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The study contributes data-driven insights to enhance digital library service quality. Methods include user surveys, data preprocessing, and Orange Data Mining with Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) to classify satisfaction levels, followed by model comparison. Findings indicate SVM achieves recall 0.587, while precision is higher for SVM and AUC is greater for kNN, indicating optimization needs.","IMPLEMENTATION OF MACHINE LEARNING IN IMPROVING WEBSITE USER EXPERIENCE AND SATISFACTION  \nShiefti Dyah Alyusi*1), Imam Yuadi2)  \n1. Universitas Airlangga, Surabaya  \n2. Universitas Airlangga, Surabaya  \nArticle Info  \nKeywords: Machine Learning; User Satisfaction; Support Vector Machine; K-Nearest Neighbor  \nArticle history:  \nReceived 3 Januari 2025  \nRevised 15 February 2025  \nAccepted 23 February 2025  \nAvailable online 1 Maret 2025  \nDOI :  \n[https://doi.org/10.29100/jipi.v10i1.7439](https://doi.org/10.29100/jipi.v10i1.7439)  \n* Shiefti Dyah Alyusi. Corresponding Author E-mail address:  \n[shiefti.dyah.alyusi-2024@fisip.unair.ac.id](shiefti.dyah.alyusi-2024@fisip.unair.ac.id)  \nABSTRACT  \nThis research aims to analyze user satisfaction in accessing the Airlangga University library website through the application of machine learning algorithms. The benefit of this research is that it provides insight into improving the quality of digital library services based on data-based analysis. The methods used include user surveys, data preprocessing, and application of the Orange Data Mining with models Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) algorithms to classify user satisfaction levels, as well as comparing the results of the two models. The results show that the SVM model is able to achieve a Recall accuracy of 0.587 in identifying user satisfaction, but the precision metric is greater in SVM and the AUC is greater in kNN so it still requires optimization. This research concludes that the application of machine learning, especially SVM, can be an effective tool for improving user experience and providing more precise recommendations for improving library services.  \n.  \nI. INTRODUCTION  \nechnological advances in the current era of globalization are undeniable in their benefits. Almost every  \nTcorner of human life uses technology to meet information needs because of its efficient, effective and easily  \naccessible nature. According to [1] in almost every field of education, the use of technological developments is very necessary. In recent decades, the development of information technology has allowed libraries to  \nprovide access to digital collections more widely, even remotely. Internet technology makes it easy to establish communication and the process of retrieving information through cyberspace becomes very easy [2] .  \nThe influence of information technology developments has an impact on various sectors, including digitalbased library services. In the digital era, online libraries are a relevant solution in meeting the information needs of users. However, the level of user satisfaction with online library services is often not optimal due to constraints such as ineffective interfaces, slow access to information, or lack of relevance in search results. According to [3], evaluation of user satisfaction is urgently needed to improve the quality of digital library services. In addition, [4] shows that traditional approaches in analyzing user satisfaction are often insufficient to capture complex needs and preferences.  \nSeveral previous studies have discussed the use of modern technology to improve library user satisfaction. For example, research by [5] shows that the application of machine learning-based recommendation systems can improve the user experience in finding relevant literature. Similarly, a study from [6] leverages sentiment analysis from user reviews to identify the strengths and weaknesses of library services. Another research by [7] developed a user satisfaction prediction model based on usage patterns and preferences. On the other hand,[8] underlines the importance of service quality by personalizing user needs which can be done in 2 (two) ways, namely assessing personal needs and work unit needs.  \nIn terms of methods, machine learning-based approaches have proven effective in a variety of data analysis contexts. Research by [7] also shows that unsupervised learning methods, such as cluste","cbCaieTNpXSZ3jOy","https://ap.wps.com/l/cbCaieTNpXSZ3jOy","pdf",597030,1,"English","en",105,"# Introduction\n## Background of Digital Library Services and User Satisfaction\n## Related Work on Machine Learning for Library Experience\n# Research Methods\n## Data Collection and Preprocessing\n## Classification Using SVM and kNN\n## Model Comparison","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To analyze user satisfaction with the Airlangga University library website by applying machine learning algorithms and using the results to identify ways to improve digital library services.\"},{\"question\":\"Which machine learning models are used to classify user satisfaction?\",\"answer\":\"The research applies Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) using Orange Data Mining, after survey data preprocessing.\"},{\"question\":\"How do the models perform based on the reported evaluation metrics?\",\"answer\":\"SVM reaches recall accuracy of 0.587 for identifying user satisfaction, while precision is higher for SVM; kNN provides a higher AUC, so further optimization is still required.\"}]","Implementation of Machine Learning in Improving Website User Experience and Satisfaction | PDF",1785734348,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"implementation-of-machine-learning-in-improving-website-user-experience-and-satisfaction","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/implementation-of-machine-learning-in-improving-website-user-experience-and-satisfaction/121205/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the research?","Question",{"text":74,"@type":75},"To analyze user satisfaction with the Airlangga University library website by applying machine learning algorithms and using the results to identify ways to improve digital library services.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning models are used to classify user satisfaction?",{"text":79,"@type":75},"The research applies Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) using Orange Data Mining, after survey data preprocessing.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the models perform based on the reported evaluation metrics?",{"text":83,"@type":75},"SVM reaches recall accuracy of 0.587 for identifying user satisfaction, while precision is higher for SVM; 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