[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117019-id":3,"doc-seo-117019-113":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},117019,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",52,"Teknologi","Implementasi Machine Learning untuk Identifikasi Orang Batuk/Bersin","Implementasi machine learning untuk identifikasi orang yang batuk atau bersin, dengan fokus pada klasifikasi suara pada kondisi berisik (noise) di data mentah. Penelitian menekankan pentingnya ekstraksi fitur dari sinyal, lalu membangun program berbasis Long Short Term Memory (LSTM) untuk mengenali pola suara. Rangkaian kerja mencakup pre-processing, training, dan prediction. Hasil menunjukkan performa rata-rata 68,52% akurasi, 88,10% presisi, serta 62,03% recall.","IMPLEMENTASI MACHINE LEARNING UNTUK IDENTIFIKASI ORANG BATUK/BERSIN  \nSandy Bhawana Mulia1, Nur Wisma Nugraha2, Muhammad Hanif Robbani3 Teknik Otomasi Manufaktur & Mekatronika, Politeknik Manufaktur Bandung 1,2,3 email 1 : [sandy@ae.polman-bandung.ac.id](sandy@ae.polman-bandung.ac.id)  \nAbstract  \nThe most important thing in machine learning is feature extraction from a raw data. In this journal will be explained regarding Implementation of Machine Learning for identification people coughing/sneezing. The main objective of this research is how to classify the sound of sneezing and coughing in the situation as difficult as the noise contained in the raw data. The development of artificial intelligence is quite rapid especially on sound classification using machine learning can help to distinguish coughs and sneezes based on voice. In this final project, the Long Short Term algorithm Memory (LSTM) is used because the algorithm is capable classify the sound pattern of a data. For produce a cough detection program the author makes 3 The main programs are Pre-Processing, Training, and Prediction. Through the method used, Machine Implementation Learning to identify people coughing/sneezing capable achieved an average value of 68.52% accuracy, 88.10% precision and recall 62.03%.  \nKeywords: Cough, LSTM, Machine Learning, Sneeze.  \nAbstrak  \nHal terpenting pada machine learning adalah fitur ekstraksi dari sebuah data mentah. Pada jurnal ini akan dijelaskan mengenai implementasi machine learning untuk identifikasi orang batuk/bersin. Tujuan utama penelitian ini adalah bagaimana mengklasifikasi suarabersin dan batuk pada situasi yang sulit seperti noise yang terdapat pada data mentah. Perkembangan kecerdasan buatan yang cukup pesat terutama pada klasifikasi suara menggunakan machine learning dapat membantu untuk membedakan batuk dan bersin berdasarkan suara. Dalam tugas akhir ini, algoritmaLong Short Term Memory (LSTM) digunakan karena algoritma ini mampu mengklasifikasikan pola suaradari suatu data. Untuk menghasilkan program pendeteksi batuk penulis membuat 3 program utama yaitu Pre-Processing, Training, dan Prediction. Melalui metode yang digunakan, implementasi machine learning untuk identifikasi orang batuk/bersin mampu mencapai nilai rata-rata akurasi 68,52%, presisi 88,10% dan recall 62,03% .  \nKata Kunci: Batuk, Bersin, LSTM, Machine Learning.  \nI. PENDAHULUAN  \nBatuk merupakan mekanisme pertahanan tubuh disaluran pernapasan dan merupakan gejala suatu penyakit ataureaksi tubuh terhadap iritasi di tenggorokan karena adanyalendir, makanan, debu, asap dan sebagainya [1] . Pada seseorang yang mengalami batuk kronis, baik frekuensi dan intensitas batuk sangat penting untuk menentukan keparahanbatuk yang dialami oleh orang tersebut secara umum [2] . Tingkat kekuatan batuk dapat diukur secara objektifberdasarkan aliran batuk yang keluar [3] .  \nBersin adalah keluarnya udara semi otonom yang terjadidengan keras lewat hidung dan mulut. Udara ini dapat mencapai kecepatan 70 m/detik (250 km/jam) . Bersin dapat menyebarkan penyakit lewat butir-butir air yang terinfeksi yang diameternya antara 0,5 hingga 5 µm. Sekitar 40.000 butir air seperti itu dapat dihasilkan dalam satu kali bersin [4] .  \nBaik batuk maupun bersin ketika hal tersebut dialami oleh seseorang maka dua hal tersebut akan mengakibatkan keluarnya saliva dari dalam mulut, dimana di dalam saliva dapat mengandung berbagai macam jenis bakteri, virus, sertamikroorganisme lainnya yang dapat membahyakan tubuh. melalui pengukuran secara subjektif demikian muncul kebutuhan untuk menciptakan alat pendeteksi batuk untuk mengatasi penyebaran mikrooganisme yang berbahaya tersebut.  \nDalam penelitian ini diharapkan penulis dapat mengembangkan sistem pendeteksi batuk dan bersin dengan menggunakan metode machine learning berbasis python untuk mengklasifikasikan jenis batuk dan bersin dengan menambahkan sistem penyemprotan berkala setiap 2 jam, serta penulis mencoba untuk menambahkan sistem IOT","cbCaik5PQzRV1WcT","https://ap.wps.com/l/cbCaik5PQzRV1WcT","pdf",628748,3,1,6,"Indonesian","id",113,"# Pendahuluan\n# Kajian Pustaka\n## Machine Learning\n## Long Short Term Memory Network (LSTM)","[{\"question\":\"Apa tujuan utama penelitian implementasi machine learning ini?\",\"answer\":\"Mengklasifikasikan suara batuk dan bersin pada kondisi yang sulit akibat noise pada data mentah.\"},{\"question\":\"Mengapa algoritma LSTM digunakan pada tugas akhir ini?\",\"answer\":\"LSTM digunakan karena mampu mengklasifikasikan pola suara dan memanfaatkan informasi dari data secara berurutan.\"},{\"question\":\"Langkah program apa saja yang dibuat untuk menghasilkan pendeteksi batuk?\",\"answer\":\"Penulis membuat tiga program utama: pre-processing, training, dan prediction.\"}]","Implementasi Machine Learning untuk Identifikasi Orang Batuk/Bersin | 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tujuan utama penelitian implementasi machine learning ini?","Question",{"text":76,"@type":77},"Mengklasifikasikan suara batuk dan bersin pada kondisi yang sulit akibat noise pada data mentah.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Mengapa algoritma LSTM digunakan pada tugas akhir ini?",{"text":81,"@type":77},"LSTM digunakan karena mampu mengklasifikasikan pola suara dan memanfaatkan informasi dari data secara berurutan.",{"name":83,"@type":74,"acceptedAnswer":84},"Langkah program apa saja yang dibuat untuk menghasilkan pendeteksi batuk?",{"text":85,"@type":77},"Penulis membuat tiga program utama: pre-processing, training, dan prediction.","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},[94,99,103,107,111,115,119,123,125,129,133],{"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 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