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All Journal Informatika Mulawarman JIKO (Jurnal Informatika dan Komputer) Jurnal Ilmiah Matrik Jurnal Abdimas Mahakam METIK JURNAL Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Reswara: Jurnal Pengabdian Kepada Masyarakat Teknika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Bangkit Indonesia Jurtik STMIK Bandung
Taghfirul Azhima Yoga Siswa, Taghfirul Azhima Yoga
Universitas Muhammadiyah Kalimantan Timur
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences

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Journal : Jurtik STMIK Bandung

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PERBANDINGAN KINERJA ALGORITMA C4.5, NAÏVE BAYES, K-NEAREST NEIGHBOR, LOGISTIC REGRESSION, DAN SUPPORT VECTOR MACHINES UNTUK MENDETEKSI PENYAKIT KANKER PAYUDARA Taghfirul Azhima Yoga Siswa; Prihandoko Prihandoko
JURTIK:Jurnal Penelitian dan Pengembangan Teknologi Informasi dan Komunikasi Vol 7 No 2 (2018): JURTIK : Jurnal Teknologi Informasi dan Komunikasi
Publisher : LPPM STMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (792.339 KB) | DOI: 10.58761/jurtikstmikbandung.v7i2.105

Abstract

Evaluate the best performance comparison of C4.5, Naïve Bayes, K-Nearest Neighbor, Logistic Regression, and Support Vector Machines classification methods for detecting breast cancer using a 10 fold Cross Validation test by comparing the values of accuracy, precision, and recall using confusion matrix . The breast cancer dataset used was 699 records with 11 indicator parameters consisting of Code Number, Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, Mitoses, and Classes obtained from http://archive.ics.uci.edu. The data was processed using Rapid Miner Version 9 software. The results of this study found that the percentage of performance of each classification algorithm analyzed, that is C4.5 Algorithm (accuracy 93.70%, precision 94.26%, recall 87.86%), Naïve Bayes Algorithm (accuracy 96.19 %, precision 92.25%, recall 97.50%), K-Nearest Neighbor Algorithm (95.61% accuracy, precision 94.99%, recall of 92.43%), Logistic Regression Algorithm (accuracy 96.77%, precision 95.93%, recall 94.98%), and Support Vector Machines algorithm (accuracy 96.78%, precision 94.83%, recall 96.20%). The best performance results tested using T-Test found that the Logistic Regression and Support Vector Machines algorithm has the same highest accuracy value that is equal to 0.968.
Co-Authors Agustya Nanda Pratiwi Anis Nurrohkayati Anton Prafanto Anton Saputra Arbansyah Arbansyah Asnur Karima Darmawan Setiya Budi Gubtha Mahendra Putra Heri Abijono Jubaidi Jubaidi Mardiana Mardiana Muhammad Najeri Al Syahrin Muhammad Norhalimi Muhammad Rhosyid Akhmad Naufal Azmi Verdikha Paula Mariana Kustiawan Pitoyo Pitoyo Prihandoko, Prihandoko Renaldi Panji Wibowo Rizky Aspiah Santi Yatnikasari Wawan Joko Pranoto Yoga Priantama