Article Per Year (5 Year)
p-Index From 2021 - 2026
2.286
P-Index
This Author published in this journals
All Journal Jurnal Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Science and Technology Indonesia Jurnal Penelitian Sains BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT KOMPUTEK Inferensi Diseminasi : Jurnal Pengabdian kepada Masyarakat Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal Pepadu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Jurnal Matematika Integratif
Endro Setyo Cahyono, Endro Setyo
Jurusan Matematika, Fakultas MIPA, Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : KOMPUTEK

 1 
Klasifikasi Gejala Awal Covid-19 dengan Algoritma Classification and Regression Tree (Cart) Agung Alamsyah; Anita Desiani; Endro Setyo Cahyono
KOMPUTEK Vol 7, No 2 (2023): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v7i2.2095

Abstract

COVID-19 is a disease that can cause death and can spread to others. By identifying early symptoms of the disease, early detection can be made for several symptoms that may cause COVID-19. One way to predict COVID-19 is through classification methods. By identifying the symptoms that have an impact on COVID-19, it is hoped that the COVID-19 virus can be stopped from spreading and the world's condition can be normal. This study shows an analysis of attributes that may have an impact on the onset of COVID-19 in an individual. The classification method used is one of the decision tree methods, namely the Classification and Regression Tree (CART). The training and testing methods used in this study are cross-validation and percentage split. The attribute that has a significant influence in this classification using CART method is lung infection. The performance of the system using cross-validation method with a value of k of 10 obtained an accuracy of 85%, which is considered good, while using a percentage split of 66%, an accuracy of 87% was obtained. The evaluation results for the class indicating COVID-19 with precision and recall in cross-validation are 70% and 68%, respectively, while for the percentage split method, precision and recall values of 75% and 70% were obtained, respectively.
Klasifikasi Gejala Awal Covid-19 dengan Algoritma Classification and Regression Tree (Cart) Alamsyah, Agung; Desiani, Anita; Cahyono, Endro Setyo
KOMPUTEK Vol. 7 No. 2 (2023): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v7i2.2095

Abstract

COVID-19 is a disease that can cause death and can spread to others. By identifying early symptoms of the disease, early detection can be made for several symptoms that may cause COVID-19. One way to predict COVID-19 is through classification methods. By identifying the symptoms that have an impact on COVID-19, it is hoped that the COVID-19 virus can be stopped from spreading and the world's condition can be normal. This study shows an analysis of attributes that may have an impact on the onset of COVID-19 in an individual. The classification method used is one of the decision tree methods, namely the Classification and Regression Tree (CART). The training and testing methods used in this study are cross-validation and percentage split. The attribute that has a significant influence in this classification using CART method is lung infection. The performance of the system using cross-validation method with a value of k of 10 obtained an accuracy of 85%, which is considered good, while using a percentage split of 66%, an accuracy of 87% was obtained. The evaluation results for the class indicating COVID-19 with precision and recall in cross-validation are 70% and 68%, respectively, while for the percentage split method, precision and recall values of 75% and 70% were obtained, respectively.
Co-Authors Agung Alamsyah Alamsyah, Agung Alfensi Faruk Ali Amran Anita Desiani Armalia, Divasanda Asri Kuntari Asri Kuntari, Asri Bambang Suprihatin Des Alwine Zayanti, Des Alwine Dewi, Deshinta Arrova Dian Cahyawati Eka susanti Eka Susanti Eka Susanti Elisa Nurnawati Evi Yuliza, Evi Fitri Maya Puspita Fitri Maya Puspita Indrawati Indrawati Indrawati Muhammad 'Aqil Muhammad Alifian Nuriman Muji Gunarto Nadya Amalia Yulianti Ning Eliyati Novi Rustiana Dewi Oki Dwipurwani Permata, Anggun Pratiwi, Ananda Putra Bahtera Jaya Bangun, Putra Bahtera Jaya Putra BJ Bangun, Putra BJ Rima Kesuma Robinson Sitepu Sarjani, Ita Sisca Octarina Villando, Gio Yanita, Nurul Fadhila Yuli Andriani