Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Tahtri Nadia Utami
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementasi Fuzzy k-Nearest Neighbor (Fk-NN) untuk Klasifikasi Jenis Kanker berdasarkan Susunan Protein Tahtri Nadia Utami; Marji Marji; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (288.21 KB)

Abstract

Cancer is the most deadly disease besides heart disease. A common cause of cancer is gene mutation in protein 53 that serves to control the replication of DNA as a regulator of the cell function resulting in the wrong protein sequence. The protein sequences is used as a basis to classifying the types of cancer and then it can ease in determining the right handling or therapeutics method. The classification of cancer using the Fuzzy k-Nearest Neighbor (Fk-NN) method. The data used are 752 protein sequences with 393 sequence length on every sequence. The classification class includes non-cancer, breast cancer, collorectal cancer and lung cancer. The Fk-NN method calculates the degree of membership of each class at the k smallest distances generated from k-Nearest Neighbor method. The highest average accuracy rate is 52.56% of the test results using k-fold-validation. The optimal k value of the Fk-NN method is k = 5 with the average accuracy rate of 54.99%. The large variation in the amount of training data that is 90% of the dataset results in the highest accuracy rate of 55.33%.
Co-Authors Lailil Muflikhah Marji Marji