[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125411-id":3,"doc-seo-125411-113":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},125411,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","PERANCANGAN MODEL MACHINE LEARNING UNTUK MENDETEKSI BERITA HOAKS - DENGAN ALGORITMA KLASIFIKASI - Skripsi","Penelitian ini bertujuan mengembangkan aplikasi deteksi berita hoaks berbasis Natural Language Processing (NLP) dan Machine Learning menggunakan metode Research and Development (R&D) 4D dengan tahapan pengembangan model Waterfall. Dataset berjumlah 26.557 berita terdiri atas 14.665 berita valid dan 11.892 berita palsu. Empat model diuji: Logistic Regression, Naive Bayes, Random Forest, dan BERT. Hasil evaluasi menunjukkan Logistic Regression paling baik dengan accuracy 92,72%, precision 0,93, recall 0,93, serta F1-score 0,93, dan tidak terindikasi overfitting. Uji coba melibatkan 35 mahasiswa melalui materi, penggunaan aplikasi, dan post-test 10 soal tervalidasi. Uji One-Sample Wilcoxon Signed Rank Test menghasilkan p\u003C0,001, menandakan aplikasi berpengaruh signifikan terhadap peningkatan literasi digital peserta, serta efektif sebagai sarana edukasi publik.","PERANCANGAN MODEL MACHINE LEARNING UNTUK MENDETEKSI BERITA HOAKS MENGGUNAKAN ALGORITMA KLASIFIKASI  \nSKRIPSI  \nDiajukan untuk memenuhi syarat dalam memperoleh gelar Sarjana Pendidikan di Program Studi Pendidikan Sistem dan Teknologi Informasi  \nOleh:  \nAdrian KusumaWidjaja Kardana  \nNIM. 2103456  \nPROGRAM STUDI S1  \nPENDIDIKAN SISTEM DAN TEKNOLOGI INFORMASI KAMPUS UPI DI PURWAKARTA  \nUNIVERSITAS PENDIDIKAN INDONESIA  \nLEMBAR HAK CIPTA  \nPERANCANGAN MODEL MACHINE LEARNING UNTUK MENDETEKSI BERITA HOAKS MENGGUNAKAN ALGORITMA KLASIFIKASI  \nOleh  \nAdrian Kusuma Widjaja Kardana  \nSebuah skripsi yang diajukan untuk memenuhi syarat memperoleh gelar Sarjana Pendidikan di Universitas Pendidikan Indonesia Kampus Daerah Purwakarta  \n© Adrian KusumaWidjaja Kardana 2025  \nUniversitas Pendidikan Indonesia  \nAgustus 2025  \nHak Cipta dilindungi Undang-Undang  \nSkripsi ini tidak boleh diperbanyak seluruhnya atau sebagian, dengan dicetak ulang, difotokopi, atau cara lainnya tanpa izin dari penulis.  \nPERANCANGAN MODEL MACHINE LEARNING UNTUKMENDETEKSI BERITA HOAKS MENGGUNAKAN ALGORITMA KLASIFIKASI  \nAdrian KusumaWidjaja Kardana  \n2103456  \nABSTRAK  \nPenelitian ini bertujuan mengembangkan aplikasi deteksi berita hoaks berbasis Natural Language Processing (NLP) dan algoritma Machine Learning dengan metode Research and Development (R&D) 4D, di mana tahappengembangan aplikasi menggunakan model Waterfall. Dataset penelitian terdiridari total 26.557 berita, yakni 14.665 berita valid dan 11.892 berita palsu. Empat model diuji: Logistic Regression, Naive Bayes, Random Forest, dan BERT. Berdasarkan evaluasi, Logistic Regression menghasilkan kinerja terbaik (accuracy 92,72%, precision 0,93, recall 0,93, F1-score 0,93) dengan performa stabil tanpaindikasi overfitting. Uji coba lapangan melibatkan 35 mahasiswa sebagai responden melalui pemberian materi, penggunaan aplikasi, dan post-test 10 soal yang telahdivalidasi. Hasil uji One-Sample Wilcoxon Signed Rank Test menunjukkanp\u003C0,001, menandakan aplikasi berpengaruh signifikan terhadap peningkatan literasi digital peserta. Temuan ini menunjukkan bahwa sistem tidak hanya akurat mendeteksi berita hoaks, tetapi juga efektif sebagai sarana edukasi publik.  \nKata kunci: Deteksi Hoaks, Natural Language Processing, Machine Learning  \nDEVELOPMENT OF A MACHINE LEARNING MODEL FOR HOAX NEWS DETECTION USING A CLASSIFICATION ALGORITHM  \nAdrian KusumaWidjaja Kardana  \n2103456  \nABSTRACT  \nThis study aims to develop a hoax news detection application based on Natural Language Processing (NLP) and Machine Learning algorithms using the Research and Development (R&D) 4D method, with the development stage implemented through the Waterfall model. The dataset comprises 26,557 news articles, consisting of 14,665 valid and 11,892 hoax articles. Four models were tested: Logistic Regression, Naive Bayes, Random Forest, and BERT. Logistic Regression achieved the best performance (accuracy 92. 72%, precision 0.93, recall 0.93, F1-score 0.93) with stable results and no significant overfitting. A field experiment involved 35 university students as respondents, including material delivery, application usage, and a 10-item validated post-test. The One-Sample Wilcoxon Signed Rank Test yielded p\u003C0.001, indicating a significant effect of the application on improving participants’ digital literacy. These findings suggest that the system is not only accurate in detecting hoax news but also effective as a public educational tool.  \nKeywords: Hoax Detection, Natural Language Processing, Machine Learning  \nDAFTAR ISI  \nLEMBAR HAK CIPTA .......................................................................................... ii  \nLEMBAR PENGESAHAN.................................................................................... iii  \nPERNYATAAN BEBAS PLAGIARISME ........................................................... iv  \nKATA PENGANTAR............................................................................................. v  \n[UCAPAN SYU","cbCaiioIFWJaBrhQ","https://ap.wps.com/l/cbCaiioIFWJaBrhQ","pdf",445506,3,1,14,"Indonesian","id",113,"# Daftar Isi\n## Lembar Hak Cipta\n## Lembar Pengesahan\n## Pernyataan Bebas Plagiarisme\n## Kata Pengantar\n## Ucapan Syukur\n## Abstrak\n## Abstract\n## Daftar Isi\n## Daftar Tabel\n## Daftar Gambar\n## Daftar Lampiran\n## Bab I Pendahuluan\n## Bab II Tinjauan Pustaka","[{\"question\":\"Apa tujuan penelitian pada skripsi ini?\",\"answer\":\"Mengembangkan aplikasi deteksi berita hoaks berbasis NLP dan Machine Learning, dengan tahapan pengembangan menggunakan metode R\\u0026D 4D dan model Waterfall.\"},{\"question\":\"Model Machine Learning apa saja yang diuji untuk mendeteksi hoaks?\",\"answer\":\"Logistic Regression, Naive Bayes, Random Forest, dan BERT diuji menggunakan dataset yang disediakan.\"},{\"question\":\"Bagaimana hasil evaluasi dan dampak aplikasi terhadap literasi digital?\",\"answer\":\"Logistic Regression menghasilkan kinerja terbaik (accuracy 92,72% dan F1-score 0,93) tanpa indikasi overfitting, sedangkan uji One-Sample Wilcoxon Signed Rank Test menunjukkan p\\u003c0,001 yang berarti aplikasi berpengaruh signifikan terhadap peningkatan literasi digital peserta.\"}]","PERANCANGAN MODEL MACHINE LEARNING UNTUK MENDETEKSI BERITA HOAKS - DENGAN ALGORITMA KLASIFIKASI - Skripsi | PDF",1785898765,22,{"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},"designing-a-machine-learning-model-for-detecting-hoax-news-using-classification-algorithms-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/designing-a-machine-learning-model-for-detecting-hoax-news-using-classification-algorithms-thesis/125411/",4,{"url":52,"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","2026-08-05",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 pada skripsi ini?","Question",{"text":76,"@type":77},"Mengembangkan aplikasi deteksi berita hoaks berbasis NLP dan Machine Learning, dengan tahapan pengembangan menggunakan metode R&D 4D dan model Waterfall.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Model Machine Learning apa saja yang diuji untuk mendeteksi hoaks?",{"text":81,"@type":77},"Logistic Regression, Naive Bayes, Random Forest, dan BERT diuji menggunakan dataset yang disediakan.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana hasil evaluasi dan dampak aplikasi terhadap literasi digital?",{"text":85,"@type":77},"Logistic Regression menghasilkan kinerja terbaik (accuracy 92,72% dan F1-score 0,93) tanpa indikasi overfitting, sedangkan uji One-Sample Wilcoxon Signed Rank Test menunjukkan p\u003C0,001 yang berarti aplikasi berpengaruh signifikan terhadap peningkatan literasi digital peserta.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"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"]