Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 3 No 1 (2019): Januari 2019

Implementasi Fuzzy k-Nearest Neighbor (Fk-NN) untuk Klasifikasi Jenis Kanker berdasarkan Susunan Protein

Tahtri Nadia Utami (Fakultas Ilmu Komputer, Universitas Brawijaya)
Marji Marji (Fakultas Ilmu Komputer, Universitas Brawijaya)
Lailil Muflikhah (Fakultas Ilmu Komputer, Universitas Brawijaya)



Article Info

Publish Date
27 Aug 2018

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%.

Copyrights © 2019






Journal Info

Abbrev

j-ptiik

Publisher

Subject

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Description

Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian ...