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All Journal Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Abdimas Sangkabira Journal of International Conference Proceedings Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Sipakaraya : Jurnal Pengabdian Masyarakat EBAMUKAI; JURNAL PENGABDIAN ILMU PENGETAHUAN DAN TEKNOLOGI
Caecilia Bintang Girik Allo
Universitas Cenderawasih
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Public Health Social Sciences Other

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Journal : JAMBURA JOURNAL OF PROBABILITY AND STATISTICS

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Comparing Logistic Regression and Support Vector Machine in Breast Cancer Problem Caecilia Bintang Girik Allo; Leonardus Sandy Ade Putra; Nicea Roona Paranoan; Vincentius Abdi Gunawan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.19246

Abstract

There are several methods used for the classification problems. There are many different kinds of fields that can be used. Nowadays, Support Vector Machine (SVM) is a popular classification method that has been proposed by many researchers. Using the same method but different distribution methods for creating training and testing data in the same dataset can yield varying results in terms of prediction accuracy, which is crucial in classification. In this paper, we compare the prediction accuracy between SVM results and Logistic Regression results to determine the better method to  classify the current condition of the patient after undergoing some treatment.  Several treatments are used in this paper, including feature selection, feature extraction, separating the train and testing data using Holdout and K-Fold CV. Stepwise selection is done to reduce the features. Training and testing dataset is obtained using the five stratified and non-stratified holdout and five fold stratified and non-stratified cross validation. The result shows that the best method to classify the cancer dataset is five fold stratified cross validation SVM with radial kernel. The obtained accuracy is 81,816% with variance as much as 0,94%.
Co-Authors Abraham Anou, Kezia Noviani Ayub Sahala Gultom FEBY SERU Gunawan, Vincentius Abdi Gusthvi, Wickly Hamzah, Hardi Hardi Hamzah Haryati, Kristina Jonathan k. Wororomi Khaeriah Dahlan Lolita Tuhumena Nazal, Muhammad Asghar Nicea Roona Paranoan Papriani, Nada Pertiwi Putra, Leonardus Sandy Ade Radian Januari Situmeang Rahmadhan, Richardo Valentino Rahman Rahman Saputro, Agung Dwi Simbiak, Esther Talita Sitti Rosnafi’an Sumardi Steven Y.Y. Mantiri Susilowati, Rachmadani Octalia Sutarman, Tatang Tommi Tommi, Tommi Umbekna, Sara Wahyu Kumala Sari Winda Ade Fitriya B Yulindra Margaretha Numberi