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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Semantik JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Inspiration Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Electrical Engineering and Computer Science Randwick International of Social Science Journal IJISTECH
Elviawaty Muisa Zamzami
Universitas Sumatera Utara
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Peningkatan Akurasi Metode K-Nearest Neighbor dengan Seleksi Fitur Symmetrical Uncertainty Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3254

Abstract

Accuracy of K-Nearest Neighbor (KNN) tends to be lower than other classification methods. The cause of this is related to the attributes used and the percentage of the influence of these attributes on the classification process in a data. And also attributes with less relevant influence can be a problem in determining the new class. One way that can be done to overcome this is by doing Feature Selection. In this research, the author selects features on K-Nearest Neighbor by using Symmetrical Uncertainty to remove attributes that have an unfavorable effect from the data set. Testing of the proposed method uses data sets obtained from the UCI Machine Learning Repository. The results obtained from testing the proposed method using feature selection with Symmetrical Uncertainty are able to increase the classification accuracy of KNN, with an increase in accuracy obtained after feature selection is 3.00 %.
Co-Authors Anirma Kandida Br Ginting Anirma Kandida Br Ginting Arif Ridho Lubis Boy Yako Siahaan Eko Kuswardono Budiardjo Eko Kuswardono Budiardjo Erna Budhiarti Nababan Hayanti, Septi Herman Mawengkang Indah Mulyani Mahyuddin K. M. Nasution, Mahyuddin K. M. Maya Silvi Lydia Muhammad Rizal Muhammad Zarlis Niskarto Zendrato Ogiana Kiro Opim Salim Sitompul Suherman Suherman