Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series.

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data.

COVID-19 has cursorily spread globally. Just in four months, its status altered into a pandemic. In Indonesia, the virus epicenter is identified in Java. The first positive case was identified in West Java and later spread in all Java. The Large-scale Social Restrictions are seemingly inefficient as the SARS-CoV-2 transmission remains. As such, the government is struggling to find anticipatory policies and steps best to mitigate the transmission. In this particular article, we used a Spatio-temporal model method for the total COVID-19 cases in Java and forecasted the total cases for the next 14 days, allowing the stakeholders to make more effective policies. The data we were using were the daily data of the cumulative number of COVID-19 cases taken from www.covid19.go.id. Data modelling was conducted using a generalized spatio-temporal autoregressive model. The model acquired to model the COVID-19 cases in Java was the GSTAR(1)(1,0,0) model.

Krisik Village is one of the villages located in Gandusari District, Blitar Regency, East Java Province. Krisik Village has abundant natural resources. Krisik Village has livestock products in the form of milk and its processed products which are managed independently by the Bumdes Krisik. Krisik Village already has several types of dairy products, namely fermented milk, milk sticks, milk candy and milk ice cream. As a step to improve the typical product of Krisik village, it is necessary to have an activity that is able to increase public understanding in product processing and marketing. This service activity aims to improve the quality and marketing of dairy products in Krisik village. Activities that have been carried out are in the form of coordination with the village, making ice cream packaging designs and product marketing training by utilizing social media. This activity is expected to increase the independence of the crisis village community in marketing their products.

The development of spatial modeling for soil properties has progressed in recent decades. This responds to the growing demand for land spatial data and exact soil property prediction for agronomical reasons, particularly in precision farming, in order to speed up precision agricultural activities. In this regards a comparison of the GWR and RF models was carried out in order to determine which model is the best at forecasting surface soil texture and how dependable each model is at doing so. The purpose of this research is to get the best model in predicting particle soil fraction (PSF). 50 topsoil samples were collected from several locations in the Kalikonto Watershed, Indonesia, and the soil PSF (sand, silt, and clay) in the upper 10 cm varied. The LMV, slope, and elevation were calculated using DEM data and utilized as predictor variables. As a result, the weighting of the GWR model has a considerable impact on the final model, and all other factors have a major effect on the PSF determination. The RF, on the other hand, looks to be superior than the GWR variants. The RF model outperformed the other models in every PSF variable. This study reveals that topsoil quality and terrain attributes are linked, which may be assessed using field measurements and model projections. More research is needed to generate more efficient input parameters that will help with soil variability precision and accuracy of soil map products.