Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation

 (*)Ritham Tuntun Mail (Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia)
 Kusrini Kusrini (Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia)
 Kusnawi Kusnawi (Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: August 12, 2022; Published: October 25, 2022

Abstract

This study aims to compare the performance of two classification data mining algorithms, namely the K-Nearest Neighbor algorithm, and C4.5 using the K-fold cross validation method. The data used in this study are iris public data with a total of 150 data and 3 label target classes, namely iris-setosa, iris-versicolor, and iris-virginica. The training data used is 97% or 145 data from 150 data, and the testing data used is 3% or 5 data, and the number of K in the K-fold cross validation is 30 or 30 times the experimental stage. The results showed that the performance of the K-Nearest Neighbor algorithm was 95.33%, recall was 95.33%, and precision was 96.27%. While the C4.5 algorithm obtained an accuracy of 96.00%, recall of 94.44%, and precision of 93.52%.

Keywords


K-Nearest Neighbor; C4.5; Cross validation; Classification; Data mining

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References

Kusrini and L. Taufiq Emha, Algoritma Data mining Yogyakarta, no. February. 2009. [Online]. Available: https://books.google.co.id/books?id=-Ojclag73O8C&printsec=frontcover#v=onepage&q&f=false

S. Santoso, Statistik Deskriptif Konsep dan Aplikasi dengan Microsoft excel dan SPSS. 2003.

Y. D. Atma and A. Setyanto, “Perbandingan algoritma c4.5 dan K-NN dalam identifikasi mahasiswa berpotensi drop out,” Metik J. ISSN 2580-1503, vol. 2, no. 2, pp. 31–37, 2018.

D. Suyanto, Data mining Untuk Klasifikasi Dat, no. x. Bandung: Informatika, 2019.

D. J. Hand, Principles of data mining, vol. 30, no. 7. 2007. doi: 10.2165/00002018-200730070-00010.

A. Setianingrum, A. Hindayanti, D. M. Cahya, and D. S. Purnia, “Perbandingan Metode Algoritma K-NN & Metode Algoritma C45 Pada Analisa Kredit Macet (Studi Kasus PT Tungmung Textil Bintan),” EVOLUSI J. Sains dan Manaj., vol. 9, no. 2, pp. 78–92, 2021, doi: 10.31294/evolusi.v9i2.11166.

S. Widaningsih, “Perbandingan Metode Data mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

M. Azhari, Z. Situmorang, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 640, 2021, doi: 10.30865/mib.v5i2.2937.

Ardiyansyah, P. A. Rahayuningsih, and R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data mining Untuk Dataset Blogger Dengan Rapid miner,” J. Khatulistiwa Inform., vol. VI, no. 1, pp. 20–28, 2018.

R. Nofitri and N. Irawati, “Analisis Data Hasil Keuntungan Menggunakan Software Rapidminer,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 5, no. 2, pp. 199–204, 2019, doi: 10.33330/jurteksi.v5i2.365.

F. N. Hasan, N. Hikmah, and D. Y. Utami, “Perbandingan Algoritma C4.5, KNN, dan Naive bayes untuk Penentuan Model Klasifikasi Penanggung jawab BSI Entrepreneur Center,” J. Pilar Nusa Mandiri, vol. 14, no. 2, p. 169, 2018, doi: 10.33480/pilar.v14i2.908.

F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive bayes pada Penentuan Status Gunung Berapi dengan K-fold cross validation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 577, 2018, doi: 10.25126/jtiik.201855983.

t ramadhan, e suswaini, and a uperiati, “Implementasi Algoritma C4. 5 Dalam Klasifikasi Ketepatan Waktu Kelulusan Pada Data Mahasiswa Penerima Beasiswa Bidikmisi (Studi …,” Student Online J. …, vol. 2, no. 2, pp. 1348–1357, 2021, [Online]. Available: https://soj.umrah.ac.id/index.php/sojft/article/view/1014

H. N. Zerlinda, I. Slamet, and E. Zukhronah, “Klasifikasi Calon Penerima Bidikmisi Dengan Menggunakan,” Semin. Nas. Penelit. Pendidik. Mat. 2019 Umt Klasifikasi, pp. 88–93, 2019.

I. Prihandi, “KNN on Iris Data with Python Programming,” vol. 2, no. 7, pp. 6–8, 2019.

F. Gorunescu, Data mining: Concepts, models and techniques, vol. 12. 2011. doi: 10.1007/978-3-642-19721-5.

D. Nofriansyah and G. W. Nurcahyo, Algoritma Data mining Dan Pengujian. Yogyakarta, 2019.

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