Optimasi Decision Tree menggunakan Particle Swarm Optimization untuk klasifikasi sel Pap Smear
Keywords:
Herlev, Pap Smear, Classification, Decision tree, particle swarm optimization
Abstract
This research presents the classification of Pap Smear cells. The data used is the Herlev dataset which amounts to 917. This research aims to determine whether the use of particle swarm optimization algorithm can improve the performance of the Decision tree algorithm in classifying Pap Smear Cell data. The stages of this research are preprocessing, feature optimization, knowledge rules, evaluation and performance report. The results of this research are the Decision Tree algorithm produces an accuracy of 91.39% with AUC 0.858, While the application of particle swarm optimization algorithm in Decision tree resulted in better accuracy of 96.76% with AUC 0.926.
References
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[2] M. Fauza, “Faktor yang Berhubungan dengan Deteksi Dini Kanker Serviks Metode IVA di Puskesmas Kota Padang,” vol. 14, no. 1, 2019.
[3] WHO, “Cancer,” 2020. [Online]. Available: https://www.who.int/health-topics/cancer#tab=tab_1. [Accessed: 15-Jan-2020].
[4] WHO, “Cervicial Cancer,” 2020. [Online]. Available: https://www.who.int/health-topics/cervical-cancer#tab=tab_1. [Accessed: 10-Mar-2020].
[5] A. Wardani, “Kasus Kanker Serviks Indonesia Terbanyak Kedua di Dunia,” 2019.
[6] G. C. Observatory, “Cervicial Cancer,” 2019. [Online]. Available: https://gco.iarc.fr/. [Accessed: 01-Jun-2020].
[7] K. Adrian, “Penyebab Kanker Serviks dan Faktor Risiko yang Perlu Diketahui,” 2020.
[8] D. Murtiastutik, G. Mastutik, A. Rahniayu, A. Arista, and T. Setyaningrum, “The Genotype Of Human Papilloma Virus Of Male Patients With Anogenital,” vol. 55, no. 2, pp. 100–106, 2019.
[9] A. Djie, “Virus HPV, Salah Satu Penyebab Kanker Serviks,” 2019.
[10] A. N. Sholihah and E. Sulistyorini, “Hubungan Antara Sikap Pencegahan Kanker Serviks Dengan Minat Deteksi Dini Menggunakan Inspeksi Visual Asam Asetat Pada Wanita Usia Subur Di Rw Iv Desa Cangkol Mojolaban Sukoharjo Tahun 2015,” pp. 102–116, 2015.
[11] L. Tan, “Pap Smear,” 2020. [Online]. Available: https://www.sehatq.com/tindakan-medis/pap-smear-siapa-saja-yang-membutuhkannya#:~:text=Pap smear adalah sebuah tes,serviks) yang berisiko menjadi kanker. [Accessed: 06-Jun-2020].
[12] I. Muhimmah, “Analysis of Features to Distinguish Epithelial Cells and Inflammatory Cells in Pap Smear Images,” no. Bmei, pp. 519–523, 2013.
[13] I. Muhimmah, R. Kurniawan, and Indrayanti, “Automated Cervical Cell Nuclei Segmentation Using Morphological Operation and Watershed Transformation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., pp. 163–168, 2012.
[14] T. Arifin, “Implementasi Algoritma PSO Dan Teknik Bagging Untuk Klasifikasi Sel Pap Smear,” J. Inform., vol. 4, no. 2, pp. 155–162, 2017.
[15] T. Arifin, “Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Citra Sel Pap Smear Menggunakan Analisis Tekstur Nukleus,” J. Inform., vol. 2, no. 1, pp. 287–295, 2015.
[16] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Practical Machine Learning Fourth Edition. Elsevier MK, 2017.
[17] V. Kotu and B. Deshpande, Predictive Analytics and Data Mining. Elsevier MK, 2015.
[18] D. L. Olson and D. Wu, Predictive Data Mining Models, Second Edi. Springer, 2020.
[19] N. Ye, Data Mining Theories, Algorithms, and Examples. CRC Press, 2014.
[20] T. Rosandy, “Perbandingan Metode Naive Bayes Classifier Dengan Metode Decision Tree ( C4 . 5 ) Untuk Menganalisa Kelancaran Pembiayaan ( Study Kasus : KSPPS / BMT AL-FADHILA ),” vol. 02, no. 01, pp. 52–62, 2016.
[21] S. Wahyuningsih, D. R. Utari, U. B. Luhur, D. Tree, and K. Validation, “Perbandingan Metode K-Nearest Neighbor , Naïve Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit,” pp. 8–9, 2018.
[22] T. Arifin and A. Herliana, “Optimasi Decision Tree Menggunakan Particle Swarm Optimization Untuk Identifikasi Penyakit Mata Berdasarkan Analisis Tekstur Optimizing Decision Tree Using Particle Swarm Optimization To Identify Eye Diseases,” vol. 8, no. January, pp. 59–63, 2020.
[23] T. Arifin and A. Herliana, “Optimasi Metode Klasifikasi dengan Menggunakan Particle Swarm Optimization untuk Identifikasi Penyakit Diabetes Retinopathy,” khazanah Inform., vol. 4, no. 2, pp. 77–81, 2018.
[24] T. Hidayatulloh, A. Herliana, and T. Arifin, “Klasifikasi Sel Tunggal Pap Smear Berdasarkan Analisis Fitur Berbasis Naïve Bayes Classifier,” vol. IV, no. 2, pp. 186–193, 2016.
[25] M. Sihombing and E. Manik, “Jurnal Teknologi , Kesehatan dan Ilmu Sosial Penerapan Fuzzy Dengan Particle Swarm Optimization ( PSO ) Jurnal Teknologi , Kesehatan dan Ilmu Sosial,” vol. 1, no. 1, pp. 1–4, 2019.
[26] A. Byna, “Penerapan Optimasi Pso Untuk Meningkatkan Akurasi Algortima Id3 Pada Prediksi Penyakit Ibu Hamil,” pp. 65–70.
[27] T. Arifin, D. Riana, and G. I. Hapsari, “Klasifikasi Statistikal Tekstur Sel Pap Smear Dengan Decesion Tree,” J. Inform., vol. 1, no. 1, 2014.
[28] Y. N. Dewi and F. A. Sariasih, “Metode Sample Bootstrapping Untuk Meningkatkan Performa,” vol. 12, no. 1, 2019.
[29] J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, “Pap-smear Benchmark Data For Pattern Classification Pap-smear Benchmark Data For Pattern Classification,” no. March, 2015.
[30] M. North, Data Mining for the Masses. A Global Text Project Book, 2012.
[31] Y. Zhang, S. Wang, P. Phillips, and G. Ji, “Binary PSO With Mutation Operator For Feature Selection Using Decision Tree Applied To Spam Detection,” Knowledge-Based Syst., vol. 64, pp. 22–31, 2014.
Published
2020-12-18
Section
Articles
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