KLASIFIKASI KANKER SERVIKS MENGGUNAKAN METODE EXTREME LEARNING MACHINE (ELM)
Abstract
Kanker serviks merupakan salah satu penyakit kanker yang terjadi pada organ reproduksi wanita. Kanker serviks ini terjadi ketika sel-sel di leher rahim berubah menjadi sel kanker. Penyebab utama kanker serviks adalah Human Papilloma Virus (HPV) yang ditularkan melalui hubungan seksual. HPV adalah sekelompok virus yang umumnya menginfeksi saluran reproduksi pria dan wanita yang aktif secara seksual. Penelitian ini memanfaatkan teknologi Artificial Intelligence untuk mengidentifikasi tingkat keshalehan secara otomatis menggunakan metode Extreme Learning Machine (ELM) demi menilai penderita kanker servikc sejak dini. Serta sebagai pedoman untuk klasifikasi tingkat penderita kanker serviks. Hasil dari sistem yang dibangun berdasarkan data dengan parameter Kfold 3 pada neuron 400 menghasilkan akurasi 83,3%, Sensitifity 84%, serta specificity sebesar 80%. Sedangkan akurasi rata-rata tertinggi sebesar 73,22% dengan menggunakan Kfold 4. Untuk rata-rata keseluruhan percobaan yaitu 72,46%.
Keywords
References
A. Ghoneim, G. Muhammad, and M. S. Hossain, “Cervical cancer classification using convolutional neural networks and extreme learning machines,” Futur. Gener. Comput. Syst., vol. 102, pp. 643–649, 2020, doi: 10.1016/j.future.2019.09.015.
N. Rijati, D. Purwitasari, S. Sumpeno, and M. Hery Purnomo, “Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior),” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 1, pp. 25–34, 2020, doi: 10.22146/jnteti.v9i1.118.
C. Yuan, Y. Yang, and Y. Liu, “Sports decision-making model based on data mining and neural network,” Neural Comput. Appl., vol. 0123456789, 2020, doi: 10.1007/s00521-020-05445-x.
A. Lomuscio and L. Maganti, “An approach to reachability analysis for feed-forward ReLU neural networks,” 2017, [Online]. Available: http://arxiv.org/abs/1706.07351.
A. R. Rao and M. Reimherr, “Non-linear Functional Modeling using Neural Networks,” pp. 1–13, 2019.
L. Naji, M. Tawfiq, and O. M. Salih, “Design Suitable Feed Forward Neural Network To Solve Troesch’S Problem,” Sci.Int.(Lahore), vol. 31, no. 1, pp. 41–48, 2019.
A. M. Hemeida et al., “Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research,” Ain Shams Eng. J., vol. 11, no. 3, pp. 659–675, 2020, doi: 10.1016/j.asej.2020.01.007.
D. Parikh and V. Menon, “Machine Learning Applied to Cervical Cancer Data,” Int. J. Math. Sci. Comput., vol. 5, no. 1, pp. 53–64, 2019, doi: 10.5815/ijmsc.2019.01.05.
C. J. Tseng, C. J. Lu, C. C. Chang, and G. Den Chen, “Application of machine learning to predict the recurrence-proneness for cervical cancer,” Neural Comput. Appl., vol. 24, no. 6, pp. 1311–1316, 2014, doi: 10.1007/s00521-013-1359-1.
S. K. Suman and N. Hooda, “Predicting risk of Cervical Cancer : A case study of machine learning,” J. Stat. Manag. Syst., vol. 22, no. 4, pp. 689–696, 2019, doi: 10.1080/09720510.2019.1611227.
C. Gu, C. W. H. Chan, S. Twinn, and K. C. Choi, “The influence of knowledge and perception of the risk of cervical cancer on screening behavior in mainland Chinese women,” Psychooncology., vol. 21, no. 12, pp. 1299–1308, 2012, doi: 10.1002/pon.2037.
R. S. D. Wijaya, Adiwijaya, Andriyan B Suksmono, and Tati LR Mengko, “Segmentasi Citra Kanker Serviks Menggunakan Markov Random Field dan Algoritma K-Means,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 139–147, 2021, doi: 10.29207/resti.v5i1.2816.
" global cancer Observatori " oganizatio https://www.who.int/cancer/prevention/diagnosis/cervical-cancer/en/ (accessed Jul. 22, 2022).
E. Surbakti, “Determinan Deteksi Dini Kanker Serviks Pada Wanita Usia Subur,” J. Ilm. PANNMED (Pharmacist, Anal. Nurse, Nutr. Midwivery, Environ. Dent., vol. 15, no. 2, pp. 153–160, 2020, doi: 10.36911/pannmed.v15i2.671.
F. Fuadah, S. Rejeki, H. Triana, and ..., “Deteksi Dini Kanker Serviks Melalui Pemeriksaan IVA Test Pada Wanita Usia Subur Di Desa Babakan Kecamatan Ciparay Kab Bandung,” … Kpd. Masy. …, pp. 4–5, 2020, [Online]. Available: http://journal.unjani.ac.id/index.php/unex/article/view/30.
O. K. Bhatti, A. O. Öztürk, R. Maham, and W. Farooq, “Examining Islamic piety at workplace via an artificial neural network,” Cogent Psychol., vol. 8, no. 1, 2021, doi: 10.1080/23311908.2021.1907038.
I. M. Fitriani, D. E. Ratnawati, and S. Anam, “Klasifikasi Senyawa Kimia dengan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Extreme Learning Machine (ELM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 5, pp. 4516–4524, 2019, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5275.
DOI: 10.24269/jkt.v6i2.1265
Refbacks
- There are currently no refbacks.
