Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm

Authors

  • Putri Tsatsabila Ramadhani Telkom University
  • Untari Novia Wisesty Telkom University
  • Annisa Aditsania Telkom University

DOI:

https://doi.org/10.21108/INDOJC.2017.2.2.173

Abstract

Di beberapa tahun terakhir, pemanfaatan teknologi microarray memiliki pengaruh besar dalam menentukan gen informatif yang menyebabkan kanker. Micorarray mampu menentukan ekspresi ribuan gen dan secara simultan memantau proses bilogis yang sedang berlangsung. Dengan melakukan analisa terhadap data micorarray, selanjutnya ekspresi dari ribuan gen yang merepresentasikan suatu jaringan pada manusia, akan diklasifikasikan sebagai jaringan kanker atau bukan. Dalam penulisan penelitian penelitian, penulis meng-implementasikan Functional Link Neural Network dengan fungsi basis Legendre Polynomial untuk klasifikasi data yang akurat dan menggunakan Genetic Algorithm sebagai seleksi fitur untuk mereduksi data berdimensi tinggi yang sering ditemukan pada data microarray. Dengan serangkaian proses yang telah dilakukan, maka diperoleh kinerja tertinggi terhadap klasifikasi data microarray Colon Tumor sebesar 92.3% dan Leukemia sebesar 87.5%. Perbedaan kinerja yang diperoleh disebabkan oleh perbedaan karakteristik masing-masing data.

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Author Biography

Putri Tsatsabila Ramadhani, Telkom University

Saya Putri Tsatsabila Ramadhani, mahasiswa tingkat akhir di Universitas Telkom Program Studi Ilmu Komputasi yang sedang melakukan penelitian terkait tugas akhir saya tentang pengolahan data microarray untuk tujuan deteksi kanker.

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Published

2017-11-20

How to Cite

Ramadhani, P. T., Novia Wisesty, U., & Aditsania, A. (2017). Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm. Indonesian Journal on Computing (Indo-JC), 2(2), 11–22. https://doi.org/10.21108/INDOJC.2017.2.2.173

Issue

Vol. 2 No. 2 (2017): September, 2017

Section

Computer Science

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