INTEGRASI METODE SAMPLE BOOTSTRAPPING DAN WEIGHTED PRINCIPAL COMPONENT ANALISYS (PCA) UNTUK MENINGKATKAN PERFORMA NAÏVE BAYES PADA CITRA TUNGGAL PAP SMEAR
Abstract
Research on cervical cancer with the Pap Smear method is useful for finding pre-cancer diagnoses. Associated with previous research that the accuracy of the Naïve Bayes algorithm to the classification of a single Pap smear image still has an unsatisfactory accuracy. Whereas determining the class of single Pap cell smears is very important in determining whether these cells are normal or not. This study aims to determine whether integration using the Sample Bootstrapping (SB) method with the Weighted Principal Component Analysis (W-PCA) algorithm can improve the performance of the Naïve Bayes algorithm for seven different cell types. This model is the best solution used in the classification of datasets that are classified as having large dimensions. So that the integration of the two algorithms can increase the accuracy value to 87.24% for the seven classes and 97.30% for the two classes, and it can be concluded that with this integration model can improve the best accuracy value.
Downloads
References
Dewi, Y. N., Riana, D., & Mantoro, T. (2017). Improving Naïve Bayes performance in single image pap smear using weighted principal component analysis (WPCA). 2017 International Conference on Computing, Engineering, and Design (ICCED), 2018-March, 1–5. https://doi.org/10.1109/CED.2017.8308130
Dewi, Y. N., & Sariasih, F. A. (2019). Metode Sample Bootstrapping Untuk Meningkatkan Performa. Jurnal Teknik Informatika, 12(1), 1–10.
Firtiyani, fitriyani, & Wahono, R. S. (2015). Integrasi Bagging dan Greedy Forward Selection pada Prediksi Cacat Software dengan Menggunakan Naive Bayes. Journal of Software Engineering, 1(2), 101–108.
Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2011). Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 41(3), 552–568. https://doi.org/10.1109/TSMCA.2010.2084081
McRoberts, R. E., Magnussen, S., Tomppo, E. O., & Chirici, G. (2011). Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data. Remote Sensing of Environment, 115(12), 3165–3174. https://doi.org/10.1016/j.rse.2011.07.002
Pinto Da Costa, J. F., Alonso, H., & Roque, L. (2011). A Weighted Principal Component Analysis and Its Application to Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(1), 246–252. https://doi.org/10.1109/TCBB.2009.61
Riana, D. (2010). Hierarchical Decision Approach Berdasarkan Importance Performance Analysis Untuk Klasifikas Citra Tunggal Pap Smear Menggunakan Fitur Kuantitatif dan Kualitatif. Universitas Indonesia.
Riana, D., Plissiti, M. E., Nikou, C., Widyantoro, D. H., Mengko, T. L. R., & Kalsoem, O. (2015). Inflammatory cell extraction and nuclei detection in pap smear images. International Journal of E-Health and Medical Communications, 6(2), 27–43. https://doi.org/10.4018/IJEHMC.2015040103
Riana, D., Widyantoro, D. H., & Mengko, T. L. (2015). Extraction and Classification Texture of Inflammatory Cells and Nuclei in Normal Pap smear Images. 2015 4th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, ICICI-BME, 65–69. https://doi.org/10.1109/ICICI-BME.2015.7401336
Setiawan, T. A., Wahono, R. S., & Syukur, A. (2015). Integrasi Metode Sample Bootstrapping dan Weighted Principal Component Analysis untuk Meningkatkan Performa K Nearest Neighbor pada Dataset Besar. Journal of Intelligent Systems, 1(2), 76–81.
Susetyoko, R., & Purwantini, E. (2010). Teknik Reduksi Dimensi Menggunakan Komponen Utama Data Partisi Pada Pengklasifikasian Data Berdimensi Tinggi dengan Ukuran Sampel Kecil. 2010, 978–979.
Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., & Yao, J. (2017). DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE Journal of Biomedical and Health Informatics, 21(6), 1633–1643. https://doi.org/10.1109/JBHI.2017.2705583
Abstract viewed = 356 times
PDF downloaded = 318 times
Penulis yang menerbitkan jurnal ini menyetujui ketentuan berikut:
1. Penulis memegang hak cipta dan memberikan hak jurnal mengenai publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution 4.0 International License. yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas karya penulis dan publikasi awal pada jurnal.
2. Penulis dapat memasukkan pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi jurnal yang diterbitkan (misalnya, mengirimkannya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan atas publikasi awalnya pada Jurnal.
3. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, dalam penyimpanan institusional atau di situs web mereka) sebelum dan selama proses pengiriman, karena hal itu dapat menghasilkan pertukaran yang produktif, serta kutipan dari karya yang diterbitkan sebelumnya.
