[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121731-id":3,"doc-seo-121731-113":31,"detail-sidebar-cat-0-id-113":92},{"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},121731,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","PERBANDINGAN PENDEKATAN DEEP LEARNING(FFNN,RNN,LSTM&CNN)DAN MACHINE LEARNING(LOGISTIC REGRESSION,SVC,NB&KNN) - UNTUK KLASIFIKASI SENTIMEN DATA BERITA","Penelitian ini membandingkan performa pendekatan Deep Learning (FFNN, RNN, LSTM, dan CNN) serta Machine Learning (Logistic Regression, SVC, NB, dan KNN) untuk klasifikasi sentimen data berita. Dataset yang digunakan adalah kumpulan data berita yang telah dilabeli sentimennya, dengan evaluasi menggunakan metrik akurasi, presisi, recall, dan F1 score pada data uji. Hasil menunjukkan Deep Learning lebih unggul dibanding Machine Learning, dengan model LSTM sebagai performa terbaik (akurasi 75,70%, presisi 76,61%, recall 82,81%, dan F1 score 79,59%).","PERBANDINGAN PENDEKATAN DEEPLEARNING(FFNN,RNN,LSTM&CNN)DANMACHINE LEARNING (Logistic Regression,SVC,NB&KNN)UNTUK KLASIFIKASI SENTIMEN DATABERITA  \ndeep learningIC UNIVERSITVS  \nmachine learningRTA  \nPERBANDINGAN PENDEKATAN DEEPLEARNING(FFNN,RNN,LSTM &CNN)DANMACHINE LEARNING(Logistic Regression,SVC,NB&KNN)UNTUK KLASIFIKASI SENTIMEN DATABERITA  \nANor Anisa  \n21206052009  \n# PERNYATAAN KEASLIAN\n\nYang bertanda tangan di bawah ini:  \n:Nor Anisa  \nNama  \nNim  \n:21206052009  \n:Magister  \nJenjang  \nProgram Studi  \n:Informatika  \nmenyatakan bahwa naskah tesis ini secara keseluruhan adalah hasil penelitian/karya sayasendiri,kecuali pada bagian-bagian yang dirujuk sumbernya.  \nPalangka Raya,08 April 2023Saya yang menyatakan,  \nH机作种he4  \n鲁  \n凰  \nMETERA  \nTEMPE  \n27DBAKX328259754  \nNor AnisaNIM:21206052009  \nSTATE ISLAMIC  \n# PERNYATAAN BEBAS PLAGIASI\n\nSaya yang bertanda tangan di bawah ini:  \nNama:Nor AnisaNIM:21206052009Jenjang:Magister ProgramStudi:Informatika  \nMenyatakan bahwa saya menyerahkan bahwa naskah tesis ini secara keseluruhan benar-benarbebas dari plagiasi.Jika,dikemudian hari terbukti melakukan plagiasi maka,saya siap ditindaksesuai dengan ketentuan hukum yang berlaku  \nPalangka Raya,08 April 2023Sayayang menyatakan  \n般  \nNor AnisaNIM.21206052009  \nPENGESAHAN TUGAS AKHIR  \nNomor:B-1176/Un.02/DST/PP.00.9/05/2023  \nTugas Akhir dengan judul:PERBANDINGAN PENDEKATAN DEEP LEARNING(FFNN,RNN,LSTM &CNN)DAN MACHINE LEARNING (LOGISTIC REGRESSION,SVC,NB&KNN)UNTUKKLASIFIKASI SENTIMEN DATA BERITA  \nyang dipersiapkan dan disusun oleh:  \nNama:NOR ANISA,S.KomNomor Induk Mahasiswa:21206052009Telah diujikan pada:Rabu,12 April 2023Nilai ujian Tugas Akhir:A-  \ndinyatakan telah diterima oleh Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta  \n# TIM UJIAN TUGAS AKHIR\n\nDr.Ir.Bambang Sugiantoro,S.Si.,M.T.,IPM.SIGNEDValid ID:6451ad4d95728PengujiIPengujiⅡIr.Muhammad Taufiq Nuruzzaman,S.T.Dr.Sugiyanto,S.Si.,ST.,M.Si  \nM.Eng.,Ph.D.SIGNEDSIGNEDValid ID:645068bab3be3Valid ID:6437dccd3c2bf  \nValid ID:6462dfa9babee  \nKetua Sidang  \nYogyakarta,12 April 2023UIN Sunan KalijagaDekan Fakultas Sains dan Teknologi  \nDr.Dra.Hj.Khurul Wardati,M.Si.SIGNED  \n# SURAT PERSETUJUAN TUGAS AKHIR\n\nHal:Persetujuan Tugas Akhir  \nKepada:  \nYth.Dekan Fakultas Sains dan TeknologiUin Sunan KalijagaYogyakarta Di Yogyakarta  \nAssalamu'alaikum Wr.Wb.  \nSetelah melakukan bimbingan,arahan,dan koreksi terhadap penulisan tesis yang berjudul:PERBANDINGAN PENDEKATAN DEEP LEARNING(FFNN,RNN,LSTM&CNN)DAN MACHINE LEARNING (Logistic Regression,SVC,NB&KNN)UNTUKKLASIFIKASI SENTIMEN DATA BERITA yang di tulis oleh:Nama:Nor AnisaNIM:21206052009Jenjang:MagisterProgram Studi:Informatika  \nSudah dapat diajukan kepada Program Studi Magister Informatika Fakultas Sains danTeknologi UIN Sunan Kalijaga sebagai salah satu syarat untuk memperoleh gelar MagisterInformatika.  \nDengan ini saya mengharap agar tugas tersebut di atas agar dapat segera dimunaqosyahkan.Atas perhatiannya saya ucapkan terimakasih.  \nWassalamu'alaikum Wr.Wb.  \nYogyakarta,9 April 2023Pembimbing,  \nt  \nDr.Ir.Bambang Sugiantoro,S.Si.,M.T.,IPM.197701032005011003  \n# NOTA DINAS PEMBIMBING\n\nKepada Yth,  \nDekan Fakultas Sains dan TeknologiUIN Sunan KalijagaYogyakarta  \nAssalamu'alaikum wr.Wb.  \nSetelah melakukan bimbingan,arahan,dan koreksi terhadap penulisantesis yang berjudul:  \nPERBANDINGAN PENDEKATAN DEEP LEARNING(FFNN,RNN,LSTM&CNN)DANMACHINE LEARNING(LogisticRegression,SVC,NB&KNN)UNTUK KLASIFIKASISENTIMEN DATA BERITA  \nYang ditulis oleh:  \nNama  \n:Nor Anisa  \nNIM  \n:21206052009  \nJenjang  \n:Magister  \n:Informatika  \nProgram Studi  \nSaya berpendapat bahwa tesis tersebut sudah dapat diajukan kepadaMagister Informatika UIN Sunan Kalijaga untuk diujikan dalamrangka memperoleh gelar Magister Informatika.  \nWassalamu'alaikum wr.wb.  \nYogyakarta,23 November 2022Pembimbing,  \n(Dr.Ir.Bambang Sugiantoro,S.Si.,M.T.)  \n# ABSTRAK\n\nPenelitian ini dilakukan untuk membandingkan performapendekatan Deep Learning(FFNN,RNN,LSTM,dan CNN)danMachine Le","cbCaivVHhBtNmP6f","https://ap.wps.com/l/cbCaivVHhBtNmP6f","pdf",3199485,4,1,39,"Indonesian","id",113,"# PERNYATAAN KEASLIAN\n# PERNYATAAN BEBAS PLAGIASI\n# PENGESAHAN TUGAS AKHIR\n# TIM UJIAN TUGAS AKHIR\n# SURAT PERSETUJUAN TUGAS AKHIR\n# NOTA DINAS PEMBIMBING\n# ABSTRAK","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Membandingkan performa Deep Learning (FFNN, RNN, LSTM, CNN) dan Machine Learning (Logistic Regression, SVC, NB, KNN) untuk klasifikasi sentimen data berita.\"},{\"question\":\"Bagaimana proses evaluasi performa dilakukan?\",\"answer\":\"Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1 score pada data uji.\"},{\"question\":\"Model mana yang memberikan performa terbaik dan terburuk?\",\"answer\":\"Model LSTM memberikan performa terbaik dengan akurasi 75,70%, presisi 76,61%, recall 82,81%, dan F1 score 79,59%. Model RNN merupakan model dengan performa terburuk.\"}]","PERBANDINGAN PENDEKATAN DEEP LEARNING(FFNN,RNN,LSTM&CNN)DAN MACHINE LEARNING(LOGISTIC REGRESSION,SVC,NB&KNN) - UNTUK KLASIFIKASI SENTIMEN DATA BERITA | PDF",1785806535,60,{"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},"comparison-of-deep-learningffnnrnnlstmcnn-and-machine-learninglogistic-regressionsvcnbknn-for-news-sentiment-classification","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/comparison-of-deep-learningffnnrnnlstmcnn-and-machine-learninglogistic-regressionsvcnbknn-for-news-sentiment-classification/121731/",{"url":53,"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","2026-08-15","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},"Membandingkan performa Deep Learning (FFNN, RNN, LSTM, CNN) dan Machine Learning (Logistic Regression, SVC, NB, KNN) untuk klasifikasi sentimen data berita.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana proses evaluasi performa dilakukan?",{"text":81,"@type":77},"Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1 score pada data uji.",{"name":83,"@type":74,"acceptedAnswer":84},"Model mana yang memberikan performa terbaik dan terburuk?",{"text":85,"@type":77},"Model LSTM memberikan performa terbaik dengan akurasi 75,70%, presisi 76,61%, recall 82,81%, dan F1 score 79,59%. Model RNN merupakan model dengan performa terburuk.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,114,116,120,124,128,132],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":30,"slug":97},55,"Agama & Spiritualitas","religion-spirituality",{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":30,"slug":101},48,"Cerita & Novel","story-novel",{"id":103,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":30,"slug":105},56,"Gaya Hidup","lifestyle",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":30,"slug":109},51,"Komik","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":30,"slug":113},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":115},"research-report",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},49,"Sastra","literature",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":30,"slug":123},52,"Teknologi","technology",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":30,"slug":127},50,"Ujian","exam",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":30,"slug":131},57,"Umum","general",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":4,"slug":135},181,"Formulir","formulir"]