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All Journal EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN SMATIKA InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informasi dan Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System JURSIMA Jurnal Sistem Informasi dan Manajemen Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika
Odi Nurdiawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : SMATIKA

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Komparasi Algoritma Naïve Bayes dan Algoritma K-Nearst Neighbor terhadap Evaluasi Pembalajaran Daring Odi Nurdiawan; Ruli Herdiana; Saeful Anwar
SMATIKA JURNAL Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.621

Abstract

Since the outbreak of the endemic caused by the Corona virus in Indonesia, many methods have been tried, one of which is conducting remote training and encouraging students to practice from home each time. The use of digital technology in the midst of the COVID-19 endemic has a big contribution to learning institutions by practicing online learning. Students are expected to be able to accept the procedures that have been implemented by the state. However, this condition does not guarantee that students agree or accept this stage. Therefore, measurements are needed to determine the level of student happiness in carrying out online learning. With that in mind, the author conducted an experiment on the ability of the algorithm first, namely the form of grouping with the Naïve Bayes Algorithm and the K-Nearst Neighbor Algorithm. The information used is the basic information, meaning that the information obtained from the results of the questionnaire circulars for students in semester 3(3) semester 5(5) and semester 7(7) amounted to 352 respondents. In the development of the form of the algorithm using the type 9.3 rapid miner tools with the operators used are retrive, multiply, cross validation, Naïve Bayes Algorithm and knn, apply form and performance. The results of the accuracy of the Naïve Bayes Algorithm are 91.45%. The results of the accuracy of the K-Nearst Neighbor Algorithm are 97, 72%. The accuracy of the K-Nearst Neighbor Algorithm is greater than the Nave Bayes algorithm, so it can be concluded that the K-Nearst Neighbor Algorithm has good ability in grouping.
Co-Authors Abdul Jamalul'ain Abdur Rosid Ade Irma Purnamasari agus bahtiar Ahmad Bahrum Maula Rahman Ananda Rafly Aria Pratama Arif Nur Hakim Arif Rinaldi Dikananda Budi Saputra Chaerudin, Chaerudin Cindyk Irawanto Dicky Miftakhul Rizki Dwi Teguh Afandi Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fidya Arie Pratama Gifthera Dwilestari Iman Nurrohmat Irvan Himawan Liyanda Pangestu Martanto Martanto Melawati Melawati Mulyawan Mulyawan nur syarief abdullah Ruli Herdiana Saeful Anwar Shofian Yunus Syafi'i Syafi'i Teguh Abdi Mangun Tio Prasetiya Tio Prasetya