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Penelitian ini merancang dan mengembangkan sistem chatbot berbasis web dengan pipeline NLP lengkap: text preprocessing, pembobotan TF-IDF, dan pencocokan intent memakai Cosine Similarity. Knowledge base dibangun dari 32 intent, 655 pola pertanyaan, dan 46 jawaban, dengan adaptive threshold berbasis kata kunci PPDB. Evaluasi akurasi pada 35 sampel menghasilkan 82,9%, pengujian Black Box valid, latensi rata-rata 1,05 detik, serta UAT 87,4% (Sangat Baik).",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/web-school-ppdb-information-chatbot-system-based-on-tf-idf-and-cosine-similarity-using-adaptive-threshold/204412/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/web-school-ppdb-information-chatbot-system-based-on-tf-idf-and-cosine-similarity-using-adaptive-threshold/204412.png","ImageObject",300,407,{"name":89,"@type":90},"Oliver","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-10-07","2026-09-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",7,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa masalah utama pada layanan informasi PPDB di SMP Sejahtera 2 Cileungsi?","Question",{"text":110,"@type":111},"Layanan masih manual, menyebabkan keterlambatan respons serta penumpukan pertanyaan dari calon pendaftar.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana sistem chatbot dibangun untuk memahami pertanyaan pengguna?",{"text":115,"@type":111},"Sistem memakai pipeline NLP: text preprocessing, pembobotan TF-IDF, lalu pencocokan intent menggunakan Cosine Similarity.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana hasil evaluasi sistem dan indikator performanya?",{"text":119,"@type":111},"Akurasi keseluruhan mencapai 82,9%, pengujian fungsional Black Box valid pada 8 skenario, latensi rata-rata 1,05 detik, dan kepuasan UAT 87,4%.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},204412,1788573128,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Sistem Chatbot Informasi PPDB Berbasis TF-IDF dan Cosine Similarity Menggunakan Adaptive Threshold pada Web Sekolah  \nRaditya Mayesha Beldyq 1*, Rika Apriani2  \n1,2 Program Studi Teknik Informatika, Universitas Bina Insani, Indonesia 1* [radityabeldyq@gmail.com](radityabeldyq@gmail.com), [2](2 rikaapriani@binainsani.ac.id)[ rikaapriani@binainsani.ac.id](2 rikaapriani@binainsani.ac.id)  \nAbstrak: Layanan informasi Penerimaan Peserta Didik Baru (PPDB) di SMPSejahtera 2 Cileungsi masih dilakukan secara manual sehingga menyebabkan keterlambatan respons dan penumpukan pertanyaan dari calon pendaftar. Penelitian ini bertujuan merancang dan mengembangkan sistem chatbot berbasis web yang mengimplementasikan pipeline Natural Language Processing (NLP) secara penuh, mencakup Text Preprocessing, pembobotan Term Frequency–Inverse Document Frequency (TF-IDF), dan pencocokan intent menggunakan Cosine Similarity. Tahapan Text Preprocessing meliputi case folding, cleansing, tokenization, normalisasi sinonim, stopword removal, stemming, dan ekstraksi bigram. Knowledge base disusun dari 32 intent, 655 pola pertanyaan, dan 46 jawaban yang diperoleh melalui wawancara dan observasi di SMP Sejahtera 2. Sistem dikembangkan menggunakan metode Rapid Application Development (RAD) dengan arsitektur serverless berbasis Firebase Firestore dan Vanilla JavaScript sehingga seluruh komputasi TF-IDF dan Cosine Similarity dijalankan pada sisi klien tanpa backend khusus. Adaptive threshold diterapkan secara dinamis berdasarkan keberadaan kata kunci domain PPDB untuk meningkatkan relevansi jawaban. Evaluasi akurasi model dilakukan terhadap 35 sampel pertanyaan uji yang dibagi dalam tiga kategori: in-pattern, out-of-pattern, dan out-of-domain, menghasilkan akurasikeseluruhan sebesar 82,9% . Pengujian fungsional menggunakan Black Box Testing terhadap 8 skenario seluruhnya dinyatakan valid. Evaluasi latensi menunjukkan rata-rata waktu respons chatbot sebesar 1,05 detik. User Acceptance Test (UAT) terhadap 25 responden memperoleh rata-rata kepuasan 87,4% dengan kategori Sangat Baik. Hasil penelitian membuktikan bahwa implementasi pipeline NLP berbasis TF-IDF dan Cosine Similarity mampumeningkatkan kualitas layanan informasi PPDB secara otomatis, cepat, dandapat diakses kapan saja.  \nKata Kunci: Chatbot; Cosine Similarity; Natural Language Processing; Term Frequency-Inverse Document Frequency; Text Preprocessing;  \nAbstract: The student admission (PPDB) information service at SMP Sejahtera 2 Cileungsi is still conducted manually, resulting in delayed responses and accumulation of inquiries from prospective students and parents. This study aims to design and develop a web-based chatbot system implementing a complete Natural Language Processing (NLP) pipeline, encompassing Text Preprocessing, Term Frequency–Inverse Document Frequency (TF-IDF)  \nweighting, and intent matching using Cosine Similarity. The Text Preprocessing pipeline includes case folding, cleansing, tokenization, synonym normalization, stopword removal, stemming, and bigram extraction. The knowledge base consists of 32 intents, 655 question patterns, and 46 responses collected through interviews and observations at SMP Sejahtera 2. The system was developed using the Rapid Application Development (RAD) method with aserverless architecture based on Firebase Firestore and Vanilla JavaScript, enabling all TF-IDF and Cosine Similarity computations to run client-side without a dedicated backend. An adaptive threshold mechanism was applied dynamically based on the presence of PPDB domain keywords to enhance answer relevance. Model accuracy was evaluated using 35 test question samples across three categories: in-pattern, out-of-pattern, and out-ofdomain, achieving an overall accuracy of 82.9% . Functional testing using the Black Box Testing method across 8 scenarios was entirely declared valid. The latency evaluation demonstrated an average chatbot response time of 1.05 seconds. The User Acceptance Test (UAT) invo","cbCaikKh2iqTifNl","https://ap.wps.com/l/cbCaikKh2iqTifNl","pdf",421241,12,"Indonesian","# PENDAHULUAN\n## Latar belakang Machine Learning dan NLP\n## PPDB dan kebutuhan layanan informasi responsif","[{\"question\":\"Apa masalah utama pada layanan informasi PPDB di SMP Sejahtera 2 Cileungsi?\",\"answer\":\"Layanan masih manual, menyebabkan keterlambatan respons serta penumpukan pertanyaan dari calon pendaftar.\"},{\"question\":\"Bagaimana sistem chatbot dibangun untuk memahami pertanyaan pengguna?\",\"answer\":\"Sistem memakai pipeline NLP: text preprocessing, pembobotan TF-IDF, lalu pencocokan intent menggunakan Cosine Similarity.\"},{\"question\":\"Bagaimana hasil evaluasi sistem dan indikator performanya?\",\"answer\":\"Akurasi keseluruhan mencapai 82,9%, pengujian fungsional Black Box valid pada 8 skenario, latensi rata-rata 1,05 detik, dan kepuasan UAT 87,4%.\"}]","Sistem Chatbot Informasi PPDB Berbasis TF-IDF dan Cosine Similarity Menggunakan Adaptive Threshold pada Web Sekolah | PDF",18]