FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201935557> ?p ?o ?g. }

• W3201935557 abstract "Rice is one of the primary agricultural resources in Indonesia. The highest rice production is in East Java. By nature of yearly rice yield data, only a limited quantity of the dataset could be acquired. Furthermore, there is only a limited study that uses weather data, especially precipitation and temperature, are essential in rice crop. There is yet to be a rice yield prediction using weather data in East Java Indonesia, with last year yield production and most of the past researcher in Indonesia using regression methods and not using a neural network Predicting the rice yield of IndonesiaAƒi?½A¯Â?½AƒÂ¯A‚Â?A‚½Aƒi?½A¯Â?½Aƒi?½A‚¢Aƒi?½A¯Â?½Aƒi?½A‚¯Aƒi?½A¯Â?½Aƒi?½A‚Â?Aƒi?½A¯Â?½Aƒi?½A‚½Aƒi?½A¯Â?½Aƒi?½A‚¯Aƒi?½A¯Â?½Aƒi?½A‚Â?Aƒi?½A¯Â?½Aƒi?½A‚½s East Java province from 1971 to 2018, with climate conditions that are precipitation and average temperature per month, and last year rice yield production as predictors was researched using Multi-layer Perceptron (MLP) and Linear Regression (LR) models. In the evaluation, last year yield emerged as a dominant predictor that affect Multilayer Perceptron model evaluation percentage error reduction in 90.22% in Mean Squared Error (MSE) evaluation and 68.77% Root Mean Squared Error (RMSE) than not using last year yield data. The result shows that Multi-layer Perceptron (MLP) model performance is better than Linear Regression model. Rice is one of the primary agricultural resources in Indonesia. The highest rice production is in East Java. By nature of yearly rice yield data, only a limited quantity of the dataset could be acquired. Furthermore, there is only a limited study that uses weather data, especially precipitation and temperature, are essential in rice crop. There is yet to be a rice yield prediction using weather data in East Java Indonesia, with last year yield production and most of the past researcher in Indonesia using regression methods and not using a neural network Predicting the rice yield of IndonesiaAƒi?½A¯Â?½AƒÂ¯A‚Â?A‚½Aƒi?½A¯Â?½Aƒi?½A‚¢Aƒi?½A¯Â?½Aƒi?½A‚¯Aƒi?½A¯Â?½Aƒi?½A‚Â?Aƒi?½A¯Â?½Aƒi?½A‚½Aƒi?½A¯Â?½Aƒi?½A‚¯Aƒi?½A¯Â?½Aƒi?½A‚Â?Aƒi?½A¯Â?½Aƒi?½A‚½s East Java province from 1971 to 2018, with climate conditions that are precipitation and average temperature per month, and last year rice yield production as predictors was researched using Multi-layer Perceptron (MLP) and Linear Regression (LR) models. In the evaluation, last year yield emerged as a dominant predictor that affect Multilayer Perceptron model evaluation percentage error reduction in 90.22% in Mean Squared Error (MSE) evaluation and 68.77% Root Mean Squared Error (RMSE) than not using last year yield data. The result shows that Multi-layer Perceptron (MLP) model performance is better than Linear Regression model." @default.
• W3201935557 created "2021-10-11" @default.
• W3201935557 creator A5007926487 @default.
• W3201935557 creator A5046242495 @default.
• W3201935557 creator A5090787463 @default.
• W3201935557 date "2020-01-01" @default.
• W3201935557 modified "2023-09-26" @default.
• W3201935557 title "Predicting Rice Crop Yield Production Through Climate Data with Neural Network" @default.
• W3201935557 hasPublicationYear "2020" @default.
• W3201935557 type Work @default.
• W3201935557 sameAs 3201935557 @default.
• W3201935557 citedByCount "0" @default.
• W3201935557 crossrefType "journal-article" @default.
• W3201935557 hasAuthorship W3201935557A5007926487 @default.
• W3201935557 hasAuthorship W3201935557A5046242495 @default.
• W3201935557 hasAuthorship W3201935557A5090787463 @default.
• W3201935557 hasConcept C105795698 @default.
• W3201935557 hasConcept C107054158 @default.
• W3201935557 hasConcept C118518473 @default.
• W3201935557 hasConcept C119857082 @default.
• W3201935557 hasConcept C126343540 @default.
• W3201935557 hasConcept C127413603 @default.
• W3201935557 hasConcept C134121241 @default.
• W3201935557 hasConcept C139719470 @default.
• W3201935557 hasConcept C139945424 @default.
• W3201935557 hasConcept C152877465 @default.
• W3201935557 hasConcept C153294291 @default.
• W3201935557 hasConcept C162324750 @default.
• W3201935557 hasConcept C166957645 @default.
• W3201935557 hasConcept C179717631 @default.
• W3201935557 hasConcept C191897082 @default.
• W3201935557 hasConcept C192562407 @default.
• W3201935557 hasConcept C199360897 @default.
• W3201935557 hasConcept C205649164 @default.
• W3201935557 hasConcept C2778348673 @default.
• W3201935557 hasConcept C33923547 @default.
• W3201935557 hasConcept C39432304 @default.
• W3201935557 hasConcept C41008148 @default.
• W3201935557 hasConcept C48921125 @default.
• W3201935557 hasConcept C50644808 @default.
• W3201935557 hasConcept C548217200 @default.
• W3201935557 hasConcept C60908668 @default.
• W3201935557 hasConcept C6557445 @default.
• W3201935557 hasConcept C83546350 @default.
• W3201935557 hasConcept C86803240 @default.
• W3201935557 hasConcept C88463610 @default.
• W3201935557 hasConceptScore W3201935557C105795698 @default.
• W3201935557 hasConceptScore W3201935557C107054158 @default.
• W3201935557 hasConceptScore W3201935557C118518473 @default.
• W3201935557 hasConceptScore W3201935557C119857082 @default.
• W3201935557 hasConceptScore W3201935557C126343540 @default.
• W3201935557 hasConceptScore W3201935557C127413603 @default.
• W3201935557 hasConceptScore W3201935557C134121241 @default.
• W3201935557 hasConceptScore W3201935557C139719470 @default.
• W3201935557 hasConceptScore W3201935557C139945424 @default.
• W3201935557 hasConceptScore W3201935557C152877465 @default.
• W3201935557 hasConceptScore W3201935557C153294291 @default.
• W3201935557 hasConceptScore W3201935557C162324750 @default.
• W3201935557 hasConceptScore W3201935557C166957645 @default.
• W3201935557 hasConceptScore W3201935557C179717631 @default.
• W3201935557 hasConceptScore W3201935557C191897082 @default.
• W3201935557 hasConceptScore W3201935557C192562407 @default.
• W3201935557 hasConceptScore W3201935557C199360897 @default.
• W3201935557 hasConceptScore W3201935557C205649164 @default.
• W3201935557 hasConceptScore W3201935557C2778348673 @default.
• W3201935557 hasConceptScore W3201935557C33923547 @default.
• W3201935557 hasConceptScore W3201935557C39432304 @default.
• W3201935557 hasConceptScore W3201935557C41008148 @default.
• W3201935557 hasConceptScore W3201935557C48921125 @default.
• W3201935557 hasConceptScore W3201935557C50644808 @default.
• W3201935557 hasConceptScore W3201935557C548217200 @default.
• W3201935557 hasConceptScore W3201935557C60908668 @default.
• W3201935557 hasConceptScore W3201935557C6557445 @default.
• W3201935557 hasConceptScore W3201935557C83546350 @default.
• W3201935557 hasConceptScore W3201935557C86803240 @default.
• W3201935557 hasConceptScore W3201935557C88463610 @default.
• W3201935557 hasLocation W32019355571 @default.
• W3201935557 hasOpenAccess W3201935557 @default.
• W3201935557 hasPrimaryLocation W32019355571 @default.
• W3201935557 hasRelatedWork W1968764626 @default.
• W3201935557 hasRelatedWork W2017993898 @default.
• W3201935557 hasRelatedWork W2187118997 @default.
• W3201935557 hasRelatedWork W2348908174 @default.
• W3201935557 hasRelatedWork W2375522578 @default.
• W3201935557 hasRelatedWork W2616610202 @default.
• W3201935557 hasRelatedWork W3011112218 @default.
• W3201935557 hasRelatedWork W3048820825 @default.
• W3201935557 hasRelatedWork W3088666096 @default.
• W3201935557 hasRelatedWork W3095773266 @default.
• W3201935557 hasRelatedWork W3118925381 @default.
• W3201935557 hasRelatedWork W3128622810 @default.
• W3201935557 hasRelatedWork W3131745224 @default.
• W3201935557 hasRelatedWork W3171336435 @default.
• W3201935557 hasRelatedWork W3183861396 @default.
• W3201935557 hasRelatedWork W3184219984 @default.
• W3201935557 hasRelatedWork W3193403845 @default.
• W3201935557 hasRelatedWork W3193541977 @default.
• W3201935557 hasRelatedWork W3204368319 @default.
• W3201935557 hasRelatedWork W45663112 @default.
• W3201935557 isParatext "false" @default.

• next