IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI PRODUK LARIS Decision Tree C4.5 Produk Laris
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Published:
Sep 6, 2021
Keywords:
Klasifikasi, Produk Laris, Decision tree, C4.5, Data mining
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Abstract
Decision Tree C4.5 algorithm is an algorithm that can be used to make a decision tree. Decision tree (Decision Tree) is one method that is quite easily interpreted by humans. However, this algorithm has never been tested for product classification using private data (stock data and sales of goods at PT Cipta Karya Gorontalo). Therefore this study aims to test the accuracy of C4.5 in classifying best-selling products (private data). As a result of the evaluation of product classification models using Decision Tree C4.5 obtained from this study amounted to 90% and AUC value of 0.709 where this value is included in the Good Classification. It can be used as a data mining classification method Decision Tree C4.5 algorithm is accurate in classifying hot-selling products.
Keywords— Decision Tree, C4.5, Classification, Best-Selling Product
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References
Daftar Pustaka
[1] B. Utami and P. Aliandu, “KLASIFIKASI PENENTUAN TIM UTAMA OLAHRAGA HOCKEY MENGGUNAKAN ALGORITMA C4.pdf,” Proc. Int. Conf. Information, Commun. Technol. Syst., vol. 5, no. 4, pp. 1–5, 2013.
[2] X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008.
[3] J. Eska, “Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C45,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 2, pp. 9–13, 2016.
[4] D. F. Ristianti, “Komparasi Algoritma Klasifikasi pada Data Mining,” vol. 1, no. 1, pp. 148–156, 2019.
[5] Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.
[6] W. D. Septiani, “Dan Naive Bayes Untuk Prediksi Penyakit Hepatitis,” vol. 13, no. 1, pp. 76–84, 2017.
[7] A. H. Nasrullah, “Penerapan Metode C4.5 untuk Klasifikasi Mahasiswa Berpotensi Drop Out,” Ilk. J. Ilm., vol. 10, no. 2, pp. 244–250, 2018, doi: 10.33096/ilkom.v10i2.300.244-250.
[8] I. Carolina and R. Kresna, “Klasifikasi kelahiran prematur menggunakan algoritma c4.5,” Semin. Nas. Teknol., pp. 668–672, 2018.
[9] Hariati, M. Wati, and B. Cahyono, “Penerapan Algoritma C4.5 Decision Tree pada Penentuan Penerima Program Bantuan Pemerintah Daerah Kabupaten Kutai Kartanegara,” Jurti, vol. 2, no. 1, pp. 27–36, 2018.
[10] Y. Rosela, “IMPLEMENTASI KLASIFIKASI DECISION TREE MENGANALISA STATUS PENJUALAN BARANG MENGGUNAKAN C4 . 5 ( Studi Kasus : Pt . Matahari Department Store Medan Mall ),” J. Pelita Inform., vol. 18, no. 1, pp. 143–150, 2019.
[11] D. R. S. P, A. P. Windarto, D. Hartama, and I. S. Damanik, “Penerapan Klasifikasi C4.5 Dalam Meningkatkan Sistem Pembelajaran Mahasiswa,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 593–597, 2019, doi: 10.30865/komik.v3i1.1665.
[12] D. R. Ente, S. A. Thamrin, S. Arifin, H. Kuswanto, and A. Andreza, “Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus Di Rumah Sakit Unhas Menggunakan Algoritma C4.5,” Indones. J. Stat. Its Appl., vol. 4, no. 1, pp. 80–88, 2020, doi: 10.29244/ijsa.v4i1.330.
[13] L. N. Rani, “Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit,” INOVTEK Polbeng - Seri Inform., vol. 1, no. 2, p. 126, 2016, doi: 10.35314/isi.v1i2.131.
[14] D. Istiawan and L. Khikmah, “Implementation of C4.5 Algorithm for Critical Land Prediction in Agricultural Cultivation Areas in Pemali Jratun Watershed,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 67, 2019, doi: 10.24014/ijaidm.v2i2.7569.
[15] P. Assiroj, “Kajian Perbandingan Teknik Klasifikasi Algoritma C4 . 5 , Naïve Bayes Dan Cart Untuk Prediksi Kelulusan Mahasiswa ( Studi Kasus : Stmik Rosma Karawang ) ( Studi Kasus : Stmik Rosma Karawang ),” Media Inform., vol. 15, no. 2, pp. 1–17, 2018, doi: 10.5281/zenodo.1184054.
[16] E. Fitriani, R. Aryanti, A. Saepudin, and D. Ardiansyah, “Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing,” Paradig. - J. Komput. dan Inform., vol. 22, no. 1, pp. 72–78, 2020, doi: 10.31294/p.v22i1.6898.
[1] B. Utami and P. Aliandu, “KLASIFIKASI PENENTUAN TIM UTAMA OLAHRAGA HOCKEY MENGGUNAKAN ALGORITMA C4.pdf,” Proc. Int. Conf. Information, Commun. Technol. Syst., vol. 5, no. 4, pp. 1–5, 2013.
[2] X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008.
[3] J. Eska, “Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C45,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 2, pp. 9–13, 2016.
[4] D. F. Ristianti, “Komparasi Algoritma Klasifikasi pada Data Mining,” vol. 1, no. 1, pp. 148–156, 2019.
[5] Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.
[6] W. D. Septiani, “Dan Naive Bayes Untuk Prediksi Penyakit Hepatitis,” vol. 13, no. 1, pp. 76–84, 2017.
[7] A. H. Nasrullah, “Penerapan Metode C4.5 untuk Klasifikasi Mahasiswa Berpotensi Drop Out,” Ilk. J. Ilm., vol. 10, no. 2, pp. 244–250, 2018, doi: 10.33096/ilkom.v10i2.300.244-250.
[8] I. Carolina and R. Kresna, “Klasifikasi kelahiran prematur menggunakan algoritma c4.5,” Semin. Nas. Teknol., pp. 668–672, 2018.
[9] Hariati, M. Wati, and B. Cahyono, “Penerapan Algoritma C4.5 Decision Tree pada Penentuan Penerima Program Bantuan Pemerintah Daerah Kabupaten Kutai Kartanegara,” Jurti, vol. 2, no. 1, pp. 27–36, 2018.
[10] Y. Rosela, “IMPLEMENTASI KLASIFIKASI DECISION TREE MENGANALISA STATUS PENJUALAN BARANG MENGGUNAKAN C4 . 5 ( Studi Kasus : Pt . Matahari Department Store Medan Mall ),” J. Pelita Inform., vol. 18, no. 1, pp. 143–150, 2019.
[11] D. R. S. P, A. P. Windarto, D. Hartama, and I. S. Damanik, “Penerapan Klasifikasi C4.5 Dalam Meningkatkan Sistem Pembelajaran Mahasiswa,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 593–597, 2019, doi: 10.30865/komik.v3i1.1665.
[12] D. R. Ente, S. A. Thamrin, S. Arifin, H. Kuswanto, and A. Andreza, “Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus Di Rumah Sakit Unhas Menggunakan Algoritma C4.5,” Indones. J. Stat. Its Appl., vol. 4, no. 1, pp. 80–88, 2020, doi: 10.29244/ijsa.v4i1.330.
[13] L. N. Rani, “Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit,” INOVTEK Polbeng - Seri Inform., vol. 1, no. 2, p. 126, 2016, doi: 10.35314/isi.v1i2.131.
[14] D. Istiawan and L. Khikmah, “Implementation of C4.5 Algorithm for Critical Land Prediction in Agricultural Cultivation Areas in Pemali Jratun Watershed,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 67, 2019, doi: 10.24014/ijaidm.v2i2.7569.
[15] P. Assiroj, “Kajian Perbandingan Teknik Klasifikasi Algoritma C4 . 5 , Naïve Bayes Dan Cart Untuk Prediksi Kelulusan Mahasiswa ( Studi Kasus : Stmik Rosma Karawang ) ( Studi Kasus : Stmik Rosma Karawang ),” Media Inform., vol. 15, no. 2, pp. 1–17, 2018, doi: 10.5281/zenodo.1184054.
[16] E. Fitriani, R. Aryanti, A. Saepudin, and D. Ardiansyah, “Penerapan Algoritma C4.5 Untuk Klasifikasi Penempatan Tenaga Marketing,” Paradig. - J. Komput. dan Inform., vol. 22, no. 1, pp. 72–78, 2020, doi: 10.31294/p.v22i1.6898.