Many acute respiratory infections or ARI are caused by viruses that attack the nose, trachea (breathing tube), or the lungs. It can be said that ARI is caused by inflammation that disrupts a person's breathing process. If not treated quickly, ARI can spread to all respiratory systems and prevent the body from getting proper oxygen, moreover it can cause the loss of a person's life. This research aims to diagnose ARI as an early step in practicing artificial intelligence in medicine, designing and apply an expert system that can diagnose ARI. The procedure used in this study uses forward chaining with tracking that begins with input data, and then creates a diagnosis or solution. The expert system used to diagnose acute respiratory inflammation uses the Forward chaining procedure with a data-driven approach, in this approach tracking starts from input data, and then seeks to draw conclusions, so that it can be used. diagnose the type of disease associated with the ARD disease experienced by showing the existing signs.
Pada masa ini di zaman big data , penggunaan media sosial sering kali membuat postingan di akun media social miliknya berupa opini-opini terhadap kejadian dan barang disekitarnya, salah satunya yaitu membuat suatu postingan yang memberikan opini pada suatu barang sehingga kita jadi tahu dampak atau pandangan publik dari suatu produk pada kasus ini yaitu jafra. Adapun beberapa tahapan-tahapan dalam penelitian ini diawali dari pengumpulan data yang dilakukan dengan mengcollect data tweet pada media social twitter sebanyak 1.000 tweet yang berkaitan dengan produk jafra, selanjutnya dilakukan pra-prosesing untuk mencari kata-kata yang sering muncul didalam tweet. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap dampak dari penjualan produk Jafra dimasa pandemic virus covid-19 ini, sehingga membantu usaha penjualan untuk melakukan riset atas opini publik. Klasifikasi algoritma seperti Naive Bayes (NB), K-Nearest Neighbor (k-NN) dan Decision Tree yang diusulkan oleh banyak peneliti untuk digunakan dalam analisis sentimen teks. Ketiga algoritma dan metodenya, akan diuji dengan dua masukan dengan menggunakan komentar Tokenize and Transform Case yang positif dan negatif , akurasi yang didapat algoritma Naïve Bayes accuracy: 74.92%, k-NN accuracy: 76.22%, Decision Tree accuracy: 77.85% Hasil penelitian menunjukkan bahwa algoritma Decisoin Tree mendapatkan hasil terbaik dan akurat
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