Analisa Data Mining Menggunakan Frequent Pattern Growth pada Data Transaksi Penjualan PT Mora Telematika Indonesia untuk Rekomendasi Strategi Pemasaran Produk Internet
DOI:
https://doi.org/10.30865/mib.v4i4.2300Keywords:
Data Mining, Association Rule, FP-Growth, Tree Pattern, FP TreeAbstract
Utilizing a lot of stored sales transaction data can provide useful knowledge in making policy and business strategy for PT Mora Telematika Indonesia. To realize the things can be applied with the Market Basket Analysis. Association Rule is a data mining technique which is a procedure in the Market Basket Analysis to find the knowledge of consumer purchase patterns. This pattern can be an input in making business policies and strategies. A pattern is determined by two parameters, which are support (supporting value) and confidence (value of certainty). In this study, the Market Basket Analysis used a Frequent Pattern Growth (FP-Growth) algorithm to find patterns by implementing TREE data structures or called FP-Tree. One of the patterns resulting from the analysis of data on sales transactions in the period of January 2018 to April 2018 is 7 Association rules with the highest lift ratio value is if there is an installation of OxygenHome 25-Super Double Then there will be installation OxygenHome 15-Super Double with elevator ratio 4.59%, support value of 3,125%, and confidence value 0.67%.
References
Astrina, I., Arifin, M. Z., & Pujianto, U. (2019). Penerapan Algoritma FP-Growth dalam Penentuan Pola Pembelian Konsumen pada Kain Tenun Medali Mas. Matrix : Jurnal Manajemen Teknologi dan Informatika, 9(1), 32. https://doi.org/10.31940/matrix.v9i1.1036
Fitria, R., Nengsih, W., & Qudsi, D. H. (2017). Implementasi Algoritma FP-Growth Dalam Penentuan Pola Hubungan Kecelakaan Lalu Lintas. Jurnal Sistem Informasi, 13(2), 118. https://doi.org/10.21609/jsi.v13i2.551
Gunadi, G., & Sensuse, D. I. (2012). Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth ) : Telematika, 4(1), 118–132.
Herasmus, H. (2017). Analisa Customer Service System Menggunakan Metode Data Mining Dengan Algoritma Fp-Growth (Studi Kasus di PT Batamindo Investment Cakrawala). Jurnal Teknik Ibnu Sina (JT-IBSI), 2(2), 31–43. https://doi.org/10.36352/jt-ibsi.v2i2.57.
Ikhwan, A., Nofriansyah, D., & Sriani. (2015). Penerapan Data Mining dengan Algoritma Fp-Growth untuk Mendukung Strategi Promosi Pendidikan (Studi Kasus Kampus STMIK Triguna Dharma). Saintikom, 14(3), 211–226.
Larasati, D. P., Nasrun, M., & Ahmad, U. A. (2017). Analisis dan Implementasi Algoritma Fp-Growth Pada Aplikasi Smart Untuk Menentukan Market Basket Analysis Pada Usaha Retail (Studi Kasus Pt X). E-Proceeding of Engineering, 2(1), 749–755.
Larose, Daniel T. (2005). Discover Knowledge Data: An Introduction to Data Mining. John Wiiley & Sons, Inc.
Maulana, A., & Fajrin, A. A. (2018). Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma Fp-Growth Pada Data Transaksi Penjualan Spare Part Motor. Klik - Kumpulan Jurnal Ilmu Komputer, 5(1), 27. https://doi.org/10.20527/klik.v5i1.100.
Priyana, F. A., & Kardianawati, A. (2015). Data Mining Asosiasi Untuk Menentukan Cross-Selling Produk Menggunakan Algoritma Frequent Patern-Growth Pada Koperasi Karyawan PT. Phapros Semarang Sistem informasi yang terkomputerisasi organisasi dalam mengumpulkan berbagai data dalam suatu basis data. Ilmu Komputer, 1–7.
Soepomo, P. (2014). Penggunaan Algoritma FP-Growth Untuk Menemukan Aturan Asosiasi Pada Data Transaksi Penjualan Obat di Apotek (Studi Kasus : Apotek UAD). Jurnal Sarjana Teknik Informatika, 2(3), 130–139. https://doi.org/10.12928/jstie.v2i3.2883.
Turban, E. (2005). Decision Support Systems and Intelligent Systems Edisi Bahasa Indonesia Jilid 1. Andi: Yogyakarta.
Tjhandra, J. E., & Widiastiwi, Y. (2019). Implementasi Algoritma FP-GROWTH Untuk Menentukan Frequent Item Set Pada Peyediaan Sparepart (Studi Kasus: Bengkel Resmi Yamaha Anugerah Motor). 24–25.
Vardaro, M. J., Systems, H. I. T., AG, H. T., Jari, A., Pentti, M., Information, B. G., … Measurements, C. (2016). No Titleبیبیب. ثبثبثب, 2002(1), 35–40. https://doi.org/10.1109/ciced.2018.8592188.
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