{"id":1639,"date":"2025-09-17T13:08:10","date_gmt":"2025-09-17T06:08:10","guid":{"rendered":"https:\/\/www.its.ac.id\/publikasi\/?p=1639"},"modified":"2025-09-17T13:08:10","modified_gmt":"2025-09-17T06:08:10","slug":"prediction-of-rice-productivity-using-the-random-forest-regression-algorithm-in-cikaret-subdistrict-for-the-years-2020-2024","status":"publish","type":"post","link":"https:\/\/www.its.ac.id\/publikasi\/2025\/09\/17\/prediction-of-rice-productivity-using-the-random-forest-regression-algorithm-in-cikaret-subdistrict-for-the-years-2020-2024\/","title":{"rendered":"Prediction of Rice Productivity Using the Random Forest Regression Algorithm in Cikaret Subdistrict for the Years 2020-2024"},"content":{"rendered":"<section class=\"item authors\">\n<ul class=\"authors\">\n<li><span class=\"name\">Muhammad Firdaus<\/span><span class=\"affiliation\">Universitas Diponegoro<\/span><\/li>\n<li><span class=\"name\">Firman Hadi<\/span><span class=\"affiliation\">Universitas Diponegoro<\/span><\/li>\n<li><span class=\"name\">L M Sabri<\/span><span class=\"affiliation\">Universitas Diponegoro<\/span><\/li>\n<\/ul>\n<\/section>\n<section class=\"item doi\">\n<h2 class=\"label\">DOI:<\/h2>\n<p><span class=\"value\"><a href=\"https:\/\/doi.org\/10.12962\/geoid.v20i1.2570\">https:\/\/doi.org\/10.12962\/geoid.v20i1.2570<\/a><\/span><\/section>\n<section class=\"item keywords\">\n<h2 class=\"label\">Keywords:<\/h2>\n<p><span class=\"value\">challanges, forecasting, MAPE, MSE, RMSE<\/span><\/section>\n<section class=\"item abstract\">\n<h2 class=\"label\">Abstract<\/h2>\n<p>The challenges surrounding rice productivity in Indonesia are growing more complex due to factors like climate change, population growth, and limited agricultural land. As the primary food source and main carbohydrate provider, rice is crucial for the majority of Indonesians. This study focuses on predicting rice productivity using the random forest regression algorithm, incorporating predictor variables such as NDVI, NDMI, land area, land surface temperature, rainfall, fertilizer type, and pests. To ensure the accuracy of the model, multicollinearity tests were conducted to check for strong correlations among the independent variables. The tests confirmed the absence of significant linear relationships, allowing all variables to be included in the model. The prediction model was built using time-series data from 2020 to 2023, resulting in 840 samples after eliminating outliers. The optimization process targeted the mtry parameter and the number of decision trees to reduce prediction error. The optimal model, utilizing 7 predictor features and 150 decision trees, achieved a low out of bag (OOB) error and stable mean square error (MSE). Model performance metrics showed a Mean Absolute Error (MAE) of 0.324 tons\/hectare, MSE of 0.158 tons\/hectare, Root Mean Square Error (RMSE) of 0.398 tons\/hectare, and a coefficient of determination (R\u00b2) of 0.87. These results demonstrated that the random forest regression algorithm is highly effective in predicting rice productivity, particularly when dealing with complex data involving multiple predictor variables and potential multicollinearity.<\/p>\n<p><a href=\"https:\/\/journal.its.ac.id\/index.php\/geoid\/article\/view\/2570\">PDF<\/a><\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Muhammad FirdausUniversitas Diponegoro Firman HadiUniversitas Diponegoro L M SabriUniversitas Diponegoro DOI: https:\/\/doi.org\/10.12962\/geoid.v20i1.2570 Keywords: challanges, forecasting, MAPE, MSE, RMSE Abstract The challenges surrounding rice productivity in Indonesia are growing more complex due to factors like climate change, population growth, and limited agricultural land. As the primary food source and main carbohydrate provider, rice is crucial for [&hellip;]<\/p>\n","protected":false},"author":250,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[91],"tags":[],"class_list":["post-1639","post","type-post","status-publish","format-standard","hentry","category-geoid-jurnal"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prediction of Rice Productivity Using the Random Forest Regression Algorithm in Cikaret Subdistrict for the Years 2020-2024 - IJC ( ITS Journal Center )<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.its.ac.id\/publikasi\/2025\/09\/17\/prediction-of-rice-productivity-using-the-random-forest-regression-algorithm-in-cikaret-subdistrict-for-the-years-2020-2024\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction of Rice Productivity Using the Random Forest Regression Algorithm in Cikaret Subdistrict for the Years 2020-2024 - IJC ( ITS Journal Center )\" \/>\n<meta property=\"og:description\" content=\"Muhammad FirdausUniversitas Diponegoro Firman HadiUniversitas Diponegoro L M SabriUniversitas Diponegoro DOI: https:\/\/doi.org\/10.12962\/geoid.v20i1.2570 Keywords: challanges, forecasting, MAPE, MSE, RMSE Abstract The challenges surrounding rice productivity in Indonesia are growing more complex due to factors like climate change, population growth, and limited agricultural land. 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