[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123555-id":3,"doc-seo-123555-113":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},123555,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","Optimasi Analisis Sentimen terhadap Aplikasi Pencari Kerja menggunakan Perbandingan Algoritma Machine Learning - BAB II Landasan Teori","Penelitian pada BAB II Landasan Teori merangkum studi terdahulu terkait analisis sentimen dengan fokus perbandingan algoritma machine learning. Tinjauan sepuluh artikel jurnal menunjukkan penggunaan yang dominan pada Naïve Bayes (NB) dan Support Vector Machine (SVM), disertai algoritma lain seperti KNN, Decision Tree (DT), serta pendekatan berbasis TF-IDF. Hasil berbagai studi memperlihatkan perbedaan akurasi antar algoritma, di mana SVM unggul pada beberapa kasus (misalnya Zoom dan Kampus Merdeka), sedangkan NB tampil lebih baik pada kasus lain seperti ulasan perokok Twitter dan beberapa dataset vaksinasi.","BAB II  \nLANDASAN TEORI  \nPenelitian Terdahulu  \nTabel 2 1 Penelitian terdahulu  \n\n| No | Jurnal | Judul | Penulis | Metode | Hasil |\n| --- | --- | --- | --- | --- | --- |\n| 1 | CESS (Journal\u003Cbr>of Computer\u003Cbr>Engineering\u003Cbr>System and\u003Cbr>Science) p\u003Cbr>ISSN :2502-\u003Cbr>7131\u003Cbr>Vol. 5 No. 2\u003Cbr>Juli 2020 | Analisis\u003Cbr>Sentimen\u003Cbr>Zoom Cloud\u003Cbr>Meetings di\u003Cbr>Play Store\u003Cbr>Menggunakan\u003Cbr>Naïve Bayes\u003Cbr>dan Support\u003Cbr>Vector\u003Cbr>Machine | Nuraeni\u003Cbr>Herlinawati,\u003Cbr>Yuri Yuliani,\u003Cbr>Siti Faizah,\u003Cbr>Windu Gata,\u003Cbr>Samudi\u003Cbr>Samudi | NB\u003Cbr>dan SVM | untuk NB nilai akurasi = 74,37% Sedangkan untukalgoritma SVM nilai akurasi = 81,22% . dapat diketahuibahwa tingkat akurasi yang didapatkan algoritma Support Vector Machine (SVM) lebih unggul 6,85% dibandingkan algoritma Naïve Bayes (NB) . |\n| 2 | Jurnal Teknik\u003Cbr>Informatika\u003Cbr>dan Sistem\u003Cbr>Informasi\u003Cbr>Vol.8,No.2,Jun\u003Cbr>i 2021 | Analisis\u003Cbr>Sentimen Pada\u003Cbr>Ulasan\u003Cbr>Pengguna\u003Cbr>Aplikasi Bibit\u003Cbr>Dan Bareksa\u003Cbr>Dengan\u003Cbr>Algoritma\u003Cbr>KNN | Alusius Dwiki Adhi Putra, Safitri Juanita | KNN | Akurasi untuk aplikasi Bibit 85,14%,91,91%,dan 76,44%\u003Cbr>Aplikasi Bareksa\u003Cbr>81,70%,87,15%,75,73% |\n\n| 3 | Unnes Journal\u003Cbr>of\u003Cbr>Mathematics\u003Cbr>Vol.10,No.2\u003Cbr>Desember\u003Cbr>2021 | Analisis\u003Cbr>Sentimen\u003Cbr>Aplikasi Gojek\u003Cbr>Menggunakan\u003Cbr>Support Vector\u003Cbr>Machine dan\u003Cbr>K Nearest\u003Cbr>Neighbour | M.Nurul Muttaqin, Iqbal\u003Cbr>Kharisudin | SVM dan\u003Cbr>KNN | KNN memperoleh akurasi 82,14%,82,28%,dan 95,43%\u003Cbr>SVM memperoleh akurasi 87,98%,88,55% dan 95,43% |\n| --- | --- | --- | --- | --- | --- |\n| 4 | IJNMT\u003Cbr>(International\u003Cbr>Journal of\u003Cbr>New media\u003Cbr>Technology ),\u003Cbr>Vol.8,No.1,20\u003Cbr>21 | Sentiment\u003Cbr>Analysis about\u003Cbr>Indonesian\u003Cbr>LawyersClub\u003Cbr>Television\u003Cbr>Program Using\u003Cbr>K-Nearest\u003Cbr>Neighbor,\u003Cbr>Naïve Bayes\u003Cbr>Classifier, and\u003Cbr>Decision Tree | Nico\u003Cbr>Nathanael\u003Cbr>Wilim,\u003Cbr>Raymond\u003Cbr>Sunardi\u003Cbr>Oetama | KNN, NB,\u003Cbr>dan DT | Akurasi sebesar 76,94% diperoleh algoritma KNN. Namun,penggunaan dataset pada tahun yang berbedamenunjukkan bahwa akurasitertinggi diperoleh algoritma\u003Cbr>NB |\n| 5 | Sinkron:Jurnal\u003Cbr>dan Penelitian\u003Cbr>Teknik\u003Cbr>Informatika,V\u003Cbr>ol. 8, No. 1,\u003Cbr>2023 | Sentiment\u003Cbr>Analysis on\u003Cbr>App Reviews\u003Cbr>Using Support\u003Cbr>Vector\u003Cbr>Machine and\u003Cbr>Naïve Bayes\u003Cbr>Classification | Marchenda\u003Cbr>Fayza\u003Cbr>Madjid,Dian\u003Cbr>Eka\u003Cbr>Ratnawati,Bay\u003Cbr>u Rahayudi\u003Cbr>Marchenda | TF-IDF,NB dan SVM | SVM memperoleh akurasi 94,29% dan NB memperolehakurasi 93,97% |\n\n| 6 | Jurnal\u003Cbr>Rekayasa\u003Cbr>Sistem dan\u003Cbr>Teknologi\u003Cbr>Informasi,\u003Cbr>Vol.7,No.1\u003Cbr>,2023 | Naïve Bayes\u003Cbr>and TF-IDF\u003Cbr>for Sentiment\u003Cbr>Analysis of the\u003Cbr>Covid-19\u003Cbr>Booster\u003Cbr>Vaccine | Imelda, Arief Ramdhan Kurnianto | Naïve\u003Cbr>Bayes | Naïve Bayes memperolehakurasi 85,26% |\n| --- | --- | --- | --- | --- | --- |\n| 7 | International\u003Cbr>Research\u003Cbr>Journal of\u003Cbr>Engineering\u003Cbr>and\u003Cbr>Technology\u003Cbr>Vol.7,No.7,20\u003Cbr>20 | Support\u003Cbr>Vector\u003Cbr>Machine\u003Cbr>versus Naïve\u003Cbr>Bayes\u003Cbr>Classifier : A\u003Cbr>Juxtaposition\u003Cbr>of Two\u003Cbr>Machine\u003Cbr>Learning\u003Cbr>Algorithms for\u003Cbr>Sentiment\u003Cbr>Analysis | Ananya Arora, Prayag Patel, Saud Shaikh , Prof. Amit Hatekar | SVM, NB | Naïve Bayes Sedikit lebih baik secara keseluruhan |\n| 8 | Indonesian\u003Cbr>Journal of\u003Cbr>Computer\u003Cbr>Science\u003Cbr>Vol.12,No.1,2\u003Cbr>023 | Analisa\u003Cbr>Sentimen\u003Cbr>pengguna\u003Cbr>social media\u003Cbr>Twitter\u003Cbr>terhadap\u003Cbr>perokok di\u003Cbr>indonesia | Dewisetiyawati , Nuri Cahyono | NB,SVM dan Random\u003Cbr>Forest | Naïve Bayes memiliki hasilakurasi terbesar dengan nilai\u003Cbr>akurasi tertinggi 62,1% |\n\n| 9 | Jurnal\u003Cbr>Nasional\u003Cbr>Teknik Elektro\u003Cbr>dan Teknologi\u003Cbr>Informasi\u003Cbr>Vol.10,No.2,2\u003Cbr>021 | Analisis\u003Cbr>Sentimen\u003Cbr>Masyarakat\u003Cbr>terhadap\u003Cbr>Tindakan\u003Cbr>Vaksinisasi\u003Cbr>dalam Upaya\u003Cbr>Mengatasi\u003Cbr>Pandemi\u003Cbr>Covid-19 | Brian Laurensz, Eko Sediyono | NB,SVM | Hasil akurasi tertinggi diraiholeh Naïve Bayes denganangka rata-rata 85,59% berbanding sedikit dengan SVM dengan angka rata-rata 84,41% |\n| --- | --- | --- | --- | --- | --- |\n| 10 | Jurnal Sistem Komputer dan Informatika Vol.4,No.2,20 22 | Analisis\u003Cbr>Sentimen\u003Cbr>Ter","cbCaisxCjeEYHYL0","https://ap.wps.com/l/cbCaisxCjeEYHYL0","pdf",644441,4,1,18,"Indonesian","id",113,"# Penelitian Terdahulu\n## Perbandingan Algoritma Analisis Sentimen","[{\"question\":\"Algoritma apa yang paling sering digunakan pada penelitian terdahulu untuk analisis sentimen?\",\"answer\":\"Naïve Bayes (NB) dan Support Vector Machine (SVM) menjadi algoritma yang paling sering digunakan, disertai beberapa algoritma lain seperti KNN dan Decision Tree (DT).\"},{\"question\":\"Algoritma mana yang unggul pada analisis review marketplace (Bibit dan Bareksa) menurut studi kedua?\",\"answer\":\"Studi kedua menggunakan algoritma KNN dengan akurasi yang lebih tinggi pada beberapa komponen pengujian, misalnya Bibit 85,14% serta Bareksa 87,15% (nilai akurasi tercantum pada tabel studi).\"},{\"question\":\"Pada studi tentang sentimen isu perokok di Twitter, algoritma mana yang menghasilkan akurasi tertinggi?\",\"answer\":\"Naïve Bayes (NB) menghasilkan akurasi tertinggi sebesar 62,1%, mengalahkan SVM dan DT.\"}]","Optimasi Analisis Sentimen terhadap Aplikasi Pencari Kerja menggunakan Perbandingan Algoritma Machine Learning - BAB II Landasan Teori | PDF",1785817296,28,{"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-analysis-optimization-for-a-job-search-application-bab-ii-theoretical-background","",{"@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/sentiment-analysis-optimization-for-a-job-search-application-bab-ii-theoretical-background/123555/",{"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","2026-08-16","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},"Algoritma apa yang paling sering digunakan pada penelitian terdahulu untuk analisis sentimen?","Question",{"text":76,"@type":77},"Naïve Bayes (NB) dan Support Vector Machine (SVM) menjadi algoritma yang paling sering digunakan, disertai beberapa algoritma lain seperti KNN dan Decision Tree (DT).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Algoritma mana yang unggul pada analisis review marketplace (Bibit dan Bareksa) menurut studi kedua?",{"text":81,"@type":77},"Studi kedua menggunakan algoritma KNN dengan akurasi yang lebih tinggi pada beberapa komponen pengujian, misalnya Bibit 85,14% serta Bareksa 87,15% (nilai akurasi tercantum pada tabel studi).",{"name":83,"@type":74,"acceptedAnswer":84},"Pada studi tentang sentimen isu perokok di Twitter, algoritma mana yang menghasilkan akurasi tertinggi?",{"text":85,"@type":77},"Naïve Bayes (NB) menghasilkan akurasi tertinggi sebesar 62,1%, mengalahkan SVM dan DT.","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},[94,99,103,107,111,115,117,121,125,129,133],{"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"]