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All Journal Jurnal Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Jurnas Nasional Teknologi dan Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Meza Silvana
Universitas Andalas
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Journal : Jurnal Informatika

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The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
Co-Authors Afdhal Zikri Afrizal Yuhanef Akbar, Ricky Alberta Rahmat Ramadhan Alfi Syahnum Aulia Fikiri Alizar Danton . Derisma, - Dini Hariyati Adam Dwiny Meidelfi Firdaus Firdaus Firdaus, - Hafiz Fajrin Harris Suryamen Ikhwana Elfitri Rahayu Tifani Rahmadi Kurnia Ulfa Nurhasanah Hendri Yulindon