Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

The development of information technology is currently causing increase fierce competition between industries. The competition has an impact on the development of the micro small and medium business sector (MSME). The MSMEs sector are often less aware on the importance of information technology in this case the use of online trading system (e-commerce) which used to increase the scope of product sales and can speed up existing business processes. Especially MSMEs in Malang District are limited to self-taught sales without using information technology support. This has an impact on the products sold that are not widely known by the public in general which causes MSME revenues to tend to be low. Therefore, in this study trying to design an e-commerce application that aims to increase the income of Micro Small Medium Enterprises in Malang Districts.

Penelitian ini bertujuan untuk menganalisis kendala atau kesulitan yang dialami siswa saat mendapatkan pembelajaran online pada mata pelajaran matematika selama masa pandemi Covid-19. Metode yang digunakan dalam penelitian ini adalah deskriptif kualitatif.  Penelitian ini dilaksanakan di SDN  Cibeureum 04 Bogor dengan melibatkan 28 siswa kelas 4. Penelitian ini menggunakan beberapa instrumen antara lain angket untuk siswa, bimbingan orang tua, dan untuk guru, sehingga dapat dianalisis. Hasil penelitian menunjukkan bahwa beberapa kesulitan berdampak pada siswa kelas 4 di SDN 04 Cibeureum, Bogor.  Kendalanya adalah pembelajaran online yang tidak efektif dan kurangnya dukungan fasilitas.  Dari angket terlihat hanya beberapa siswa saja yang memiliki fasilitas yang memadai untuk mendukung pembelajaran online.  Apalagi akses internet yang tidak stabil dapat menghambat siswa untuk belajar matematika secara online.  Dalam penyampaian materi, guru harus inovatif agar proses pembelajaran tidak membosankan siswa selama pembelajaran matematika online.  Guru harus berkomunikasi agar siswa mudah memahami materi yang dijelaskan karena bagi sebagian siswa matematika dianggap sebagai mata pelajaran yang sulit, sehingga guru dan siswa harus lebih siap menerima tantangan di masa pandemi covid-19 ini.  sehingga pembelajaran online akan berjalan dengan optimal.

Daya dukung lahan pertanian merupakan salah satu komponen penting dalam  menentukan keberlanjutan pertanian. Hal ini terjadi karena tanpa adanya dukungan lahan pertanian maka keberlanjutan pertanian tidak akan dapat dipertahankan. Berdasarkan hal tersebut maka penelitian ini dilakukan dengan tujuan untuk mengkaji   daya dukung lahan pertanian dan  keberlanjutan pertanian di Desa Duren Kecamatan Bandungan Semarang. Jumlah sampel yang diambil sebanyak 92 rumahtangga tani  secara random sampling.  Data terdiri dari data primer dan data sekunder. Pengukuran daya dukung lahan   pertanian dilakukan secara kuantitatif, sedangkan pengukuran keberlanjutan pertanian dilakukan secara kualitatif dengan skala likerts dan dianalisis dengan metode kuartil (Q). Hasil analisis daya dukung lahan pertanian menunjukkan bahwa lahan pertanian   tidak lagi mendukung terhadap kehidupan petani, namun dari sisi keberlanjutannya masih menunjukkan tinggi. Hal ini terlihat dari hasil analisis keberlanjutan pertanian, dari 5 (lima)  dimensi  keberlanjutan, hanya 1 (satu) dimensi yang tergolong rendah yaitu dimensi sosal, sedangkan   dimensi ekonomi, dimensi lingkungan, dimensi kelembagaan dan dimensi teknologi masih tergolong tinggi dalam mendukung keberlanjutan pertanian. Carrying capacity of agricultural land is one important component in determining agricultural sustainability. This happens because without the carrying capacity of agricultural land, the sustainability of agriculture will not be maintained. Based on this, this research was conducted with the aim for analyze the carrying capacity of agricultural land and agricultural sustainability  in the Duren Village  Bandungan District, Semarang. The number of samples taken was 92 farming households by random sampling. Data consists of primary data and secondary data. Measurement of the carrying capacity of agricultural land is done quantitatively, while the measurement of agricultural sustainability is done qualitatively with a Likerts scale and analyzed by the quartile (Q) method. The results of the analysis of the carrying capacity of agricultural land indicate that agricultural land no longer supports the lives of farmers, but in terms of sustainability it still shows high. This can be seen from the results of the analysis of agricultural sustainability, from 5 (five) dimensions of sustainability, only 1 (one) dimension which is classified as low namely social dimension, while the economic dimension, environmental dimension, institutional dimension and technological dimension are still relatively high in supporting agricultural sustainability.