Klasifikasi Penyakit Liver Menggunakan Alogritma Deep Neural Network (DNN)
Keywords:
Klasifikasi, Liver, Deep Neural NetworkAbstract
Abstract
Penyakit hati telah menyerang lebih dari satu juta pasien baru di dunia. yang dimana organ hati memiliki fungsi penting untuk metabolisme tubuh dalam menyalurkan beberapa fungsi vital. Penyakit hati memiliki gejala diantarnya sakit kuning, sakit perut, lelah, mual, muntah, sakit punggung, perut bengkak,penurunan berat badan, limpa membesar dan kantong empedu serta memiliki kelainan yang sangat sulit untuk dideteksi, karena hati berkerja seperti biasa meskipun sebagian fungsi hati telah rusak. Diagnosis penyakit hati dapat dilakukan dengan algoritma klasifikasi Deep Neural Network. Klasifikasi dengan Algortima Deep Neural Network dapat menggunakan 3 jenis fungsi aktivasi diantaranya yaitu Min-Max, Sigmoid, dan Softmaz kemudian dapat dilakukan normalisasi dengan penambahan hidden layar pada arsitektur Deep Neural Network. Kemudian selain fungsi aktivasi, dapat dilakukan membuat paramater seperti epoch dan layer yang bervariasi. Epoch yang digunakan sebesar 50, 100, 150, 200 dan 250, sedangkan layer yang di gunakan 1 layer – 5 layer. Hasil dari model klasifikasi yang dilakukan mendapatkan akurasi terbaik sebesar 71% pada 1 layer dengan 100 epoch, sendangkan hasil terendah dengan akurasi sebesar 62% pada pengujian 4 layer dengan 50 epoch. pada dataset Bupa Liver Disorsder
References
REFERENCE/DAFTAR PUSTAKA
Alasadi, S. A., & Bhaya, W. S. (2017). Review of Data Preprocessing Techniques.pdf. In Journal of Engineering and Applie Sciences (Vol. 12, Issue 16, pp. 4102–4107).
Fathi, M., Nemati, M., Mohammadi, S. M., & Abbasi-Kesbi, R. (2020). A MACHINE LEARNING APPROACH BASED ON SVM FOR CLASSIFICATION OF LIVER DISEASES. Biomedical Engineering: Applications, Basis and Communications, 32(03), 2050018. https://doi.org/10.4015/S1016237220500180
Ghosh, S., & Waheed, S. (2017). Analysis of classification algorithms for liver disease diagnosis. Journal of Science Technology and Environment Informatics, 5, 361–370. https://doi.org/10.18801/jstei.050117.38
Haque, Md Rezwanul, Islam, M. M., Iqbal, H., Reza, M. S., & Hasan, M. K. (2018). Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 1–5.
Jyoti, O., Islam, N., & Hasnain, F. M. D. S. (2020). Prediction of Hepatitis Disease Using Effective Deep Neural Network. 2020 IEEE International Conference for Innovation in Technology (INOCON), 1–5.
Karthik, S., Srinivasa Perumal, R., & Chandra Mouli, P. V. S. S. R. (2018). Breast cancer classification using deep neural networks. Knowledge Computing and Its Applications: Knowledge Manipulation and Processing Techniques: Volume 1, 227–241. https://doi.org/10.1007/978-981-10-6680-1_12
Kumar, P., & Thakur, R. S. (2021). Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach. Multimedia Tools and Applications, 80(11), 16515–16535. https://doi.org/10.1007/s11042-019-07978-3
Kumar, S. K. (2017). On weight initialization in deep neural networks. ArXiv, 9. https://arxiv.org/abs/1704.08863
Kumar, S., & Katyal, S. (2018). Effective analysis and diagnosis of liver disorder by data mining. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 1047–1051.
M. Barhoom, A., Abu-Naser, S., Abu-Nasser, B., Khalil, A., Musleh, M., & Alajrami, E. (2019). Predicting Liver Patients using Artificial Neural Network. 1–11.
Patel, P., Nandu, M., & Raut, P. (2018b). Initialization of weights in neural networks. International Journal of Scientific Development and Research (IJSDR), 3(11), 73–79.
Purba, O. H., Sarwoko, E. A., & Wibowo, A. (2020). Classification of liver cancer with microrna data using the deep neural network (DNN) method. Journal of Physics: Conference Series, 1524(1), 012129.
Yao, Z., Li, J., Guan, Z., Ye, Y., & Chen, Y. (2020). Liver disease screening based on densely connected deep neural networks. Neural Networks, 123, 299–304.
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