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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains APPA : Jurnal Pengabdian Kepada Masyarakat Journal on Pustaka Cendekia Informatika
Amir Rozy Sinaga
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Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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Journal : OKTAL : Jurnal Ilmu Komputer dan Sains

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Literature Review: Penggunaan Machine Learning Berbasis SVM untuk Klasifikasi Penyakit Liver Amir Rozy Sinaga; Raihan Rizky Nurfadila; Melanie Putria; Silvi Maerosa
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Liver disease is a significant health problem worldwide, with high mortality rates if not diagnosed early. In this study, we conducted a literature review on the application of Support Vector Machine (SVM) in liver disease classification. The SVM method was chosen because of its ability to handle imbalanced data and high complexity. The purpose of this study was to evaluate the effectiveness of SVM compared to other machine learning methods in detecting liver disease. The results of the literature review showed that SVM provided higher accuracy compared to other methods such as Naïve Bayes and Decision Tree, with some studies achieving accuracy of more than 90%. This study is expected to provide insight into the development of a machine learning-based diagnostic system for liver disease.
Co-Authors Ahmad Ismail Darul Huda Haryono, Wasis Iddodo Melanie Putria muhamad iksan Rahmat Ibnu Iman Raihan Rizky Nurfadila Silvi Maerosa T.M. Aditya Ramadhan Wafa Zabira Revan