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Data berasal dari ulasan untuk 11 rumah sakit, melalui preprocessing agar siap dianalisis. Ulasan diklasifikasikan menjadi sentimen positif, negatif, dan netral menggunakan VADER, serta kategori emosi seperti marah, senang, sedih menggunakan NRC Lexicon. Model KNN, Logistic Regression, dan Decision Tree dievaluasi; Decision Tree mencapai akurasi tertinggi 92%.","Analisis Sentimen dan Emosi dari Ulasan Google Maps untuk Layanan Rumah Sakit di PalangkaRaya Menggunakan Machine Learning  \nA C T Angel1, V H Pranatawijaya2, Widiatry3  \n1-3 Program Studi Teknik Informatika, Universitas Palangka Raya  \nE-mail: [christyanaaprilia@mhs.eng.upr.ac.id](christyanaaprilia@mhs.eng.upr.ac.id1)[1](christyanaaprilia@mhs.eng.upr.ac.id1), [viktorhp@it.upr.ac.id](viktorhp@it.upr.ac.id2)[2](viktorhp@it.upr.ac.id2),  \n[widiatry@it.upr.ac.id](widiatry@it.upr.ac.id3)[3](widiatry@it.upr.ac.id3)  \nAbstrak. Penelitian ini bertujuan untuk menganalisis sentimen dan emosi dari ulasan Google Mapsuntuk layanan rumah sakit di Palangka Raya menggunakan machine learning. Data yang digunakandalam penelitian ini adalah ulasan dari Google Maps untuk 11 rumah sakit di Palangka Raya. Data diolah dengan preprocessing untuk membersihkan dan mempersiapkan data untuk analisis. Selanjutnya, data diklasifikasikan berdasarkan sentimen (positif, negatif, netral) dengan VADER (Valence Aware Dictionary and Sentiment Reasoner) dan emosi (seperti marah, senang, sedih, dll) menggunakan NRC Lexicon. Algoritma yang digunakan dalam penelitian ini adalah K-Nearest Neighbors (KNN), Logistic Regression, dan Decision Tree. Hasil penelitian menunjukkan bahwaketiga algoritma tersebut memiliki performa yang berbeda-beda ketika mengklasifikasikan sentimen dan emosi dari ulasan. Algoritma Decision Tree memiliki akurasi tertinggi yaitu 92%, diikuti dengan Logistic Regression dengan akurasi 86%, dan KNN dengan akurasi 48% . Penelitian ini menunjukkan bahwa machine learning dapat digunakan untuk menganalisis sentimen dan emosidari ulasan pada Google Maps dengan baik.  \nKata kunci: analisis sentimen; emosi; ulasan Google Maps; layanan rumah sakit; Machine Learning  \nAbstract. This research aims to analyze the sentiment and emotion from reviews on Google Maps for hospital services in Palangka Raya using machine learning. The data used in this research was reviews from Google Maps for 11 hospitals in Palangka Raya. The data was processed using preprocessing to clean and prepare the data for analysis. Furthermore, the data was classified based on the sentiments (positive, negative, neutral) with VADER (Valence Aware Dictionary and Sentiment Reasoner) and emotions (such as angry, happy, sad, etc.) using NRC Lexicon. The algorithms used in this research are K-Nearest Neighbors (KNN), Logistic Regression, and Decision Tree. The research results show that the three algorithms have different performances when classifying sentiment and emotion from reviews. The Decision Tree algorithm has the highest accuracy of 92%, followed by Logistic Regression with an accuracy of 86%, and KNN with an accuracy of 48%. This research shows that machine learning can be used to analyze sentiment and emotion from reviews on Google Maps well.  \nKeywords: sentiment analysis; emotion; Google Maps Reviews; hospital services; Machine Learning  \n1. Pendahuluan  \nSaat ini masyarakat dengan lebih mudah mendapatkan informasi, termasuk informasi tentang layanankesehatan melalui platform digital. Salah satu platform yang populer untuk mencari informasi ini yaitu Google Maps. Ulasan-ulasan yang tersedia di Google Maps menjadi sumber informasi penting bagi calonpasien dalam memilih Rumah Sakit yang tepat. Ulasan tersebut mengandung sentimen dan emosi yang mencerminkan pengalaman dan kepuasan pasien.  \nCalon pasien dapat membaca ulasan untuk mengetahui kelebihan dan kekurangan layanan Rumah Sakit sebelum memutuskan untuk berobat. Kualitas layanan kesehatan yang baik membutuhkan keseimbangan antara aspek teknis medis dan sosial & sistem. Pada teknis medis berfokus pada interaksi antara tenaga medis dan pasien yang meliputi diagnosis, pengobatan, perawatan, dan pelayanan. Untuk sosial dan sistem mencakup faktor-faktor di luar interaksi medis, seperti peralatan, dan fasilitas [1] . Ulasan Google Maps merupakan opini yang berharga bagi pengguna lain seperti kualitas pelayanan, suasana, dan fasilita","cbCaiivstBejiUsy","https://ap.wps.com/l/cbCaiivstBejiUsy","pdf",438768,3,1,15,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan kebutuhan analisis\n## Konsep analisis sentimen dan machine learning\n# Metode\n## Pra-pemrosesan data dan persiapan analisis\n## Ekstraksi sentimen dengan VADER\n## Ekstraksi emosi dengan NRC Lexicon\n## Model klasifikasi dan evaluasi kinerja","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Penelitian ini bertujuan menganalisis sentimen dan emosi dari ulasan Google Maps untuk layanan rumah sakit di Palangka Raya menggunakan machine learning.\"},{\"question\":\"Bagaimana data dan fitur dianalisis pada penelitian ini?\",\"answer\":\"Ulasan dari Google Maps untuk 11 rumah sakit dipreprocessing untuk dibersihkan dan disiapkan, lalu diklasifikasikan berdasarkan sentimen memakai VADER dan emosi memakai NRC Lexicon.\"},{\"question\":\"Algoritma machine learning apa saja yang dibandingkan, dan bagaimana hasil akurasinya?\",\"answer\":\"Algoritma yang digunakan adalah KNN, Logistic Regression, dan Decision Tree. Decision Tree memiliki akurasi tertinggi 92%, disusul Logistic Regression 86%, dan KNN 48%.\"}]","Analisis Sentimen dan Emosi dari Ulasan Google Maps untuk Layanan Rumah Sakit di Palangka Raya Menggunakan Machine Learning | PDF",1785808766,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"sentiment-and-emotion-analysis-from-google-maps-reviews-for-hospital-services-in-palangka-raya-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/sentiment-and-emotion-analysis-from-google-maps-reviews-for-hospital-services-in-palangka-raya-using-machine-learning/122089/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-18","2026-08-04",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},"Apa tujuan penelitian ini?","Question",{"text":76,"@type":77},"Penelitian ini bertujuan menganalisis sentimen dan emosi dari ulasan Google Maps untuk layanan rumah sakit di Palangka Raya menggunakan machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana data dan fitur dianalisis pada penelitian ini?",{"text":81,"@type":77},"Ulasan dari Google Maps untuk 11 rumah sakit dipreprocessing untuk dibersihkan dan disiapkan, lalu diklasifikasikan berdasarkan sentimen memakai VADER dan emosi memakai NRC Lexicon.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma machine learning apa saja yang dibandingkan, dan bagaimana hasil akurasinya?",{"text":85,"@type":77},"Algoritma yang digunakan adalah KNN, Logistic Regression, dan Decision Tree. Decision Tree memiliki akurasi tertinggi 92%, disusul Logistic Regression 86%, dan KNN 48%.","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":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]