Analisa Performa Algoritma Machine Learning Dalam Prediksi Penyakit Liver

Abstract

Currently in the world of medicine, determining liver inflammation is something that is not easy to do. But there are medical records that have kept the patient's symptoms and diagnosis of liver inflammation. The weaknesses of the manual method encourage researchers to develop a method that does not depend 100% on humans. The developed method utilizes a computer as a tool to analyze data. This kind of thing is certainly very useful for health experts. They can use existing medical records as an aid in making decisions about the diagnosis of a patient's disease. In this study, we analyzed the performance of machine learning algorithms by comparing the support vector machine, naïve Bayes and k-nearest neighbor algorithms. This study aims to determine the performance of which algorithm has the highest accuracy in liver disease data. From the research results using splinting data 80:20 it can be concluded that the Naïve Bayes algorithm model has better performance than other algorithm models when using the SMOTE technique with an accuracy value of 65.51%, whereas when not using the SMOTE technique the Support Vector Machine algorithm has the highest performance. better than other algorithm models with an accuracy value on the data not 72.41%.

Implementation of ADABOOST Algorithm on C50 for Improving the Performance of Liver Disease Classification

“…It demonstrated that the C4.5 AdaBoost method outperformed other algorithms in terms of accuracy, achieving a rate of 77.12% [6]. Additionally, in a study analyzing the performance of machine learning algorithms in liver disease prediction, the C4.5 algorithm achieved an accuracy of 70.29%, while the Naïve Bayes algorithm achieved an accuracy of 67.05% [7]. Moreover, a study on heart disease classification examined the effectiveness of the C4.5 algorithm with feature selection and subsequent application of the AdaBoost ensemble, which improved the accuracy by 10.33% in diagnosing heart disease [8].…”

Sistem Bantu Penentuan Konsentrasi Mahasiswa Menggunakan Metode K-Nearest Neighbor Classification

“…Data yang belum melalui proses preprocessing data input Menurut Nurkholifah, Jasmarizal, Yusron Umar dan Rahmaddeni preprocessing didefinisikan sebagai berikut Prepocessing adalah tahap awal sebelum melakukan pengujian algoritma, dimana data yang yang digunakan diolah menjadi data bersih siap uji. Prepocessing bertujuan menghilangkan noice dan menyeragamkan bentuk data yang sesuai dengan kebutuhan model algoritma [7].…”

Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru

“…Splitting Data Setelah tahap preprocessing, langkah selanjutnya adalah membagi data menjadi data latih dan data uji. Dengan pemisahan data 60:40, 70:30, 80:20, Data yang belum pernah digunakan dalam suatu penelitian, tetapi juga berguna untuk mengevaluasi keberhasilan atau kegagalan suatu penelitian, disebut data pengujian, sedangkan data pelatihan adalah data yang digunakan untuk melakukan penelitian [12]…”