[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125305-en":3,"doc-seo-125305-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125305,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Multivariate Forecasting of Paddy Production - A Comparative Study of Machine Learning Models","Accurate forecasting of paddy production underpins national food security planning by enabling more reliable decisions. This study compares four machine learning algorithms—Random Forest, XGBoost, Support Vector Regression, and Linear Regression—for jointly predicting three targets: harvest area, productivity, and production. The work uses provincial annual Indonesia data from 2018 to 2024 from BPS and evaluates five metrics (MAE, RMSE, MAPE, R², and training time). Random Forest performs best under the 80:20 setup, while SVR performs worst and Linear Regression shows limited fit. Ensemble approaches, especially Random Forest and XGBoost, support data-driven, proactive agricultural management and intelligent decision-support systems.","Multivariate Forecasting of Paddy Production: A Comparative Study of  \nMachine Learning Models  \nFeri Yasin1, Muhammad Raafi'u Firmansyah*2, Dasril Aldo3, Muhammad Afrizal Amrustian4  \n1,2,3Department of Informatics, Telkom University, Indonesia 4Department of Information and Computer Science, King Fahd University of Petroleumand Minerals,  \nSaudi Arabia  \n[Email:](Email:1raafiu@telkomuniversity.ac.id)[1](Email:1raafiu@telkomuniversity.ac.id)[raafiu@telkomuniversity.ac.id](Email:1raafiu@telkomuniversity.ac.id)  \nReceived : May 3, 2025; Revised : Jun 3, 2025; Accepted : Jun 14, 2025; Published : Jun 23, 2025  \nAbstract  \n\n| Accurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS) . Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259 .52, an RMSE of 154,036 .91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain.\u003Cbr>Keywords : Forecasting, Machine Learning, Multivariate Regression, Paddy Production, Random Forest. |\n| --- |\n| This work is an open access article and licensed under a Creative Commons Attribution-Non Commercial\u003Cbr>4.0 International License\u003Cbr> |\n\n1. INTRODUCTION  \nRice is a strategic commodity related to food security and economic stability in almost all countries, including Indonesia [1], [2], [3] . As an agrarian country, Indonesia prioritizes rice as part of its food policy [4], [5] . Climate change, the rate of land degradation, the adoption of agricultural technology, and government policies are attributes in the variability of rice production [6] . Thus, the development of an accurate rice production forecasting model is essential for food security planning.  \nThe adoption of machines and digitalization, especially machine learning technology [7], [8], has automatically transformed in the last decade in the agricultural sector. Various machine learning algorithms have been applied to improve the accuracy of predictions of agricultural yields [9], [10] . With the use of machine learning, conventional statistical methods are no longer used due to their limitations in handling complex and non-linear data patterns [11], [12] .  \nRandom Forest is one of the algorithms that has shown promising performance in previous studies [13] . Random Forest XGBoost, which is a development of the boosting method, is known for its efficiency as well as accurate prediction results on large an","cbCaio1Rr7t0JR0G","https://ap.wps.com/l/cbCaio1Rr7t0JR0G","pdf",738016,1,12,"English","en",105,"# Introduction\n## Rice production forecasting for food security\n## Machine learning in agricultural yield prediction\n## Motivation for multivariate, comparative modeling\n# Abstract\n## Study objective and target variables\n## Dataset and evaluation metrics\n## Experimental results and best configuration\n## Practical implications and contributions","[{\"question\":\"What is the main goal of the study on paddy production forecasting?\",\"answer\":\"To evaluate and compare four machine learning algorithms for multivariate forecasting of harvest area, productivity, and production simultaneously.\"},{\"question\":\"Which dataset and period are used to train and test the models?\",\"answer\":\"The study uses provincial annual data in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS).\"},{\"question\":\"How were the models evaluated in this research?\",\"answer\":\"Performance was assessed using MAE, RMSE, MAPE, R², and training time, with experiments reported across training/test scenarios such as 80:20.\"}]","Multivariate Forecasting of Paddy Production - A Comparative Study of Machine Learning Models | PDF",1785898082,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multivariate-forecasting-of-paddy-production-a-comparative-study-of-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multivariate-forecasting-of-paddy-production-a-comparative-study-of-machine-learning-models/125305/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on paddy production forecasting?","Question",{"text":75,"@type":76},"To evaluate and compare four machine learning algorithms for multivariate forecasting of harvest area, productivity, and production simultaneously.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and period are used to train and test the models?",{"text":80,"@type":76},"The study uses provincial annual data in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS).",{"name":82,"@type":73,"acceptedAnswer":83},"How were the models evaluated in this research?",{"text":84,"@type":76},"Performance was assessed using MAE, RMSE, MAPE, R², and training time, with experiments reported across training/test scenarios such as 80:20.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]