Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil

Adi Supriyatna, Wida Prima Mustika

Abstract


Warts is a skin health problem that is generally characterized by the appearance of small, rough-textured lumps on the skin surface caused by a virus that is human papilloma virus (HPV). One technique of treatment of wart disease is immunotherapy, this method is a treatment by boosting the immune system to overcome the disease of warts. Naive bayes and Support Vector Machine (SVM) is a method of data mining algorithm used to classify. The aim of this study was to compare the Naive bayes algorithm with Support Vector Machine (SVM) in predicting the success of immunotherapy treatment method in the treatment of wart disease. Tests conducted using the method of Naive bayes and Support Vector Machine (SVM) using the R programming language, then the results are used to do the comparison. The results of this study revealed that the Naive bayes method has superior prediction capability compared to Support Vector Machine (SVM) because Naive bayes can predict all class instances correctly with the accuracy level of 1.

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References


F. Khozeimeh et al., “Intralesional immunotherapy compared to cryotherapy in the treatment of warts,” Int. J. Dermatol., vol. 56, no. 4, pp. 1–5, 2017.

F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, and S. Nahavandi, “An expert system for selecting wart treatment method,” Comput. Biol. Med., vol. 81, no. August 2016, pp. 167–175, 2017.

H. Amalia and E. Evicienna, “Komparasi metode Data Mining Untuk Penentuan Proses Persalinan Ibu Melahirkan,” J. Sist. Inf. (Journal Inf. Syst., vol. 13, pp. 103–109, 2017.

N. Saputra, T. B. Adji, and A. E. Permanasari, “Analisis Sentimen Data Presiden Jokowi dengan Preprocessing Normalisasi dan Stemming menggunakan Metode Naive bayes dan SVM,” J. Din. Inform., vol. 5, no. 1, 2015.

F. A. Novianti and S. W. Purnami, “Analisis Diagnosis Pasien Kanker Payudara Menggunakan Regresi Logistik dan Support Vector Machine (SVM) Berdasarkan Hasil Mamografi,” J. Sains dan Seni ITS, vol. 1, no. 1, pp. D147--D152, 2012.

M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive bayes Classifier,” Eeccis, vol. 7, no. 1, pp. 59–64, 2013.

K. Hastuti, “Analisis komparasi algoritma klasifikasi data mining untuk prediksi mahasiswa non aktif,” Semin. Nas. Teknol. Inf. Komun. Terap., vol. Juni, no. Semantik, pp. 241–249, 2012.

M. R. Faisal, Seri Belajar Data Science: Klasifikasi dengan Bahasa Pemrograman R. Banjarmasin: Indonesia .NET Developer Community, 2016.

M. R. Faisal, “Seri Belajar Pemrograman : Pengenalan Bahasa Pemrograman R,” no. April. Indonesia .NET Developer Community, Banjarmasin, p. 147, 2016.

W. Budiharto and R. N. Rachmawati, Pengantar Praktis Pemrograman R Untuk Ilmu Komputer, 1st ed. Jakarta: Halaman Moeka, 2013.

S. Mrinalini, N. S. Abinayalakshmi, and C. Vinoth Kumar, “Wavelet feature based SVM and NAIVE BAYES classification of glaucomatous images using PCA and Gabor filter,” Proc. 10th Int. Conf. Intell. Syst. Control. ISCO 2016, 2016.

G. Karthick, “Comparative Performance Analysis of Naive bayes and SVM classifier for Oral X-ray images,” vol. 17, pp. 6–10.

A. Jananto, “Algoritma Naive bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa,” Teknol. Inf. Din., vol. 18, no. 1, pp. 9–16, 2013.




DOI: http://dx.doi.org/10.30645/j-sakti.v2i2.78

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