Toni Arifin


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

Teks Lengkap:



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