[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125386-en":3,"doc-seo-125386-105":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},125386,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Optimizing Machine Learning in Hospitality Industry - Implementation of Random Forest Model in Forecasting Hotel Guest Length of Stay","This study applies the Random Forest algorithm to predict hotel guests’ length of stay (LoS), a key metric for improving operational efficiency and revenue management. It addresses challenges from fluctuating demand, varied customer preferences, and changing market conditions through predictive analytics. A dataset of 453 guest accounts is analyzed using preprocessing such as standardization, feature selection, and training/testing splits. Random Forest handles non-linear patterns and high-dimensional inputs, achieving MSE 1.89, MAE 0.80, and RMSE 1.37. Ratings and guest types emerge as the most influential predictors.","Optimizing Machine Learning in Hospitality Industry: Implementation of Random Forest Model in Forecasting Hotel Guest Length of Stay  \nYerikAfrianto Singgalen1*  \nTourism Department, Faculty of Business Administration and Communication, Atma Jaya Catholic University of Indonesia 1  \nAbstract  \nThis study explores the application of the Random Forest algorithm in predicting the length of stay (LoS) of hotel guests, a critical metric for optimizing operational efficiency and revenue management in the hospitality industry. The research is grounded in the growing need for predictive analytics to address challenges posed by fluctuating demand, diverse customer preferences, and dynamic market conditions.  \nAccurate LoS predictions allow for better resource allocation, enhanced guest experiences, and optimized pricing strategies, making this study highly relevant for advancing data-driven decision-making in the sector. The methodology involved analyzing a dataset of 453 accounts, which included key features such as ratings, guest types, room preferences, and country of origin. Comprehensive data preprocessing steps, including standardization, feature selection, and dataset splitting into training and testing subsets, ensured the reliability and robustness of the predictive model. The Random Forest algorithm, known for its ability to handle non-linear relationships and high-dimensional data, was implemented to analyze patterns and relationships. The model demonstrated high accuracy, achieving a Mean Squared Error (MSE) of 1.89, Mean Absolute Error (MAE) of 0.80, and Root Mean Squared Error (RMSE) of 1.37, effectively capturing the complexity of the dataset. The findings reveal that ratings and guest types are the most influential predictors, underscoring their importance in shaping guest behaviors. While the results are promising, limitations such as dataset size and scope suggest opportunities for further research. Future studies could incorporate more extensive, diverse datasets and explore alternative algorithms to enhance predictive accuracy and adaptability. This research contributes to advancing machine learning applications in hospitality, providing actionable insights to improve operational performance, guest satisfaction, and competitive positioning.  \nKeywords: Forecasting, Hospitality Industry, Random Forest, Machine Learning  \nA. INTRODUCTION  \nThe hospitality industry's increasing complexity in customer behavior demands advanced predictive models for operational optimization and enhanced guest experiences. Forecasting hotel guests'length of stay through machine learning, particularly the Random Forest model, provides essential resource management and revenue optimization (Ampountolas & Legg, 2021)insights. This model's robustness in handling non-linear data relationships and capacity to process large volumes of heterogeneous data makes it a practical solution for addressing fluctuating demand patterns and diverse customer preferences (Hamdan & Othman, 2022). he integration of data-driven methodologies transforms traditional frameworks, providing actionable insights in a highly competitive market while establishing a foundation for sustainable and efficient operations in the hospitality sector.  \nThe urgency of this research stems from modern industries' growing need for advanced analytical tools to address unpredictability and complexity. The expanding volume of data and demand for precise decision-making necessitates innovative methodologies for extracting meaningful insights (Dang & Nguyen, 2024; Nassif et al., 2022; Sharma & Aggarwal, 2020). The Random Forest model demonstrates  \nparticular value through its ability to handle diverse variablesand provide accurate predictions, especially in the hospitality sector, where customer behavior significantly impacts operations (Darvishmotevali et al., 2024; Singh, 2022; Yoo et al., 2024). Organizations that fail to implement these predictive systems risk operational inefficien","cbCaikEMQ2jv4X1g","https://ap.wps.com/l/cbCaikEMQ2jv4X1g","pdf",697047,1,15,"English","en",105,"# Abstract\n# A. INTRODUCTION","[{\"question\":\"What is the goal of the study in the hospitality industry?\",\"answer\":\"The study aims to enhance hotel operations and revenue management by forecasting guests’ length of stay using machine learning, specifically the Random Forest model.\"},{\"question\":\"Which data and features are used to build the Random Forest model?\",\"answer\":\"The model is trained on a dataset of 453 accounts with features including ratings, guest types, room preferences, and country of origin.\"},{\"question\":\"How well does the Random Forest model perform in predicting length of stay?\",\"answer\":\"The results report MSE of 1.89, MAE of 0.80, and RMSE of 1.37, indicating accurate capture of dataset complexity.\"}]","Optimizing Machine Learning in Hospitality Industry - Implementation of Random Forest Model in Forecasting Hotel Guest Length of Stay | PDF",1785898599,38,{"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},"optimizing-machine-learning-in-hospitality-industry-implementation-of-random-forest-model-in-forecasting-hotel-guest-length-of-stay","",{"@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/optimizing-machine-learning-in-hospitality-industry-implementation-of-random-forest-model-in-forecasting-hotel-guest-length-of-stay/125386/",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 goal of the study in the hospitality industry?","Question",{"text":75,"@type":76},"The study aims to enhance hotel operations and revenue management by forecasting guests’ length of stay using machine learning, specifically the Random Forest model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data and features are used to build the Random Forest model?",{"text":80,"@type":76},"The model is trained on a dataset of 453 accounts with features including ratings, guest types, room preferences, and country of origin.",{"name":82,"@type":73,"acceptedAnswer":83},"How well does the Random Forest model perform in predicting length of stay?",{"text":84,"@type":76},"The results report MSE of 1.89, MAE of 0.80, and RMSE of 1.37, indicating accurate capture of dataset complexity.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]