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All Journal Jurnal Rekayasa dan Manajemen Sistem Industri SITEKIN: Jurnal Sains, Teknologi dan Industri JURNAL NASIONAL TEKNIK ELEKTRO Journal of Industrial Engineering Management JRSI (Jurnal Rekayasa Sistem dan Industri) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA SISTEM INDUSTRI Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal INTECH Teknik Industri Universitas Serang Raya Jurnal Informatika dan Rekayasa Elektronik Jurnal Optimalisasi Journal of Engineering Science and Technology Management greeners: journal of green engineering for sustainability Jurnal Polimesin Journal of Green Engineering for Sustainability
Fuad Dwi Hanggara
Universitas Islam Negeri Maulana Malik Ibrahim
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Perbandingan Performa Teknik Sampling Data untuk Klasifikasi Pasien Terinfeksi Covid-19 Menggunakan Rontgen Dada Akhmad Rezki Purnajaya; Fuad Dwi Hanggara
Journal of Applied Informatics and Computing Vol 5 No 1 (2021): July 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i1.3010

Abstract

The COVID-19 virus became a virus that was deadly and shocked the world. One of the consequences caused by the COVID-19 virus is a respiratory infection. The solution put forward for this problem is with a prediction of the COVID-19 virus infection. This prediction was made based on the classification of chest X-ray data. One challenging issue in this field is the imbalance on the amount of data between infected chest X-rays and uninfected chest X-rays. The result of imbalanced data is data classification that ignores classes with fewer data. To overcome this problem, the data sampling technique becomes a mechanism to make the data balanced. For this reason, several data sampling techniques will be evaluated in this study. Data sampling techniques include Random Undersampling (RUS), Random Oversampling (ROS), Combination of Over-Undersampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link (T-Link). This study also uses the Support Vector Machines (SVM) data classification, because it has high accuracy. Furthermore, the evaluation is carried out by selecting the highest accuracy and Area Under Curve (AUC). The best sampling technique found was SMOTE with an accuracy value of 99% and an AUC value of 99.32%. The SMOTE technique is the best data sampling technique for the classification of COVID-19 chest x-ray data.
Co-Authors Abdiel Khaleil Akmal Adi Nugroho Adi Nugroho Adib Adib Allin Junikhah Andersona , Kevin Ferdinand Arrazy Elba Ridha Aulia Agung Dermawan Aulia Fikriarini M. Dwi Irawan Fariz Rifqi Zul Fahmi Fisal Yuliansyah Habib Satria Hermawan, Harry Kevin Ferdinand Andersona Purnajaya, Akhmad Rezki Rama Dani Eka Putra Remba Yanuar Efranto Rizki Agam Syahputra Sugiono Sugiono Ulfa Syahputri Yasmin Zafirah