[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124256-id":3,"doc-seo-124256-113":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},124256,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",54,"Penelitian & Laporan","Analisa Internet Movie Database (IMDb) Menggunakan Algoritma Machine Learning - Super Vector Machine - Analisis Sentimen Positif atau Negatif","Artikel ini membahas analisa Internet Movie Database (IMDb) untuk klasifikasi sentimen ulasan film menggunakan algoritma Support Vector Machine (SVM). IMDb menyediakan kumpulan ulasan pengguna yang luas sehingga dapat dimanfaatkan untuk memetakan opini publik terhadap film. Penelitian bertujuan mengklasifikasikan ulasan sebagai positif atau negatif melalui proses pra-pemrosesan seperti tokenisasi, penghapusan stop words, serta vektorisasi teks dengan TF-IDF. Model SVM kemudian dilatih dan dinilai memakai metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan performa akurasi tinggi serta membahas perbandingan SVM dengan algoritma lain dan peluang pengembangan seperti pengayaan kategori sentimen dan optimasi hiperparameter.","ANALISA INTERNET MOVIE DATABASE (IMDb) MENGGUNAKANALGORITMA MACHINE LEARNING SUPER VECTOR MACHINE  \nIndra Sari Kusuma Wardhana  \nProgram Studi Teknik Informatika, Universitas Indraprasta PGRI  \n[indraskw@gmail.com](indraskw@gmail.com)  \nSubmitted February 13, 2025; Revised April 2, 2025; Accepted April 5, 2025  \nAbstrak  \nArtikel ini menyajikan analisis Internet Movie Database (IMDb) menggunakan algoritma Support Vector Machine (SVM) untuk klasifikasi sentimen. IMDb, sebagai salah satu platform ulasan film online terbesar, menawarkan kumpulan data ulasan pengguna yang luas, yang dapat dimanfaatkan untuk menganalisis opini publik tentang film. Tujuan dari studi ini adalah untuk mengklasifikasikan ulasan film sebagai positif ataunegatif menggunakan SVM, algoritmapembelajaranmesin yang dikenal efektif untuk tugas klasifikasi biner. Dataset yang terdiri dari ribuan ulasan IMDb melewati langkah-langkah pra-pemrosesan seperti tokenisasi, penghapusan kata umum (stop words), dan vektorisasi teks menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) . Algoritma SVM kemudian diterapkan pada data yang telah diproses untuk melatih model, yang dievaluasi berdasarkan akurasi, presisi, recall, dan skor F1 . Hasil eksperimen menunjukkan bahwa model SVM memiliki kinerja akurasi yang tinggi, membuktikan keandalannya dalam tugas analisis sentimen untuk dataset ulasan film berskala besar. Makalah ini juga membahas keuntungan menggunakan SVM dibandingkan algoritmapembelajaran mesin lainnya dan menyoroti area untuk peningkatan di masa depan, termasuk menggabungkan kategori sentimen yang lebih terperinci dan mengoptimalkan hiperparameter model. Kata Kunci : IMDb, Support Vector Machine, Sentiment Analysis, Machine Learning, TF-IDF  \nAbstract  \nThis paper presents an analysis of the Internet Movie Database (IMDb) using the Support Vector Machine (SVM) algorithm for sentiment classification. IMDb, as one of the largest online movie review platforms, offers a vast dataset of user reviews, which can be leveraged to analyze public opinion on movies. The goal of this study is to classify movie reviews as positive or negative using SVM, a machine learning algorithm known for its effectiveness in binary classification tasks. The dataset, consisting of thousands of IMDb reviews, undergoes pre-processing steps such as tokenization, removal of stop words, and text vectorization using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM algorithm is then applied to this processed data to train the model, which is evaluated based on its accuracy, precision, recall, and F1-score. Experimental results indicate that the SVM model performs with high accuracy, proving its reliability in sentiment analysis tasks for large-scale movie review datasets. This paper also discusses the advantages of using SVM over other machine learning algorithms and highlights areas for future improvement, including incorporating more nuanced sentiment categories and optimizing the model's hyperparameters.  \nKey Words : IMDb, SVM, Sentiment Analysis, Machine Learning, TF-IDF  \n1. PENDAHULUAN  \nDataset IMDb (Internet Movie Database) yang terdapat pada Kaggle adalahkumpulan data komprehensif yang terkaitdengan film, bersumber dari Internet Movie Database (IMDb), yang sering digunakanuntuk analisis sentimen, pemrosesan bahasa alami (NLP), dan tugas pembelajaran  \nmesin. Dataset ini biasanya mencakupulasan film dalam skala besar dan metadata terkait, seperti teks ulasan, skor ulasan, sertaklasifikasi sentimen (positif atau negatif) . Versi paling umum dari dataset ini, berjudul\"IMDb Movie Reviews,\" berisi 50.000 ulasan yang telah diberi label dan dibagirata antara set pelatihan dan pengujian, dengan kelas yang seimbang untuk  \nklasifikasi sentimen biner. Setiap ulasan diklasifikasikan sebagai positif atau negatifberdasarkan sentimen pengulas, sehingga ideal untuk tugas pembelajaran terawasidalam analisis sentimen. Varian lainnya,\"IMDb 5000 Movie Dataset,\" menawarkan metadata y","cbCaiejV6DqtBFhH","https://ap.wps.com/l/cbCaiejV6DqtBFhH","pdf",310367,7,1,9,"Indonesian","id",113,"# Pendahuluan\n## Dataset IMDb dan sumbernya\n## Fitur utama dalam dataset film IMDb\n## Penggunaan data IMDb dalam model pembelajaran mesin","[{\"question\":\"Apa tujuan analisa IMDb dalam penelitian ini?\",\"answer\":\"Mengklasifikasikan ulasan film pada IMDb menjadi sentimen positif atau negatif menggunakan SVM.\"},{\"question\":\"Tahapan pra-pemrosesan apa yang dilakukan pada dataset ulasan IMDb?\",\"answer\":\"Meliputi tokenisasi, penghapusan stop words, dan vektorisasi teks menggunakan TF-IDF.\"},{\"question\":\"Bagaimana kinerja model SVM dievaluasi?\",\"answer\":\"Model dinilai menggunakan akurasi, presisi, recall, dan skor F1.\"}]","Analisa Internet Movie Database (IMDb) Menggunakan Algoritma Machine Learning - Super Vector Machine - Analisis Sentimen Positif atau Negatif | PDF",1785821248,14,{"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-internet-movie-database-imdb-using-machine-learning-support-vector-machine-positive-or-negative-reviews","",{"@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-internet-movie-database-imdb-using-machine-learning-support-vector-machine-positive-or-negative-reviews/124256/",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 analisa IMDb dalam penelitian ini?","Question",{"text":77,"@type":78},"Mengklasifikasikan ulasan film pada IMDb menjadi sentimen positif atau negatif menggunakan SVM.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Tahapan pra-pemrosesan apa yang dilakukan pada dataset ulasan IMDb?",{"text":82,"@type":78},"Meliputi tokenisasi, penghapusan stop words, dan vektorisasi teks menggunakan TF-IDF.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana kinerja model SVM dievaluasi?",{"text":86,"@type":78},"Model dinilai menggunakan akurasi, presisi, recall, dan skor F1.","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"]