METODE SAMPLE BOOTSTRAPPING UNTUK MENINGKATKAN PERFORMA ALGORITMA NAIVE BAYES PADA CITRA TUNGGAL PAP SMEAR
Abstract
ABSTRACT
Research on cell classification of single pap smear images is an interesting thing to discuss, where the value of consent is very important to determine whether the cells are normal or not. An example of this study is to determine whether using the bootstraping sample method can improve the performance of the Bayes naive algorithm to classify single pap smear images that are on the herlev dataset. Approval values will be given for two classes and seven classes. The method used consists of several stages, namely preprocessing, knowledge rules, evaluation, and performance reports. The results of this study prove that the bootstrap sample method can increase the accuracy of seven classes to 85.24% and 93.24% for accuracy values with two classes.
Keywords: Sample Bootstrapping; Naive Bayes; Pap Smear.
ABSTRAK
Penelitian mengenai klasifikasi sel citra tunggal pap smear menjadi hal yang menarik untuk dibahas, dimana nilai akurasi tersebut sangat penting untuk menetukan apakah sel-sel tersebut normal atau tidak. Penelitian ini bertujuan untuk menentukan apakah penggunaan metode sample bootstrapping dapat meningkatkan kinerja algoritma naive bayes untuk mengklasifikasikan citra tunggal pap smear yang ada pada dataset herlev. Nilai akurasi akan diperiksa untuk dua kelas dan tujuh kelas. Metode yang digunakan terdiri dari beberapa tahapan yaitu preprocessing, knowledge rule, evaluation, dan performance report. Hasil penelitian ini menunjukkan bahwa metode sample bootstrapping dapat meningkatkan nilai akurasi tujuh kelas menjadi 85,24% dan 93,24% untuk nilai akurasi dengan dua kelas.
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DOI: https://doi.org/10.15408/jti.v12i1.11031 Abstract - 0 PDF - 0
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