Implementasi metode extreme learning machine untuk klasifikasi pasien pengidap penyakit lever

Vieri, Daniel Kevin (2021) Implementasi metode extreme learning machine untuk klasifikasi pasien pengidap penyakit lever. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Penyakit lever menjadi salah satu penyakit yang ditakuti karena dapat membunuh tanpa menunjukkan gejala terbukti berdasarkan data dari WHO pada tahun 2018 yang menunjukkan angka penderita penyakit lever yang telah mencapai 1,34 juta jiwa dan angka kematian penyakit lever melebihi kasus kematian yang disebabkan oleh penyakit Human Immunodeficiency Virus (HIV) dan hampir menyamai angka kematian yang disebabkan oleh penyakit tuberkulosis (TBC). Pembelajaran mesin dapat digunakan untuk mengurangi angka penderita penyakit lever dengan mengklasifikasikan pasien yang menderita penyakit lever ataupun tidak dengan menggunakan metode Extreme Learning Machine sehingga penanganan yang paling baik dapat diberikan pada pasien. Penelitian ini dilakukan dengan membagi dataset sebanyak 579 data menjadi 75% data training dan 25% data testing. Berdasarkan hasil pengujian data yang dilakukan, tingkat keakurasian yang didapatkan pada angka 74%, tingkat presisi sebesar 74%, tingkat recall sebesar 98% dan f-1 score sebesar 84%. Pengujian keakurasian metode ELM terhadap jumlah hidden neuron menunjukkan angka paling optimal pada rentan 0 hingga 30./Liver disease is one of several diseases people fear most because it can kills without showing symptoms, shown by evidence from WHO in 2018 which revealed the number of liver disease patients has reached 1,34 million people and the mortality rate for liver disease exceeds the number of deaths caused by Human Immunodeficiency Virus (HIV) and almost equal with the death rate caused by Tuberculosis disease (TBC). Machine learning can be used to reduce the number of liver disease patients by classifying the patient using the Extreme Learning Machine method so that the best treatment can be given to patients. This research was conducted by dividing a dataset of 579 data into 75% training data and 25% testing data. Based on the results of the data testing, the accuracy level is 74%, the precision is 74%, the recall rate is 98% and the f-1 score is 84%. Testing the accuracy of the ELM method on the number of hidden neurons shows the most optimal number in the range of 0 to 30.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Vieri, Daniel KevinNIM03081170022danielkvieri@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorBarus, Okky PutraNIDN0127068803okky.barus@uph.edu
Uncontrolled Keywords: extreme learning machine; machine learning; klasifikasi; penyakit lever
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Subject > Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems
Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems
Depositing User: Users 18435 not found.
Date Deposited: 10 Aug 2021 04:16
Last Modified: 12 Jan 2022 09:10
URI: http://repository.uph.edu/id/eprint/41369

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