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Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.
Aryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land 2020, 9, 377 .
AMA StyleAryo Condro, Yudi Setiawan, Lilik Prasetyo, Rahmat Pramulya, Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land. 2020; 9 (10):377.
Chicago/Turabian StyleAryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. 2020. "Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform." Land 9, no. 10: 377.
Sea surface temperature is an important factor that influences climate change and productivity in the sea. Sea surface temperature is measured manually every day. The method of observation is considered to be less efficient because the process is not easy and requires relatively expensive costs. In addition, consideration is taken when extreme weather can hinder the observation process and be able to endanger the observer. In this case, a prediction system is needed that can predict the current conditions and the next few days from sea surface temperature. Before predicting sea surface temperature, the first thing to do is to forecast each parameter of sea surface temperature. Parameters of sea surface temperature consist of air temperature, air humidity, rainfall, duration of solar radiation, and wind speed. Forecasting parameters of sea surface temperature are carried out using TS-GRNN. The best GRNN models for forecasting air temperature, air humidity, rainfall, duration of solar radiation, and wind speed were obtained by RMSE of 0.1421.
Dian Candra Rini Novitasari; Wahyu Tri Puspitasari; Rahmat Pramulya; Hetty Rohayani; R. R. Diah Nugraheni Setyowati; Dwi Rukma Santi; Muhammad Fahrur Rozi; Ary Widjajanto. Forecasting Sea Surface Temperature in Java Sea Using Generalized Regression Neural Networks. Proceedings of the Second International Conference on Intelligent Transportation 2020, 249 -257.
AMA StyleDian Candra Rini Novitasari, Wahyu Tri Puspitasari, Rahmat Pramulya, Hetty Rohayani, R. R. Diah Nugraheni Setyowati, Dwi Rukma Santi, Muhammad Fahrur Rozi, Ary Widjajanto. Forecasting Sea Surface Temperature in Java Sea Using Generalized Regression Neural Networks. Proceedings of the Second International Conference on Intelligent Transportation. 2020; ():249-257.
Chicago/Turabian StyleDian Candra Rini Novitasari; Wahyu Tri Puspitasari; Rahmat Pramulya; Hetty Rohayani; R. R. Diah Nugraheni Setyowati; Dwi Rukma Santi; Muhammad Fahrur Rozi; Ary Widjajanto. 2020. "Forecasting Sea Surface Temperature in Java Sea Using Generalized Regression Neural Networks." Proceedings of the Second International Conference on Intelligent Transportation , no. : 249-257.
The weather anomaly phenomenon that occurs can have some negative impact such as flooding, floods will paralyze the economic activities of the community, transportation activities, damage public infrastructure. In this research forecasting weather parameters as a variable for predicting the amount of rainfall using the ANFIS method and Support Vector Regression (SVR) with the aim to provide information on future weather conditions quickly and accurately. The people can prepare themselves and prepare the equipment needed to deal with it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using the ANFIS method shown that for wind speed predictions having RMSE of 1.975004, temperature predictions have RMSE of 0.742332, and predictions of relative humidity have RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method pre-processing using the Support Vector Regression (SVR) method produces an MSE error value of 0.0928.
Dian Candra Rini Novitasari; H Rohayani; Suwanto; Arnita; Rico; R Junaidi; Rr Diah Nugraheni Setyowati; Rahmat Pramulya; F Setiawan. Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR). Journal of Physics: Conference Series 2020, 1501, 1 .
AMA StyleDian Candra Rini Novitasari, H Rohayani, Suwanto, Arnita, Rico, R Junaidi, Rr Diah Nugraheni Setyowati, Rahmat Pramulya, F Setiawan. Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR). Journal of Physics: Conference Series. 2020; 1501 ():1.
Chicago/Turabian StyleDian Candra Rini Novitasari; H Rohayani; Suwanto; Arnita; Rico; R Junaidi; Rr Diah Nugraheni Setyowati; Rahmat Pramulya; F Setiawan. 2020. "Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR)." Journal of Physics: Conference Series 1501, no. : 1.
Agricultural waste has the potential of biomass as a raw material for producing renewable energy. The primary processing of coffee produces waste from pulping and hulling activities. Waste can be processed further through composting, anaerobic water waste treatment and burning to be converted into electrical energy. Therefore, the calculation is needed that estimates the amount of potential biomass that is converted. Then, the purpose of the paper is to analyze each stage in the life cycle of Gayo Arabica coffee and calculate the potential amount of electrical energy produced. The life cycle assessment method uses material and energy analysis intending to explain the flow of inputs and outputs within the system boundary and analyze the movement and transformation of materials, energy, waste, and emissions. In the context of the paper, the study uses material flow analysis to estimate the biomass potential from solid and water waste treatment. The study uses interviews, observations and a cooperative report located in Central Aceh district as an Arabica coffee producer area in Indonesia. Production of Arabica coffee is managed by cooperatives involving small farmers and collectors from cultivation, primary processing, packaging, and delivery. Cultivation uses the agroforestry system with a shade tree of the type of lamtoro (Leucaena leucocephala). Packing with a pack of burlap is done by the cooperative. Activities undertaken cooperatives include the acceptance of coffee beans from the collector. Since 2016 cooperatives implemented a policy of processing coffee beans at the collector level. The estimation of the study shows that waste treatment through anaerobic water waste treatment, composting and combustion from 1 ton of cherry coffee (primary processing) has an energy potential of 34 kwh.
R Pramulya; T Bantacut; E Noor; M Yani. Material flow analysis for energy potential in coffee production. IOP Conference Series: Earth and Environmental Science 2019, 399, 012011 .
AMA StyleR Pramulya, T Bantacut, E Noor, M Yani. Material flow analysis for energy potential in coffee production. IOP Conference Series: Earth and Environmental Science. 2019; 399 (1):012011.
Chicago/Turabian StyleR Pramulya; T Bantacut; E Noor; M Yani. 2019. "Material flow analysis for energy potential in coffee production." IOP Conference Series: Earth and Environmental Science 399, no. 1: 012011.