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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Pemberdayaan Masyarakat Universitas Al Azhar Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Ade Jamal
Universitas Al Azhar Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara Annisa Handayani; Ade Jamal; Ali Akbar Septiandri
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 4: November 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

According to the World Health Organization (WHO), malignant tumor (cancer) is one of the leading causes of mortality worldwide. Among all types of malignant tumors, malignant breast tumor (breast cancer) is the most common malignant tumors found, especially in women. One of four ways to distinguish benign breast tumor from malignant ones is by doing Fine Needle Aspiration (FNA) test. This method is simple, quick, inexpensive, and can be performed either in outpatients or inpatients. Although FNA is much more preferred than the other methods, the accuracy of FNA varies widely. Therefore, this research is conducted to find the best model to classify malignant breast tumors and benign breast tumors, based on data from FNA test by evaluating value of Area Under the Curve (AUC) of three algorithms, Extreme Gradient Boosting (XGBoost), Support Vector Machine with Radial Basic Function kernel (SVM-RBF), and Multi-layer Perceptron (MLP). The breast cancer data set used in this research is obtained from Wisconsin Breast Cancer (Original) Data set available in the UCI Machine Learning Repository. The experiment shows that SVM with eliminating missing value data set achieved the best result, with AUC value 99.23 and cost $2,740.20.
Co-Authors Ali Akbar Septiandri Ali Akbar Septiandri Ali Akbar Septiandri Annisa Handayani ANNISA HANDAYANI Annisa Utami Annisa Utami Anto Satriyo Nugroho Arie Wahyu Triansyah Arif Supriyanto Bagus Mauludin Budi Wiweko Denny Hermawan Dewi Ananta Hakim Dody Haryadi Fatimah Ilona Asa Latifah Ramadhana Murilmiani Effendhi Muhammad Nugraha Pradani, Winangsari Ripmiatin, Endang Riri Safitri Solechoel Arifin Teguh Widodo Yunus Effendi