PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)

  • Reni Pratiwi Mahasiswa
  • Memi Nor Hayati Universitas Mulawarman
  • Surya Prangga Universitas Mulawarman
DOI: https://doi.org/10.30598/barekengvol14iss2pp273-284
Keywords: C5.0 Algorithm, CART, Classification, Decision Tree

Abstract

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of   family members (X2), last education  (X3)  and  gender  (X4).  After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method

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References

Prasetyo, E, Data Mining-Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta: Penerbit Andi, 2014.

Mardiani, “Perkembangan Algoritma untuk Menghitung Pola yang Sering Muncul pada Basis Data yang Besar,†Seminar Aplikasi Teknologi Informasi, Juni, 2012.

Rosni, “Analisis Tingkat Kesejahteraan Masyarakat Nelayan di Desa Dahari Selebar Kecamatan Talawi Kabupaten Batubara,†Jurnal Geografi, pp. 53-66, 2012.

Wijaya, A. C, Hasibuan, N. A dan Ramadhani, P., “Implementasi Algoritma C5.0 dalam Klasifikasi Pendapatan Masyarakat (Studi Kasus: Kelurahan Mesjid Kecamatan Medan Kota),†Majalah Ilmiah INTI. vol. 13, no. 2, pp. 192-198, Mei 2018.

Pakpahan, H. S, Indar, F dan Wati, M., “Penerapan Algoritma CART Decision Tree pada Penentuan Penerima Program Bantuan Pemerintah Daerah Kabupaten Kutai Kartanegara,†JURTI, vol. 2, no. 1, pp. 27-36, Juni 2018.

Yusuf, Y. W., “Perbandingan Performansi Algoritma Decision Tree C5.0, CART dan CHAID: Kasus Prediksi Status Resiko Kredit Bank X,†Seminar Nasional Aplikasi Teknologi Informasi, pp. 59-62, Juni, 2007.

Kusrini dan Luthfi, E. T., Algoritma Data Mining. Yogyakarta: Penerbit Andi, 2009.

Putri, Y. R., Mukhlash, I. dan Hidayat, N., “Prediksi Pola Kecelakaan Kerja pada Perusahaan Non Ekstraktif Menggunakan Algoritma Decision Tree: C4.5 dan C5.0,†Jurnal Sains dan Seni Pomits, vol. 2, no. 1, pp. 1-6, 2013.

Pratiwi, F. E. dan Zain, I., “Klasifikasi Pengangguran Terbuka Menggunakan CART (Classification and Regression Tree) di Provinsi Sulawesi Utara,†Jurnal Sains dan Seni Pomits, vol. 3, no. 1, pp. 54-59, 2014.

Akbar, M. S., Yuanita, D. dan Harini, S., “Pendekatan CART untuk Mendapatkan Faktor yang Mempengaruhi Terjangkitnya Penyakit Demam Tifoid di Aceh Utara,†Jurnal CAUCHY. vol. 1, no. 2, pp. 71-77, 2010.

Pramana, S, Yuniarto, B, Mariyah, Siti, Santoso, Ibnu dan Nooraeni., Data Mining dengan R. Konsep Serta Implementasi. Bogor: Penerbit IN MEDIA. 2018.

Prasetyo, E. (2012). Data Mining-Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Penerbit Andi.

Pass, Christopher dan Bryan Lowes. (1994). Kamus Lengkap Ekonomi, Edisi Kedua. Jakarta: Erlangga.

Reksoprayitno. (2004). Sistem Ekonomi dan Demokrasi Ekonomi. Jakarta: Bina Grafika.

Boediono. (2002). Pengantar Ekonomi. Jakarta: Erlangga.

Published
2020-06-01
How to Cite
[1]
R. Pratiwi, M. Hayati, and S. Prangga, “PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)”, BAREKENG: J. Math. & App., vol. 14, no. 2, pp. 267-278, Jun. 2020.
Issue
Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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