[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-117441-113":3,"detail-sidebar-cat-0-id-113":80,"doc-detail-117441-id":127},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},113,"id","perancangan-model-text-emotion-recognition-menggunakan-algoritma-classification-machine-learning","Perancangan Model Text Emotion Recognition Menggunakan Algoritma Classification Machine Learning","","Text Emotion Recognition adalah proses otomatis mengenali dan mengklasifikasikan emosi dalam teks dengan teknik machine learning. Penelitian ini merancang model klasifikasi teks emosi menggunakan beberapa algoritma classification machine learning. Data teks dipetakan ke enam emosi utama: sadness, joy, love, anger, fear, dan surprise. Preprocessing mencakup tokenisasi, stop word removal, serta lemmatization, sementara representasi fitur memakai TF-IDF. Lima algoritma dievaluasi untuk pelatihan dan pengujian.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/id/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/id/document/penelitian-laporan/","Penelitian & Laporan",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/id/document/perancangan-model-text-emotion-recognition-menggunakan-algoritma-classification-machine-learning/117441/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/perancangan-model-text-emotion-recognition-menggunakan-algoritma-classification-machine-learning/117441.png","ImageObject",300,407,{"name":42,"@type":43},"Miles","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-20","2026-08-02",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Penelitian ini bertujuan untuk apa?","Question",{"text":62,"@type":63},"Penelitian ini bertujuan merancang model klasifikasi teks emosi menggunakan berbagai algoritma classification machine learning.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Bagaimana data teks dipersiapkan sebelum pelatihan model?",{"text":67,"@type":63},"Tahapan preprocessing meliputi tokenisasi, stop word removal, dan lemmatization.",{"name":69,"@type":60,"acceptedAnswer":70},"Algoritma apa yang menghasilkan performa terbaik dan berapa akurasinya?",{"text":71,"@type":63},"Model SVM dan Random Forest memberikan performa terbaik dengan akurasi mencapai 85% pada data uji.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},117441,1785675888,{"code":4,"msg":81,"data":82},"success",[83,88,92,96,100,104,107,111,115,119,123],{"id":84,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":86,"slug":91},48,"Cerita & Novel","story-novel",{"id":93,"doc_module":4,"doc_module_name":25,"category_name":94,"show_sort_weight":86,"slug":95},56,"Gaya Hidup","lifestyle",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":86,"slug":99},51,"Komik","comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":86,"slug":103},53,"Layanan Kesehatan","healthcare",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":106},54,"research-report",{"id":108,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":86,"slug":110},49,"Sastra","literature",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":86,"slug":114},52,"Teknologi","technology",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":86,"slug":118},50,"Ujian","exam",{"id":120,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":86,"slug":122},57,"Umum","general",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},181,"Formulir","formulir",{"code":4,"msg":81,"data":128},{"doc_id":78,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":4,"category_id":105,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":30,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":79,"read_time":140},5909887254083,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Perancangan Model Text Emotion Recognition Menggunakan Algoritma  \nClassification Machine Learning  \nOleh: Dorkas Tri Oktavena Soebroto  \nNIM: 672020127  \nFakultas Teknologi Informasi Universitas Kristen Satya Wacana 2025  \nAbstrak  \nText Emotion Recognition merupakan proses identifikasi dan klasifikasi emosi dalam teks secaraotomatis menggunakan teknik machine learning. Penelitian ini bertujuan untuk merancang model klasifikasi teks emosi menggunakan berbagai algoritma classification machine learning. Data teksdikategorikan ke dalam enam emosi utama: sadness, joy, love, anger, fear, dan surprise. Tahapan preprocessing meliputi tokenisasi, stop word removal, dan lemmatization, sedangkan fitur teksdirepresentasikan dalam bentuk numerik menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) . Lima algoritma machine learning, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, Logistic Regression, dan MultinomialNaïve Bayes, digunakan dalampelatihan dan evaluasi model. Hasil penelitian menunjukkan bahwa model SVM dan Random Forest memberikan performa terbaik dengan akurasi mencapai 85% pada data uji. Dengan demikian, pendekatan machine learning, khususnya SVM dan Random Forest, terbukti efektif dalam klasifikasiteks emosi dan dapat diterapkan dalam berbagai aplikasi analisis sentimen.  \nKata kunci: Text Emotion Recognition; Machine Learning; Klasifikasi Teks; TF-IDF.  \nAbstract  \nText Emotion Recognition is the process of automatically identifying and classifying emotions in text using machine learning techniques. This study aims to design an emotion text classification model using various classification machine learning algorithms. Text data is categorized into six primary emotions: sadness, joy, love, anger, fear, and surprise. The preprocessing stages include tokenization, stop word removal, and lemmatization, while text features are represented numerically using Term FrequencyInverse Document Frequency (TF-IDF). Five machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, Logistic Regression, and Multinomial Naïve Bayes—are utilized for model training and evaluation. The results indicate that the SVMand Random Forest models achieve the best performance, with an accuracy of up to 85% on the test data. Thus, machine learning approaches, particularly SVM and Random Forest, have proven to be effective in emotion text classification and can be applied to various sentiment analysis applications.  \nKeywords: Text Emotion Recognition; Machine Learning; Text Classification; TF-IDF.","cbCairyL3pTa9MG9","https://ap.wps.com/l/cbCairyL3pTa9MG9","pdf",464401,"Indonesian","# Abstrak\n## Tujuan dan ruang lingkup\n## Data emosi dan preprocessing\n## Representasi fitur (TF-IDF)\n## Algoritma yang digunakan\n## Hasil dan implikasi","[{\"question\":\"Penelitian ini bertujuan untuk apa?\",\"answer\":\"Penelitian ini bertujuan merancang model klasifikasi teks emosi menggunakan berbagai algoritma classification machine learning.\"},{\"question\":\"Bagaimana data teks dipersiapkan sebelum pelatihan model?\",\"answer\":\"Tahapan preprocessing meliputi tokenisasi, stop word removal, dan lemmatization.\"},{\"question\":\"Algoritma apa yang menghasilkan performa terbaik dan berapa akurasinya?\",\"answer\":\"Model SVM dan Random Forest memberikan performa terbaik dengan akurasi mencapai 85% pada data uji.\"}]","Perancangan Model Text Emotion Recognition Menggunakan Algoritma Classification Machine Learning | PDF",5]