Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm
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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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References
O Babatunde, Leisa Armstrong, Jinsong Leng, and Dean Diepeveen. A genetic algorithm-based feature selection. International Journal of Electronics Communication and Computer Engineering, 5(4):889–905, 2014.
Erick Cantu-Paz. Feature subset selection, class separability, and genetic algorithms. In Genetic and Evolutionary Computation– GECCO 2004, pages 959–970. Springer, 2004.
Mukesh Kumar, Sandeep Singh, and Santanu Kumar Rath. Classification of microarray data using functional link neural network. Procedia Computer Science, 57:727–737, 2015.
Il-Seok Oh, Jin-Seon Lee, and Byung-Ro Moon. Hybrid genetic algorithms for feature selection. IEEE Transactions on pattern analysis and machine intelligence, 26(11):1424–1437, 2004.
Deepti Moyi Sahoo and S Chakraverty. Functional link neural network approach to solve structural system identification problems. Neural Computing and Applications, pages 1–12, 2017.
Tan Ching Siang, Ting Wai Soon, Shahreen Kasim, Mohd Saberi Mohamad, Chan Weng Howe, Safaai Deris, Zalmiyah Zakaria, Zuraini Ali Shah, and Zuwairie Ibrahim. A review of cancer classification software for gene expression data. International Journal of Bio-Science and Bio-Technology, 7(4):89–108, 2015.
Rabindra Kumar Singh and M Sivabalakrishnan. Feature selection of gene expression data for cancer classification: a review. Procedia Computer Science, 50:52–57, 2015.
C Devi Arockia Vanitha, D Devaraj, and M Venkatesulu. Gene expression data classification using support vector machine and mutual information-based gene selection. Procedia Computer Science, 47:13–21, 2015.
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