Komparasi Metode SVM dan Logistic Regression untuk Klasifikasi Hipotesa Penyakit Kanker Paru Paru Berdasarkan Gejala Awal

Shafara Rahmaeda(1*), Rastri Prathivi(2),

(1) Universitas Semarang, Indonesia
(2) Universitas Semarang, Indonesia
(*) Corresponding Author

Abstract


Lung cancer is the uncontrolled growth of cancer cells in lung tissue that occurs due to various carcinogenic substances. Throughout Indonesia, this disease is still the leading cause of death from cancer. The main risk factors include smoking habits, exposure to cigarette smoke, chest pain. Namely, classification is one way of early detection that can reduce the death rate of lung cancer. Various classification techniques have been proposed in various fields such as machine learning and expert systems. In machine learning, there are two methods used in classification, namely SVM and Logistic Regression. The advantage of SVM is to divide data into hyperplanes so that the data space is divided into two classes. SVM theory begins by collecting data that can be separated by a straight line using a hyperplane, then grouped by class. While Logistic Regression is used to describe the relationship between categorical response variables and covariates. Specifically, there is a direct relationship between the independent variable and the logarithm of the probability of an event occurring. This study aims to compare which is the best using the SVM algorithm and the Logistic Regression Algorithm in the classification of lung cancer. The lung cancer disease dataset has a total of 309 data where the data is separated into two parts, namely 70% training data consisting of 216 data, while 30% test data consists of 93 data. The performance used in predicting the model is Accuracy, Precision, Recall, and F1-Score. From the research conducted, the Accuracy value of the Logistic Regression Algorithm was 97.85%. In this case, the Logistic Regression algorithm has better performance in classifying lung cancer than the SVM algorithm.


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DOI: https://doi.org/10.30645/kesatria.v6i1.562

DOI (PDF): https://doi.org/10.30645/kesatria.v6i1.562.g557

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