Prediksi Pembelian Barang Pada Distributor Lampu Menggunakan Metode Apriori pada PT. XYZ

Authors

  • Rudolfo Rizki Damanik Universitas Pignatelli Triputra
  • Moyo Hady Poernomo Universitas Pignatelli Triputra

DOI:

https://doi.org/10.54259/jdmis.v1i1.1500

Keywords:

Data Mining, Association Rules, Algoritma Apriori, Apriori Algorithm

Abstract

Apriori is an algorithm that is widely used to determine the pattern of relationships between products that are often bought together in a store. This Apriori algorithm will be suitable to be applied in the field of determining strategy or promotion. PT. XYZ is one of the Lighting Distributor Companies. One of the problems of the company is the imbalance of stock in the warehouse. So, the purpose of this research is to obtain product purchase predictions from suppliers to maintain stock balance with product solds to customers. The object of this research is the application of apriori algorithm. This research data is in the form of sales transaction data at PT. XYZ. Data analysis using RapidMiner Software by determining the valure of support and confidence. The analysis shows that there are 22 Association Rules with a minimum support 20% and a minimum confidence 50%. This information is excpected to help the company in developing a product purchasing strategy from the supplier.

Downloads

Download data is not yet available.

References

M. J. Zaki and W. Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, United State of America: Cambridge University Press, 2014.

R. T. Vulandari, Data Mining Teori dan Aplikasi Rapidminer, Yogyakarta: Penerbit Gava Media, 2017.

J. J. Pangaribuan and M. Lestari, "Perbandingan Metode Moving Average (MA) Dan Neural Network Yang Berbasis Algoritma Backpropagation Dalam Prediksi Harga Saham," Journal Information System Development (ISD), vol. 5, no. 1, pp. 26-34, 2020.

O. P. Barus and N. Surantha, "The Classification of Arrhythmia Using the Method of Extreme Learning Machine," ICIC Express Letters, vol. 14, no. 12, pp. 1147-1154, 2020.

V. Leonie and J. J. Pangaribuan, "Prediksi Video Performance Akun Youtube Buzzfeed Menggunakan Metode Naïve Bayes," Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 5, no. 1, pp. 184-191, 2020.

G. Gunadi and D. I. Sensuse, "Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth (FP-Growth): Studi Kasus Percetakan PT. Gramedia," Jurnal Telematika MKOM M, vol. 4, no. 1, pp. 118-132, 2012.

P. Bhatia, Data Mining and Data Warehousing: Principles and Practical Techniques, United State of America: Cambridge University Press, 2019.

G. Shmuelli, P. C. Bruce, I. Yahav, N. R. Patel and K. C. Lichtendahl Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, United State of America: Wiley, 2017.

E. Pimpler, Data Visualization and Exploration with R: A practical guide to using R, RStudio, and Tidyverse for data visualization, exploration, and data science applications, United State of America: CreateSpace Independent Publishing Platform, 2018.

D. O. Embarak, Data Analysis and Visualization Using Python, United State of America: Apress Berkeley, CA, 2018.

Downloads

Published

2023-02-27

How to Cite

Damanik, R. R., & Poernomo, M. H. (2023). Prediksi Pembelian Barang Pada Distributor Lampu Menggunakan Metode Apriori pada PT. XYZ. JDMIS: Journal of Data Mining and Information Systems, 1(1), 05–19. https://doi.org/10.54259/jdmis.v1i1.1500

Issue

Vol. 1 No. 1 (2023): February 2023

Section

Articles

License

Copyright (c) 2023 Rudolfo Rizki Damanik, Moyo Hady Poernomo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).