[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117986-id":3,"doc-seo-117986-113":30,"detail-sidebar-cat-0-id-113":84},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":20,"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},117986,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK ANALISIS SENTIMEN PADA ULASAN HOTEL - ABSTRAK DAN HASIL","Penelitian membandingkan algoritma machine learning untuk analisis sentimen pada ulasan hotel di industri pariwisata dan perhotelan. Ulasan yang akurat mendukung pemanfaatan kecerdasan buatan guna meningkatkan efisiensi operasional, mengoptimalkan pendapatan, serta memperbaiki kepuasan pelanggan. Metode meliputi data scraping, pembersihan, praproses, pelabelan, lalu pelatihan dan pengujian model. Algoritma supervised yang disorot adalah Gradient Boosting, Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN). Hasil menunjukkan SVM paling unggul dengan akurasi 0.8553, precision 0.8433, recall 0.8553, dan F1-score 0.8424, sehingga direkomendasikan untuk penerapan analisis sentimen pada ulasan hotel.","PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK ANALISIS SENTIMEN PADA ULASAN HOTEL  \nViktor Handrianus Pranatawijaya a,1,*, Efrans Christian b,2  \nab Universitas Palangka Raya, Kampus Tunjung Nyaho Jalan Yos Sudarso, Palangka Raya, Kalimantan Tengah, Indonesia [1](1 viktorhp@it.upr.ac.id)[ viktorhp@it.upr.ac.id](1 viktorhp@it.upr.ac.id)*; [2](2 efrans@it.upr.ac.id)[ efrans@it.upr.ac.id](2 efrans@it.upr.ac.id);3 Email penulis ketiga (9pt)  \n* corresponding author  \nARTICLE INFO  \nABSTRACT  \n\n| Keywords\u003Cbr>Machine Learning. Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) | The paper extensively explores machine learning algorithms for evaluating sentiments in hotel reviews, particularly within the tourism and hospitality industry. It underscores the importance of precise reviews in utilizing artificial intelligence for improved operational efficiency, revenue optimization, and heightened customer satisfaction. Notably, supervised machine learning algorithms like Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor are highlighted for offering recommendations based on reviews to predict user preferences. The research methodology involves data scraping, cleaning, preprocessing, and labeling, followed by training and testing the chosen machine learning algorithms. Results indicate that the Support Vector Machine algorithm demonstrated superior performance with accuracy 0.8553, precision 0.8433, recall 0.8553, dan F1-score 0.8424, suggesting its appropriateness for sentiment analysis in hotel reviews. The paper concludes by recommending the implementation of the Support Vector Machine model for sentiment analysis in hotel reviews in Palangka Raya, Indonesia, and proposes avenues for further industry development and enhancement. |\n| --- | --- |\n\n1. Pendahuluan  \nIndustri pariwisata dan perhotelan, yang menarik jutaan orang di seluruh dunia, telah berkembang menjadi salah satu subsektor yang sangat penting dalam industri jasa. Menyusun dan merencanakan perjalanan wisata melibatkan banyak pertimbangan, terutama dalam memilih destinasi yang menarik dan memilih akomodasi yang tepat. Komentar wisatawan juga sangat pentinguntukmemilih akomodasiterbaik [1] .  \nKeakuratan penilaian yang dibuat melalui ulasan ini sangat penting karena berdampak padakeputusan yang dibuat oleh wisatawan. Industri pariwisata dan perhotelan menggunakan kecerdasan buatan untuk mengatasi kompleksitas ini dengan tujuan meningkatkan efisiensi operasional, mengoptimalkan pendapatan, dan memberikan pengalaman wisatawan yang lebih memuaskan [2] . Namun, masalah dengan menilai kecerdasan buatan dengan benar masih menjadi masalah besar [3] .  \nSalah satu yang dapat digunakan dengan supervised Machine Learning (SML) adalah untuk memberikan rekomendasi kepada pengguna berdasarkan ulasan. Hal ini mencakup pengembangan ide untuk memilih algoritma terbaik setelah pelatihan dan pengujian. Dalam hal ini, algoritma seperti Gradient Boosting (GB), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM) sangat membantu mengantisipasi preferensi pengguna terhadap ulasan.  \nKNN, yang sering digunakan dalam masalah klasifikasi dan regresi, adalah salah satu algoritmayang sederhana namun berhasil [4] . Sangat populer untuk mendukung keputusan berbasis opini karenakemampuan untuk memprediksi berdasarkan data terdekat. Selain itu, terbukti bahwa SVM [5] . membantu pengguna melakukan transaksi informasi, terutama dalam hal rekomendasi berbasis ulasan. Dalam teori pembelajaran, algoritma GM menghasilkan pengklasifikasi kombinasi yang kuat dengan menggabungkan sekelompok pengklasifikasi yang kurang akurat dengan pohon keputusan [6] .  \nGM adalah  \npilihan yang bagus untuk meningkatkan akurasi dalam hal rekomendasi berbasis ulasan karena keunggulan ini.  \nPenerapan algoritma ini sangat terasa, terutama dalam meningkatkan kemampuan pengklasifikasi yang tidak begitu tepat, seperti pohon keputusan. Dengan demikian, kecerdasan buatan dalam SMLmembantu men","cbCaineeWgL5Q4Jt","https://ap.wps.com/l/cbCaineeWgL5Q4Jt","pdf",488477,8,1,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang industri pariwisata dan perhotelan\n## Kecerdasan buatan untuk penilaian ulasan\n## Supervised machine learning dan rekomendasi berbasis ulasan\n## Perbandingan algoritma (KNN, SVM, Gradient Boosting)","[{\"question\":\"Bagaimana alur metodologi penelitian dilakukan?\",\"answer\":\"Metodologi meliputi data scraping, pembersihan, preprocessing, dan labeling, kemudian proses training dan testing terhadap algoritma machine learning yang dipilih.\"}]","PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK ANALISIS SENTIMEN PADA ULASAN HOTEL - ABSTRAK DAN HASIL | PDF",1785680644,12,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":28},"comparison-of-machine-learning-algorithms-for-sentiment-analysis-on-hotel-reviews-abstract-and-results","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/id/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/id/document/comparison-of-machine-learning-algorithms-for-sentiment-analysis-on-hotel-reviews-abstract-and-results/117986/",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","2026-08-17","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Bagaimana alur metodologi penelitian dilakukan?","Question",{"text":76,"@type":77},"Metodologi meliputi data scraping, pembersihan, preprocessing, dan labeling, kemudian proses training dan testing terhadap algoritma machine learning yang dipilih.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,91,95,99,103,107,109,113,117,121,125],{"id":87,"doc_module":4,"doc_module_name":46,"category_name":88,"show_sort_weight":89,"slug":90},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":89,"slug":94},48,"Cerita & Novel","story-novel",{"id":96,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":89,"slug":98},56,"Gaya Hidup","lifestyle",{"id":100,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":89,"slug":102},51,"Komik","comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":89,"slug":106},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":89,"slug":108},"research-report",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":89,"slug":112},49,"Sastra","literature",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":89,"slug":116},52,"Teknologi","technology",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":89,"slug":120},50,"Ujian","exam",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":89,"slug":124},57,"Umum","general",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":4,"slug":128},181,"Formulir","formulir"]