[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123310-id":3,"doc-seo-123310-113":31,"detail-sidebar-cat-0-id-113":93},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},123310,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",54,"Penelitian & Laporan","SENTIMENT ANALYSIS KUALITAS PELAYANAN MASKAPAI PENERBANGAN MENGGUNAKAN ALGORITMA MACHINE LEARNING","Penelitian ini berfokus pada analisis sentimen dari ulasan pelanggan terhadap maskapai penerbangan untuk mengidentifikasi sentimen positif, negatif, dan netral. Proses penelitian memanfaatkan dataset ulasan yang dipra-pemroses sebelum digunakan dalam pelatihan dan evaluasi model. Algoritma machine learning yang digunakan meliputi Naive Bayes, Support Vector Machines (SVM), dan Random Forest. Tahap pelatihan menerapkan validasi silang serta penyetelan parameter guna memperoleh performa optimal. Evaluasi dilakukan memakai metrik akurasi, presisi, recall, dan F1-score, dengan hasil menunjukkan kemampuan model mengklasifikasikan sentimen secara benar.","Jurnal Siliwangi Vol. 9 No. 2, 2023 Seri Sains dan Teknologi  \nP-ISSN 2477-3891 E-ISSN 2615-4765  \nSENTIMENT ANALYSIS KUALITAS PELAYANAN MASKAPAI PENERBANGAN MENGGUNAKAN ALGORITMA MACHINE LEARNING  \nMoch Ilham Fajar Gumilang  \nProgram Studi Informatika, Universitas Siliwangi  \ne-mail: [207006047@student.unsil.ac.id](207006047@student.unsil.ac.id)  \nAbstrak  \nPenelitian ini fokus pada menganalisis sentimen dalam ulasan pelanggan terhadap maskapai penerbangan. Tujuan utamanya untuk mengidentifikasi sentimen positif, negatif, atau netral dalam ulasan dan memberikanwawasan yang dapat digunakan untuk meningkatkan kualitas pelayanan maskapai. Beberapa algoritma machine learning digunakan untuk melakukan analisis sentimen antara lain: Naive Bayes, Support Vector Machines (SVM), dan Random Forest. Dataset ulasan maskapai penerbangan diunggah dan diproses sebelum digunakan dalam pelatihan dan evaluasi model. Tahap pelatihan melibatkan penggunaan teknik validasi silang dan penyetelan parameter untuk mencapai performa yang optimal. Evaluasi dilakukan dengan menggunakan metrik evaluasi seperti akurasi, presisi, recall, dan F1-score. Hasil evaluasi akurasi menunjukkan kemamauan model dalam mengklasifikasikan sentimen dengan benar. Berdasarkan hasil percobaan, algoritma yang digunakan berhasil dalam mengklasifikasikan sentimen ulasan maskapai penerbangan dengan akurasi yang yang bervariasi. Naive Bayes, SVM, dan Random Forest menunjukkan performa yang baik dalam mengenalisentimen positif, negatif, dan netral.  \nKata Kunci : sentimen analisis, support vector machine, naïve bayes, random forest.  \nAbstract  \nThis study focuses on analyzing sentiment in customer reviews of airlines. The main objective is to identify positive, negative, or neutral sentiment in reviews and provide insights that can be used to improve the quality of airline services. Several machine learning algorithms are used to perform sentiment analysis, including: Naive Bayes, Support Vector Machines (SVM), and Random Forest. The airline review dataset is uploaded and processed before being used in model training and evaluation. The training stage involves the use ofcrossvalidation techniques and parameter tuning to achieve optimal performance. Evaluation is carried out using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of the accuracy evaluation indicate the model's ability to classify sentiment correctly. Based on the experimental results, the algorithms used were successful in classifying airline review sentiment with varying accuracy. Naive Bayes, SVM, and Random Forest showed good performance in recognizing positive, negative, and neutral sentiments.  \nKeywords: sentiment analysis, support vector machine, naïve bayes, random forest..  \nI. PENDAHULUAN  \nDi zaman yang serba cepat ini, mobilitas tinggi menjadi kebutuhan yang tidak bisa dihindari olehmasyarakat. Masyarakat membutuhkan transportasi yang mudah, cepat dan terjangkau untuk mendukung mobilitas. Sebelumnya, orang menggunakan transportasi darat dan laut, tetapi sangat memakanwaktu. Oleh karena itu, masyarakat memilih opsi lainyaitu melalui udara. Namun harga yang ditawarkan sangat tinggi, sehingga tidak semua orang dapatmenikmati layanan penerbangan. Seiring waktu, layanan maskapai penerbangan dapat diimplementasikan dengan biaya yang semakinterjangkau. Maskapai murah atau low cost airlines ditandai oleh beberapa ciri utama, diantaranya: satu pesawat per kelas penerbangan, operasi penerbanganintensif, tidak ada layanan makanan atau minuman  \n(no frill), penghematan bahan bakar yang agresif, dan layanan penumpang khusus terbatas. Oleh karena itu, kualitas layanan menjadi pertimbangan penting bagikonsumen dalam memilih layanan yang digunakan. Kinerja perusahaan secara keseluruhan merupakanukuran dari kualitas layanan yang diberikan. Hal ini berdampak langsung pada kepuasan konsumen. Ketika kualitas pelayanan yang diberikan baik dankebutuhan konsumen dapat terpenuhi maka kepuasan ","cbCaiuMLtTOBtFAl","https://ap.wps.com/l/cbCaiuMLtTOBtFAl","pdf",280032,7,1,6,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang kebutuhan transportasi udara\n## Pentingnya kualitas layanan dan kepuasan pelanggan\n## Sentimen pengguna Twitter terhadap layanan maskapai\n# Metode\n## Identifikasi masalah dan studi literatur\n## Pengumpulan data dan pengolahan data\n## Prapemrosesan, vektorisasi, dan pemodelan algoritma\n## Evaluasi model dan pengujian data","[{\"question\":\"Apa tujuan utama penelitian sentiment analisis kualitas pelayanan maskapai penerbangan?\",\"answer\":\"Penelitian bertujuan mengidentifikasi sentimen positif, negatif, atau netral pada ulasan pelanggan dan memberikan wawasan untuk meningkatkan kualitas pelayanan maskapai.\"},{\"question\":\"Algoritma machine learning apa saja yang digunakan dalam analisis sentimen?\",\"answer\":\"Penelitian menggunakan Naive Bayes, Support Vector Machines (SVM), dan Random Forest untuk mengklasifikasikan sentimen.\"},{\"question\":\"Bagaimana kinerja model dievaluasi pada penelitian ini?\",\"answer\":\"Kinerja dievaluasi menggunakan metrik seperti akurasi, presisi, recall, dan F1-score, dengan hasil menunjukkan model mampu mengklasifikasikan sentimen secara benar meski akurasi bervariasi.\"}]","SENTIMENT ANALYSIS KUALITAS PELAYANAN MASKAPAI PENERBANGAN MENGGUNAKAN ALGORITMA MACHINE LEARNING | PDF",1785815867,9,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"sentiment-analysis-of-airline-service-quality-using-machine-learning-algorithms","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/sentiment-analysis-of-airline-service-quality-using-machine-learning-algorithms/123310/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-04",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Apa tujuan utama penelitian sentiment analisis kualitas pelayanan maskapai penerbangan?","Question",{"text":77,"@type":78},"Penelitian bertujuan mengidentifikasi sentimen positif, negatif, atau netral pada ulasan pelanggan dan memberikan wawasan untuk meningkatkan kualitas pelayanan maskapai.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma machine learning apa saja yang digunakan dalam analisis sentimen?",{"text":82,"@type":78},"Penelitian menggunakan Naive Bayes, Support Vector Machines (SVM), dan Random Forest untuk mengklasifikasikan sentimen.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana kinerja model dievaluasi pada penelitian ini?",{"text":86,"@type":78},"Kinerja dievaluasi menggunakan metrik seperti akurasi, presisi, recall, dan F1-score, dengan hasil menunjukkan model mampu mengklasifikasikan sentimen secara benar meski akurasi bervariasi.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]