METODE DATA MINING UNTUK KLASIFIKASI DATA SEL NUKLEUS DAN SEL RADANG BERDASARKAN ANALISA TEKSTUR

Toni Arifin

doi:10.31294/ji.v2i2.125

METODE DATA MINING UNTUK KLASIFIKASI DATA SEL NUKLEUS DAN SEL RADANG BERDASARKAN ANALISA TEKSTUR

Toni Arifin

Abstract

ABSTRACT - The Pap Smear test is done to see the presence of infection or changes in cells that can turn into cancer cells. In this research is using data on analysis results of texture image processing on previous research that is using a nucleus cell and inflammation cell in the image Pap Smear cell. The purpose of this research is to find the best method for classifying the nucleus cell and inflammation cell based on texture analysis GLCM (Gray Level Co-occurrence Matrix) in this research used of method Decision tree (C 4.5), Naive Bayes and k-Nearest Neighbour. The results of this research brings about the best methods for classification of the data nucleus cell and inflammation cell that is a method of Decision tree (C4.5) with accuracy 97,56% whereas results for Naive Bayes 90,89% and k-Nearest Neighbour 95,97%.
Keywords: Data mining, classification, Pap Smear cell, Texture Analysis

ABSTRAKSI - Tes Pap Smear dilakukan untuk melihat adanya infeksi atau perubahan sel-sel yang dapat berubah menjadi sel kanker. Pada penelitian ini menggunakan data analisis tekstur yang didapatkan dari hasil pengolahan citra pada penelitian sebelumnya yaitu menggunakan sel nukleus dan sel radang pada citra sel Pap Smear. Tujuan dari penelitian ini adalah mencari metode terbaik untuk mengklasifikasikan sel nukleus dan sel radang berdasarkan analisa teksur GLCM (Gray Level Co-occurrence Matrix) Metode yang digunakan dalam penelitian ini adalah metode Decision tree (C4.5), Naive Bayes dan k-Nearest Neighbour. Hasil dari penelitian ini didapatkan metode terbaik untuk klasifikasi data sel nukleus dan sel radang yaitu metode Decision tree (C4.5) dengan akurasi 97,56% sedangkan hasil untuk Naive Bayes 90,89% dan k-Nearest Neighbour 95,97%.
Kata Kunci: Data mining, Klasifikasi, Sel Pap Smear, Analisa Tekstur

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References

Alfisahrin C4.5, Naive Bayes dan Neural Network, S. (2014). Komparasi Algoritma Untuk Memprediksi Penyakit Jantung. Jakarta: Pascasarjana Magister Ilmu Komputer STMIK Nusa Mandiri.

Al-Naggar, R. A. (2012). Population Health and Preventive Medicine Department, Faculty of Medicine, University Teknologi MARA (UiTM), Malaysia. In Cervical Cancer: Prevention and Control.

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

Arifin, T., & Riana, D. (2015). Metode Analisa Tekstur Untuk Klasifikasi Sel Nukleus Dan Sel Radang Pada Citra Mikroskopik Pap Smear. Jakarta: STIMIK Nusa Mandiri Jakarta.

Ayres, F., Rangayyan, R., & Desautels, J. (2011). Analysis of Oriented Texture with Applications to the Detection of Architectural Distortion in Mammograms.

Bramer, M. (2013). Principle of Data Mining Second Edition. London: Springer.

Bruni, L., Barrionuevo-Rosas, L., Albero, G., Aldea, M., Serrano, B., Valencia, B., et al. (2015). Human Papillomavirus and Related Diseases Report. In Human Papillomavirus and Related Diseases Report (pp. 4-7). Barcelona, Spain: ICO Information Centre on HPV and Cancer (HPV Information Centre).

Burger, W., & Burge, M. J. (2007). Digital Image Processing.

CancerHelp. (2015). Retrieved july 1, 2015, from http://www.cancerhelp.org/

Dawson, C. (2009). Projects in Computing and Information Systems. London: Addison Wesley.

Fauzi, M., & Tjandrasa, H. (2010). Implementasi Thresholding Citra Menggunakan Algoritma Hybrid Optimal Estimation.

Fatima, M., & Seenivasagam , V. (2012). A hybrid image segmentation of cervical cells by bi-group enhancement and scan line filling. International Journal of Computer Science and Information Technology & Security (IJCSITS), 368-375.

Hassan, A., & Pei, Z. (2015). Pap Smear Images Classification for Early Detection of Cervical Cancer. International Journal of Computer Applications , 10-17.

Herliana, A., & Riana, D., (2013). Klasifikasi Sel Tunggal Pap Smear Berdasarkan Analisis Fitur dan Analisis Tekstur Terseleksi Menggunakan CFS Berbasis Decision Tree J48. STMIK Nusa Mandiri. Jakarta.

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

Kementrian Kesehatan. (2015). Retrieved july 23, 2015, from http://www.depkes.go.id/

livescience. (2015). Retrieved july 14, 2015, from http://www.livescience.com/

Matrix Laboratory. (2015). Retrieved july 5, 2015, from http://www.mathworks.com/

Marina, E., & Christophoros , N. (2009). Automated segmentation of cell nuclei in Pap Smear images. IEEE.

Marina, E., & Christophoros , N. (2010). Accurate localization of cell nuclei in Pap Smear images using gradient vector flow deformable models. IEEE.

Marina, E., & Christophoros , N. (2011). Accurate Localization Of Cell Nuclei In Pap Smear Images Using Gradient Vector Flow Deformable Models. Department of Computer Science, University of Ioannina, Ioannina, Greece .

Marina, E., & Christophoros , N. (2012). Overlapping Cell Nuclei Segmentation Using a Spatially Adaptive Active Physical Model. IEEE Transactions On Image Processing, 4568-4580.

Marina, E., & Nikou, C. (2012). Overlapping Cell Nuclei Segmentation Using a Spatially Adaptive Active Physical Model. IEEE.

Marina, E., Christophoros, N., & Charchanti, A. (2011). Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering. IEEE Transactions On Information Technology In Biomedicine, 233-241.

Martin, E. (2003). Pap-Smear Classification. Technical University of Denmark. Diambil dari: http://labs.fme.aegean.gr/decision/downloads/ (25 july 2015).

Maimon, O., & Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook. Springer.

Muhimmah, I., Kurniawan , R., & Indrayanti . (2012). Automated Cervical Cell Nuclei Segmentation Using Morphological Operation and Watershed Transformation. IEEE, 163-168.

Muhimmah, I., Kurniawan, R., & Indrayanti. (2013). Analysis of Features to Distinguish Epithelial Cells and Inflammatory Cells in Pap Smear Images. International Conference on Biomedical Engineering and Informatics (BMEI- IEEE), 519-523.

Moshavegh, R., & Ehteshami, B. B. (2013). Chromatin pattern analysis of cell nuclei for improved cervical cancer screening. Gothenburg, Sweden.

Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E., & Barrier, T. (2013). Different Approaches for Extracting Information from the Co-Occurrence Matrix.

National Cancer Institute. (2015). Retrieved july 1, 2015, from www.cancer.gov: http://www.cancer.gov/.

Nurtanio, I., Astuti, E. R., Purnama, E. I., Hariadi, M., & Purnomo

, M. H. (2013). Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features. IAENG International Journal of Computer Science.

Oscanoa, J., Mena, M., & Kemper, G. (2015). A Detection Method of Ectocervical Cell Nuclei for Pap test Images, Based on Adaptive Thresholds and Local Derivatives. International Journal of Multimedia and Ubiquitous Engineering, 37-50.

Prasetyo, E. (2011). Pengolahan citra digital dan aplikasinya menggunakan Matlab. Yogyakarta: Penerbit Andi.

Pratama, G. K., Riana, D., & Hasanudin. (2012). Pap Smear Nuclei Texture Analysis. International Conference on Women's Health in Science & Engineering. Bandung: Institut Teknologi Bandung, 1-4.

Rangayyan, R., Nguyen, T., Ayres, F., & Nandi, A. (2010). Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms.

Riana, D., Marina, E., Christophoros, N., Dwi, H., Tati Latifah , R., & Kalsoem, O. (2015). Inflammatory Cell Extraction and Nuclei Detection in Pap Smear Images. International Journal of E-Health and Medical Communications, 27-43.

Riana, D. (2010). Hierarchical Decision Approach Berdasarkan Importance Performance Analysis Untuk Klasifikasi Citra Tunggal Pap Smear Menggunakan Fitur Kuantitatif dan Kualitatif .

Smith, J. S., Lindsay , L., Hoots, B., Keys , J., Franceschi, S., Winer , R., et al. (2007). Human papillomavirus type distribution in invasive cervical cancer and high-grade cervical lesions: A meta-analysis update. the International Union Against Cancer.

Sreedevi, M., Usha, B., & Sandya, S. (2012). Papsmear Image based Detection of Cervical Cancer. International Journal of Computer Applications .

Sokouti , B., & Haghipour , S. (2011). A Pilot Study on Image Analysis Techniques for Extracting Early Uterine Cervix Cancer Cell Features. Springer Science+Business Media, LLC .

Szeliski, R. (2010). Computer Vision: Algorithms and Applications.

Tareef, A., Yang , S., Weidong , C., David , D., Feng, & Mei, C.

(2014). Automated Three-Stage Nucleus and Cytoplasm Segmentation of Overlapping Cells. IEEE.

Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. Cornwall: John Wiley & Sons, Ltd.

Vistekdatabase. (2015). Retrieved juli 23, 2015, from http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

WHO. (2015). Retrieved juny 1, 2015, from WHO: http://www.who.int/en/

Witten, I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques 2nd Edition. USA: Elsevier.

Xu, D.-H., Kurani, A., Furst, J., & Raicu, D. (2004). Run-length encoding for volumetric texture. School of Computer Science, Telecommunications, and Information Systems, DePaul University, Chicago, Illinois. USA.

DOI: https://doi.org/10.31294/ji.v2i2.125