ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

  • Sri Rahayu STMIK Nusa Mandiri
  • Fitra Septia Nugraha Ilmu Komputer STMIK Nusa Mandiri
  • Muhammad Ja’far Shidiq Ilmu Komputer STMIK Nusa Mandiri
Keywords: Cryotherapy, Machine Learning, Neural Network

Abstract

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

Downloads

Download data is not yet available.

References

Adil, R., Elektronika, P., & Surabaya, N. (2005). Pembuatan alat bantu pemantau kondisi tubuh dan keberadaan seseorang saat beraktifitas dengan tampilan web. 1–9.

Andini, W. C. (2018). Cryotherapy , Inovasi Baru untuk Menurunkan Berat Badan. Cryotherapy , Inovasi Baru Untuk Menurunkan Berat Badan, 1–7.

Aulia Adam. (2017). Cryotherapy , Senjata Alternatif Cry Melawan Kanker.

Badrul, M. (2013). PREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA DENGAN METODE NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION Pendahuluan. PREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA DENGAN METODE NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION, IX(1), 37–47.

Basarslan, M. S., & Kayaalp, F. (2018). A Hybrid Classification Example in the Diagnosis of Skin Disease with Cryotherapy and Immunotherapy Treatment. 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1–5.

Cüvitoğlu, A., & Işik, Z. (2018). Evaluation Machine-Learning Approaches for Classification of Cryotherapy and Immunotherapy Datasets. 8(4). https://doi.org/10.18178/ijmlc.2018.8.4.707

Eko Susanto, W., & Riana, D. (2016). Komparasi Algoritma. 8(3), 18–27.

Hastuti, K. (2012). Analisis komparasi algoritma klasifikasi data mining untuk prediksi mahasiswa non aktif. 2012(Semantik), 241–249.

Indriani, A., & Nbc, D. (2014). Klasifikasi Data Forum dengan menggunakan Metode Naïve Bayes Classifier. 5–10.

Khozeimeh, F., Alizadehsani, R., Roshanzamir, M., Khosravi, A., Layegh, P., & Nahavandi, S. (2017). An expert system for selecting wart treatment method. Computers in Biology and Medicine, 81(January), 167–175. https://doi.org/10.1016/j.compbiomed.2017.01.001

Rahayu, S., Nugraha, F. S., & Shidiq, M. J. (2019). Analisa tingkat keberhasilan cryoterapy menggunakan neural network. 14(2), 1–7.

Sucipto, A. (2012). CREDIT PREDICTION WITH NEURAL NETWORK ALGORITHM Ir . Adi Sucipto , M . Kom . Sains and Technology Faculty Universitas Islam Nahdlatul Ulama Jepara. (15), 978–979.

Widowati, L., & Mudahar, H. (2009). Ujiaktivitas ekstrak etanol 50% umbi keladi tikus (typhoniumflagelliforme (lood) bi) terhadap sel kanker payudara mcf-7 in vitro. XIX, 3–8.

Published
2019-09-05
How to Cite
Rahayu, S., Nugraha, F., & Shidiq, M. (2019). ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK. Jurnal Pilar Nusa Mandiri, 15(2), 141-148. https://doi.org/10.33480/pilar.v15i2.599