Penelitian ini bertujuan untuk  mengetahui pengaruh penggunaan Mind Map dengan aplikasi Prezi terhadap peningkatan aktivitas belajar dan hasil belajar siswa. Penelitian ini dilakukan di MTs. Miftahul Ulum Sidomukti pada siswa kelas VII yang berjumlah 37 siswa pada mata pelajaran IPS pokok bahasan Lembaga Sosial. Metode yang digunakan adalah kuantitatif kausal dengan penentuan daerah penelitian menggunakan metode purposive sampling. Alat pengumpulan data menggunakan angket, tes berupa ulangan harian, dokumentasi dan wawancara. Derajat valid dan reliabel alat dengan uji validitas dan reliabilitas, uji analisis data menggunakan analisis regresi linier dengan bantuan program SPSS Versi 22. Hasil penelitian menunjukkan: 1) nilai sig. dari variabel penggunaan Mind Map dengan Aplikasi Prezi terhadap aktivitas belajar siswa sebesar 0.000, kesimpulan sig. 0,05, berarti penggunaan Mind Map dengan Aplikasi Prezi berpengaruh signifikan terhadap aktivitas belajar siswa; 2) nilai sig. dari variabel penggunaan Mind Map dengan Aplikasi Prezi terhadap hasil belajar siswa adalah 0.000, kesimpulan sig. 0,05, berarti penggunaan Mind Map dengan Aplikasi Prezi berpengaruh signifikan terhadap hasil belajar siswa. Saran yang diajukan agar para guru  menyajikan pembelajaran di dalam kelas dengan lebih menarik seperti menggunkan aplikasi Prezi dan menggunakan Mind Map yang memudahkan siswa untuk belajar, memahami materi dan menciptakan kegiatan pembelajaran lebih menyenangkan.

Rainfall forecasting is essential for Indonesia, which is an agricultural country. Forecasting to see the rainfall needed to anticipate the danger of drought that will harm farmers. However, due to the complexity of topography and the interactions between the oceans, land, and atmosphere in Indonesia, it is difficult to predict rainfall. Therefore, Statistical Downscaling (SD) is needed to provide accurate rainfall predictions by considering the information about global atmospheric circulation obtained from the General Circulation Model (GCM). Statistics Downscaling (SD) modeling is a basic regression model based on the functional relationship between local scales, which is the response variable with the Global Circulation Model (GCM) global scale as a predictor variable. The Statistics Downscaling (SD) method used is Principal Component Regression (PCR) and Projection Pursuit Regression (PPR). The prediction of both methods was conducted by an Artificial Neural Network (ANN). The results showed that the prediction of rainfall in Jember using the PPR + ANN method (with the RMSE value of 79.58723) had better accuracy than the PPR, PCR, and PCR + ANN methods, which had RMSE values of 103.7539, 112.337 and 83.62029, respectively.

The COVID-19 pandemic that hit Indonesia caused a decline in agricultural production, rising food prices, restrictions on export-import activities, and a decrease in food and non-food consumption. The purpose of this study was to determine the demand for staple food during the pandemic era, to examine household budget allocations and to determine price elasticity and income elasticity. This study uses expenditure data for consumption of the Indonesian population based on the results of the March 2020 Susenas. Data analysis uses the AIDS model. The results of the analysis show that rice is the main staple food with a share of expenditure of 52% in urban areas and 57% in rural areas, followed by chicken meat, eggs, cooking oil, sugar and milk, respectively. Comparison of consumption between before and during the pandemic era shows an increase in consumption for all commodities other than milk and sugar in urban areas, while in rural areas consumption decreases for rice, milk and sugar. The share of staple food expenditure is significantly influenced by prices and income in urban areas, while in rural areas prices and incomes have no significant effect. Both in urban and rural areas, the highest income elasticity is for chicken meat and eggs, while rice, cooking oil and sugar are considered inferior goods. The pandemic era is the right moment for the government to promote local food to accelerate food diversification programs.

The COVID-19 pandemic that hit Indonesia caused a decline in agricultural production, rising food prices, restrictions on export-import activities, and a decrease in food and non-food consumption. The purpose of this study was to determine the demand for staple food during the pandemic era, to examine household budget allocations and to determine price elasticity and income elasticity. This study uses expenditure data for consumption of the Indonesian population based on the results of the March 2020 Susenas. Data analysis uses the AIDS model. The results of the analysis show that rice is the main staple food with a share of expenditure of 52% in urban areas and 57% in rural areas, followed by chicken meat, eggs, cooking oil, sugar and milk, respectively. Comparison of consumption between before and during the pandemic era shows an increase in consumption for all commodities other than milk and sugar in urban areas, while in rural areas consumption decreases for rice, milk and sugar. The share of staple food expenditure is significantly influenced by prices and income in urban areas, while in rural areas prices and incomes have no significant effect. Both in urban and rural areas, the highest income elasticity is for chicken meat and eggs, while rice, cooking oil and sugar are considered inferior goods. The pandemic era is the right moment for the government to promote local food to accelerate food diversification programs.