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Journal : Generation Journal

Sistem Pendukung Keputusan Menggunakan Certainty Factor Dalam Mendiagnosa Kategori Tingkat Demam Berdarah Achmad Rizaldi; Apriade Voutama; Susilawati Susilawati
Generation Journal Vol 5 No 2 (2021): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v5i2.16015

Abstract

Dengue Fever is still a serious health problem and a major problem in the health sector in various parts of the world and also in Indonesia. This disease is an infectious disease and can infect all ages because there are still many people who still lack awareness in maintaining cleanliness to anticipate transmission. Data from the Ministry of Health of the Republic of Indonesia (Kemenkes RI) Indonesia recorded the number of people infected with Dengue Fever disease as many as 95,893 throughout 2020 and as many as 661 people died from this disease. The method used is using the certainty factor method where if it is interpreted this method will be used to help make a decision to diagnose the category of Dengue Fever level with a level of confidence in the form of a percentage of how much someone is infected with Dengue Fever in the existing category. The results obtained in the calculation of certainty factor get a result in the category of grade 1 of 98%, grade 2 of 98%, grade 3 of 99%, grade 4 of 99%. These results are enough to prove that this certainty factor method has a fairly high percentage level result.
Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19 Euis Saraswati; Yuyun Umaidah; Apriade Voutama
Generation Journal Vol 5 No 2 (2021): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v5i2.16125

Abstract

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.
Co-Authors Abdussalam Amrullah Achmad Rizaldi Ade Purwanto ade, Nofri Adella Putri Riani Adella Putri Riani Adhi Rizal Aditiya Yoga Pratama Agnia Virli Rosdianty Agung Susilo Yuda Irawan Ahmad Ray Septa Firdaus Ahmad Ridhoi Fajri Ahzka Nabbilah Tuzzahrah Andari Yasinta Permana Andika Prasetyo Arip Solehudin Ariq Zeidan Mubarok Arsya Fathiarahma Asep Jamaludin Asep Jamaludin Azhari Ali Ridha Bayu Adhi Pamungkas Bella Nurseptia Billy Nugraha Budi Arif Dermawan Carudin Chaerur Rozikin Cika Oktavia Dadang Yusup Dea Safryda Putri Devi Fitriani Maulana Dias Raihan Azahri Dica Parameswari Syifa Dewi Didi Shaldy Rahma Dikky Setiyanto Dimas Aditya Nugroho Dodi Wahyono Dwi Latifah Rianti Elfina Novalia Elis Elis Ergy Rahmatan Ramadhan Euis Saraswati Fabiano Milan Almufqi Fakhri Muhammad Farras Salsabila Ganes Wisnu Cahya Bagaskara Garno Garno Garno Garno, Garno Gunadi Widi Nurcahyo Heryana, Nono Ika Fitrianti Indra, Jamaludin Intam Purnamasari Iqbal Maulana Ismail Adhiya Adha Joice Margaretha Jufriadif Na`am, Jufriadif Kamal Prihandani Khofifah Wulandari Khoirunnisa Hamidah Khoirunnisa Hamidah Lilis Nurjanah Sukaryati Mayland Trifena Mayland Trifena Mentari Hasibuan Miftah Fariedh Andriansyah Milla Rochmawati Muhamad Arya Fadila Muhammad Adrian Maulana Muhammad Fadhil Ar Rizqi Muhammad Fahmi Prasetio Muhammad Fiqri Widiyantoro Muhammad Fiqri Widiyantoro Muhammad Nur Yasin Muhammad Rafi Nahjan Nailufar Farha Afifah Najwa Nurshadrina Nana Mulyana Maghfur Nina Sulistiyowati Nina Sulistyowati Nono Haeryana Novalia, Elfina Rahman Maulana Ramayanti, Cindi Rianti Surya Sukatma Richard Julianno Soeganda Rihan Maulana Rini Mayasari Riza Ibnu Adam Rizal, Adhi Rizky Syaputra Salsabila Aini Rahmah Siska Suparno Susilawati Susilawati Syahri Susanto Syahrul Dwi Hilda Tasyifa Nafsiah Muthmainnah Taufik Maulana Taufik Ridwan Taufik Ridwan, Taufik Ultach Enri Vickry Ramadhan Wahyono, Dodi Winanda Bagaskara Yunitasari Yunitasari Yusma Cantika Parhati Yuyun Umaidah Yuyun Umaidah