KLASIFIKASI INTI SEL PAP SMEAR BERDASARKAN ANALISIS TEKSTUR MENGGUNAKAN CORRELATION-BASED FEATURE SELECTION BERBASIS ALGORITMA C4.5

Toni Arifin

Abstract


Abstract - Pap Smear is an early examination to diagnose whether there’s indication cervical cancer or not, the process of observations were done by observing pap smear cell under the microscope. There’s so many research has been done to differentiate between normal and abnormal cell. In this research presents a classification of pap smear cell based on texture analysis. This research is using the Harlev image which amounts to 280 images, 140 images are used as training data and 140 images other are used as testing. On the texture analysis used Gray level Co-occurance Matrix (GLCM) method with 5 parameters that is correlation, energy, homogeneity and entropy added by counting the value of brightness. For choose which the best attribute used correlation-based feature selection method and than used C45 algorithm for produce classification rule. The result accuracy of the classification normal and abnormal used decision tree C45 is 96,43% and errors in predicting is 3,57%.
Keywords : Classification, Pap Smear cell image, texture analysis, Correlation-based feature selection, C45 algorithm.

Abstrak - Pap Smear merupakan pemeriksaan dini untuk mendiagnosa apakah ada indikasi kanker serviks atau tidak, proses pengamatan dilakukan dengan mengamati sel pap smear dibawah mikroskop. Banyak penelitian yang telah dilakukan untuk membedakan antara sel normal dan abnormal. Dalam penelitian ini menyajikan klasifikasi inti sel pap smear berdasarkan analisis tektur. Citra yang digunakan dalam penelitian ini adalah citra Harlev yang berjumlah 280 citra, 140 citra digunakan sebagai data training dan 140 citra lain digunakan sebagai testing. Pada analisis tekstur mengunakan metode Gray level Co-occurrence Matrix (GLCM) menggunakan 5 parameter yaitu korelasi, energi, homogenitas dan entropi ditambah dengan menghitung nilai brightness. Untuk memilih mana atribut terbaik digunakan metode correlation-based feature selection lalu digunakan algoritma C45 untuk menghasikan rule klasifikasi. akurasi yang dihasilkan dari klasifikasi normal dan abnormal yang menggunakan decision tree C45 adalah 96,43% dan kesalahan dalam memprediksi adalah 3,57%.
Kata Kunci: Klasifikasi, Citra sel Pap Smear, Analisis tekstur, Correlation-based feature selection, Algoritma C45.

References


Arifin T, Riana D, dan Hapsari, G.I. (2013) : Klasifikasi Statistikal Tekstur Sel Pap Smear Dengan Decesion Tree, Jurnal Informatika, Universitas BSI Bandung, 1, 38.

Dalimartha S. (2004) : Deteksi Dini Kanker & Simplisia Antikanker. Jakarta: Penebar Swadaya Jakarta.

Gonzalez R.C, Woods R.E, & Eddins S.L. (2003) : Digital Image Processing Using MATLAB, 11-12

Haralick R.M, Shanmugan K, & Dinstein I. (2003) : Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, 610-621.

Indriayani C, & Riana D. (2010) : Prediction Image Pap Smear Web Based With Decision Tree. STIMIK Nusa Mandiri , 1-5.

Jantzen J, Norup G.J, Dounias, & Bjerregaard B. (2005) : Pap-smear Benchmark Data For Pattern Classification, Technical University of Denmark, 1-20.

Muhimmah I, Anwariyah K, & Indrayanti. (2012) : Extraction and Selection Features of Cervical Cell Types in Pap Smear Digital Images. Wise Health ITB, 1-7.

Mathworks. (2012) : from Matrix Laboratory: http://www.mathworks.com/. (25 Desember 2012).

Martin E. (2003) : Pap-Smear Classification. From Technical University of Denmark: http://labs.fme.aegean.gr/decision/downloads/ (25 Desember 2012).

Novitasari. (2010) : Analisis Identifikasi Serviks Normal dan Abnormal Berdasarkan Filter Gabor dan Ekstraksi Ciri Tekstur Statistik. Universitas Gunadarma , 1-7.

Prasetyo E. (2011) : Pengolahan Citra Digital Dan Aplikasinya Menggunakan Matlab. 1-2.

Pratama G, Riana D, & Hasanudin (2012) : Pap Smear Nucleus Texture Analysis. ITB , 1-4.

Riana D, widyanto D.H, & Mengko T.L. (2012) : Perbandingan Segmentasi Luas Nukleus Sel Normal dan Abnormal Pap smear Menggunakan Operasi Kanal Warna dengan Deteksi Tepi Canny dan Rekontruksi Morphologi. Wise health ITB , 1-2.

Selinger S. (2010) : Image Procesing and Texture Analysis. Dennis GaborCollege , 1-7. 1-20.

Suprapto. (2010) : Penggunaan Pengolahan Citra Digital Pada Pemeriksaan Pap Smear Dalam Pendeteksian Kanker Serviks. Universitas Brawijaya , 1-10.

WHO (2013) : WHO Guidance note. Number of pages 12 Publication 2013. From http://www.who.int/reproductivehealth/publication s/cancers/9789241505147/en/index.html (19januari 2013).

Zuiderveld K. (2000) : Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV. SanDiego: Academic Press Professional , 474–485.




DOI: https://doi.org/10.31294/ji.v1i2.48

Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 Jurnal Informatika



Index by:

 
 Published LPPM Universitas Bina Sarana Informatika with supported by Relawan Jurnal Indonesia

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Jakarta Pusat, DKI Jakarta 10450, Indonesia
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License