lockdown stopped economic activity, reduced demand, and reduced interactions with others. At the beginning of the lockdown, SMEs one by one suffered losses and went bankrupt. The impact of the Covid-19 pandemic on the home industry and SMEs sector is certainly influential because it has made a significant contribution to the economy in this field. The results showed that the use of information technology had been applied quite widely in various areas. Quite a lot of offline shops have also tried to open shops online, e-commerce sites, and even web e-commerce, apart from being accessed via the web, are also widely available. From the use of computers and the internet in managing their business. The conclusion that millennial customers' online buying interest during the Covid-19 outbreak was not influenced by product prices but influenced by millennial customer trust.

Waduk Setiabudi Barat terletak di pusat kota Jakarta dengan luas waduk 4 ha dan luas daerah layanan ±170 ha. Waduk Setiabudi Barat ini termasuk dalam jenis waduk banjir, yang pengoperasiannya mengunakan pompa, atau lebih dikenal dengan waduk sistem polder. Fungsi dari suatu waduk banjir adalah menampung sebagian aliran banjir dan memperkecil puncak banjir pada suatu wilayah agar tidak terjadi genangan/banjir. Selain itu juga Waduk Setiabudi Barat ini berfungsi menampung air dari perumahan dan perkantoran untuk memindahkan air ke banjir kanal. Waduk Setiabudi Barat ini telah dibangun sejak tahun 1982, tetapi setelah 23 tahun waduk ini melayani daerah yang berkembang dengan banyaknya gedung-gedung perkantoran, pusat bisnis, perdagangan, dan hotel, maka dari itu diperlukan suatu perhitungan ulang kembali volume Waduk Setiabudi Barat ini. Setiabudi Barat yang dihitung berdasarkan periode ulang tertentu. Waduk ini mengunakan 7 pompa dalam pengoperasiannya, terdiri dari 5 pompa dengan kapasitas masing-masing 1.1 m³/det dan 2 pompa dengan kapasitas masing-masing 1.7 m³/det yang baru dioperasikan pada tahun 2005 ini. Data yang digunakan dalam pembuatan skripsi ini adalah data curah hujan dari BMG, data komponen waduk, data denah lokasi. Tujuan dari pemodelan ini adalah untuk mendapatkan gambaran pendekatan kondisi eksisting dan mengetahui kondisi tampungan waduk apakah melebihi kapasitas ataupun mencukupi kapasitas dengan bantuan sebuah program komputer. Dengan pemodelan ini diharapkan agar mengetahui kondisi waduk ketika terjadi banjir maka akan dilakukan pemodelan ulang untuk alternatif penanggulangannya. Program komputer yang nantinya akan digunakan untuk pendekatan model hidrodinamik adalah aplikasi HEC-RAS 4.1.0.

This research uses the IS Success Model DeLone and McLean approach to finding the relationship that occurs between the quality of the Alodokter application to check the risk of contracting coronavirus. This research also tries to figure out how much benefit users have gained in conducting early detection of COVID-19. This Model uses six interrelated variables, including system quality, information quality, service quality, usage, user satisfaction, and clean benefits. With 200 respondents, data analysis uses the partial least square structural equation model (PLS-SEM) method with SmartPLS 3.0 software. This research gives the results that the better the quality of information and services of the Alodokter application, the more benefits gained by the user. However, the system quality factor from the Alodokter application does not affect how much benefits a user gains while they use the app.