IMPLEMENTASI DATA MINING DENGAN METODE POHON KEPUTUSAN ALGORITMA ID3 UNTUK MEMPREDIKSI PENJUALAN  PADA CV. MITRA BAJA CEMERLANG

Winda Nia Purba; Demak Situmorang; Yulia Alfani; Delima Hutabarat; Fransiskus William Anggiono

doi:10.37600/tekinkom.v2i1.96

Winda Nia Purba Universitas Prima Indonesia
Demak Situmorang
Yulia Alfani
Delima Hutabarat
Fransiskus William Anggiono

DOI: https://doi.org/10.37600/tekinkom.v2i1.96

Abstract

This study aims to determine the inventory control carried out by CV. Pematangsiantar Shining Steel Partners. The data used in this study are secondary data sourced from company records for the last 3 months. The data analysis method used in this study is the Decision Mining Data Tree method with ID3 algorithm. Through the application of data mining CV. Cemerlang Steel Partners will know more about the items that are of interest to consumers and items that are not in demand, so there will be no more stockpiles that accumulate in the warehouse. The results of this study found that the Root Node in determining which items are in demand and not in demand is the Size attribute. Rule obtained as many as 14 rules, which consists of 7 rules that are worth yes and 7 rules that have no value.

References

[1] A. Rufaidah and A. Fatakh, “Analisis Pengendalian Persediaan Bahan Baku Dengan Menggunakan Metode Economic Order Quantity (EOQ) Di PT. X,” KAIZEN Manag. Syst. Ind. Eng. J., vol. 1, no. 2, pp. 40–45, 2018.
[2] N. Apriyani and A. Muhsin, “Analisis Pengendalian Persediaan Bahan Baku Dengan Metode Economic Order Quantity Dan Kanban Pada PT. Adyawinsa Stamping Industries,” J. Optimasi Sist. Ind., vol. 10, no. 2, pp. 128–142, 2017.
[3] D. R. Indah, L. Purwasih, and Z. Maulida, “Pengendalian Persediaan Bahan Baku Pada PT. Aceh Rubber Industries Kabupaten Aceh Tamiang,” J. Manaj. dan Keuang., vol. 7, no. 2, pp. 157–173, 2018.
[4] D. K. Widiyati, M. Wati, and H. S. Pakpahan, “Penerapan Algoritma ID3 Decision Tree pada Penentuan Penerimaan Program Bantuan Pemerintah Daerah di Kabupaten Kutai Kartenegara,” JURTI, vol. 2, no. 2, pp. 125–134, 2018.
[5] M. S. Efendi and H. A. Wibawa, “Prediksi Penyakit Diabetes Menggunakan Algoritma ID3 dengan Pemilihan Atribut Terbaik (Diabetes Prediction using ID3 Algorithm with Best Attribute Selection),” Juita, vol. VI, no. 1, pp. 29–35, 2018.
[6] I. R. Munthe and V. Sihombing, “Klasifikasi Algoritma Iterative Dichotomizer ( ID3 ) untuk Tingkat kepuasan pada Sarana Laboratorium Komputer,” Jutikomp, vol. 1, no. 2, pp. 27–34, 2018.
[7] Wahyudin, “Metode Iterative Dichotomizer 3 ( ID3 ) Untuk Penyeleksian Penerimaan Mahasiswa Baru,” J. Pendidik. Teknol. Inf. dan Komun., vol. 2, no. 2, pp. 5–15, 2009.
[8] W. W. Ariestya, Y. E. Praptiningsih, and W. Supriatin, “Decision Tree Learning Untuk Penentuan Jalur Kelulusan Mahasiswa,” J. Ilm. FIFO, vol. 8, no. 1, pp. 14–21, 2016.
[9] T. D. Utama, S. W. Sihwi, and A. Doewes, “IMPLEMENTASI ALGORITMA ITERATIVE DICHOTOMISER 3 PADA PENYELEKSIAN PROGRAM MAHASISWA WIRAUSAHA UNS,” J. ITSMART, vol. 3, no. 2, pp. 74–82, 2014.