[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127424-en":3,"doc-seo-127424-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127424,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization","Shallots (Allium cepa L.) are a major Indonesian horticultural commodity whose monthly prices fluctuate and create losses for farmers and disadvantages for consumers. Accurate forecasting supports planning, policy making, and macro-level economic stability. This research develops and optimizes a shallot price forecasting model using random forest regression, leveraging ensemble learning of multiple decision trees to model non-linear price dynamics with improved stability and accuracy.","Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization  \nMuhammad Naufal Rauf Ibrahim1*  \n1Department of Agricultural and Biosystem Engineering, Faculty of Agroindustrial Technology, Padjadjaran University, Jl. Ir. Soekarno KM.21, Sumedang, West Java 54363 Indonesia.  \n*Corresponding author, email: [rauf.ibrahim@unpad.ac.id](rauf.ibrahim@unpad.ac.id)  \n\n| Article Info | Abstract |\n| --- | --- |\n| \u003Cbr>Submitted: 17 July 2025 | \u003Cbr>Shallots (Allium cepa L.) are a major horticultural commodity in Indonesia, with |\n| \u003Cbr>Revised: 17 September 2025\u003Cbr>Accepted: 22 September 2025\u003Cbr>Available online: 9 October 2025 | \u003Cbr>a production of 1.98 million tons in 2022, representing 13.59% of the total national vegetable production. Accurate forecasting of agricultural commodity |\n| \u003Cbr>Published: September 2025 | \u003Cbr>prices is fundamental to sustainable development in the agricultural sector and |\n| \u003Cbr>Keywords:\u003Cbr>Machine Learning; Price Forecasting; Random forest regression; Shallot. | \u003Cbr>contributes to broader economic stability. This study uses the random forest regression algorithm, a supervised machine learning technique that utilizes ensemble learning to combine multiple decision trees. This approach offers advantages in modeling non-linear relationships for agricultural price prediction |\n| \u003Cbr>How to cite: | \u003Cbr>while also reducing the risk of overfitting, resulting in more accurate and stable |\n| \u003Cbr>Ibrahim, M. N. R. (2025). Price | \u003Cbr>forecasts compared to individual decision trees. The purpose of this research is to |\n| \u003Cbr>Forecasting of Shallots Using the | \u003Cbr>develop and optimize a shallot price forecasting model using random forest |\n| \u003Cbr>Machine Learning Approach of Random | \u003Cbr>regression. The optimized model, using 50 decision tree estimators, successfully |\n| \u003Cbr>Forest Regression Supporting Price | \u003Cbr>predicted up to 15 months ahead of monthly prices and achieved an RMSE of |\n| \u003Cbr>Stabilization. Jurnal Keteknikan | \u003Cbr>2363.15 and a MAPE of 8.71% in validation, then a MAPE of 10.31% in test |\n| \u003Cbr>Pertanian, 13(3): 449-461. | \u003Cbr>evaluation. |\n| \u003Cbr>[https://doi.org/](https://doi.org/10.19028/jtep.013.3.449-)[10.19028/jtep.013.3.449-](https://doi.org/10.19028/jtep.013.3.449-)[ ](https://doi.org/10.19028/jtep.013.3.449-)[461.](461.) |  |\n\nDoi: [https://doi.org/10.19028/jtep.013.3.449-461](https://doi.org/10.19028/jtep.013.3.449-461)  \n1. Introduction  \nShallots (Allium cepa L) are one of the largest horticultural commodities in Indonesia, with production reaching 1.98 million tons in 2022, accounting for 13.59% of total vegetable production (BPS, 2024). National shallot consumption in 2022 reached 890 thousand tons and continues to increase by an average of 3.88% per year (Pusdatin Kementan, 2023). The existence of shallots plays a vital role in influencing the economy and creating job opportunities in Indonesia. The survey show that each hectare of shallot farmland can create approximately 290 workdays.(Wandschneider et al., 2013) .  \nThe forecasting of agricultural commodity prices serves as an important tool for sustainable development in the agricultural economy and broader economic stability. The ability to predict price movements enables farmers to make informed decisions about when to plant and sell their crops, potentially allowing them to switch between commodities and alternative markets to ensure favorable prices and maximize income. The price of shallots at the market fluctuates every month, and farmers  \noften suffer losses due to falling selling prices during the harvest season. On the other hand, consumers also feel disadvantaged when the price of shallots soars during periods of low availability. The fluctuating price have a significant impact, especially on people with low incomes (Matondang et al., 2024). Price forecasting is not only important for farmers and consumers but also provides future information on agricultural c","cbCainblF8Du7L38","https://ap.wps.com/l/cbCainblF8Du7L38","pdf",680061,1,13,"English","en",105,"# Introduction\n# Material and Methods\n## Data Acquisition and Preprocessing\n## Training, Validation, and Testing of the Random Forest Regression Model","[{\"question\":\"Why is shallot price forecasting important in Indonesia?\",\"answer\":\"Shallots are a major horticultural commodity and their monthly prices fluctuate, causing farmers’ losses during harvest season and disadvantaging consumers when prices rise. Forecasting also provides information that can help the government formulate policies to stabilize prices.\"},{\"question\":\"What modeling approach does the study use for forecasting?\",\"answer\":\"The study uses random forest regression, a supervised machine learning ensemble method that combines predictions from multiple decision trees to capture non-linear relationships in agricultural price data.\"},{\"question\":\"How is the forecasting model optimized and evaluated?\",\"answer\":\"The research tests different numbers of estimators to find an optimal configuration, using an ensemble of 50 decision tree estimators. Model performance is validated with metrics including RMSE and MAPE, and then further assessed on a test set using MAPE.\"}]","Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization | PDF",1785938804,33,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"price-forecasting-of-shallots-using-the-machine-learning-approach-of-random-forest-regression-supporting-price-stabilization","",{"@graph":36,"@context":86},[37,54,69],{"@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/price-forecasting-of-shallots-using-the-machine-learning-approach-of-random-forest-regression-supporting-price-stabilization/127424/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is shallot price forecasting important in Indonesia?","Question",{"text":76,"@type":77},"Shallots are a major horticultural commodity and their monthly prices fluctuate, causing farmers’ losses during harvest season and disadvantaging consumers when prices rise. Forecasting also provides information that can help the government formulate policies to stabilize prices.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What modeling approach does the study use for forecasting?",{"text":81,"@type":77},"The study uses random forest regression, a supervised machine learning ensemble method that combines predictions from multiple decision trees to capture non-linear relationships in agricultural price data.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the forecasting model optimized and evaluated?",{"text":85,"@type":77},"The research tests different numbers of estimators to find an optimal configuration, using an ensemble of 50 decision tree estimators. Model performance is validated with metrics including RMSE and MAPE, and then further assessed on a test set using MAPE.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]