Implementasi Algoritma FP-Growth Untuk Strategi Pemasaran Ritel Hidroponik (Studi Kasus : PT. HAB)

Adi Nugroho Susanto Putro, Richardus Indra Gunawan

Abstract


Abstract.

PT. Hidroponik Agrofarm Bandungan (HAB) is one of the company working in cultivating hydroponic fruit and vegetable. PT. HAB experiences two main issues selling the crops. First, the crops which has no preservatives will not stay fresh for longer time. Second, the difficulties of determining customer’s habit so that it makes some products are not optimized sold in the market. PT. HAB needs to improve the marketing strategy. The customer’s habit pattern of buying the product can be acquired by using data mining association technique. There are two algorithms in association analysis namely Apriori and FP-Growth. This research proposed FP-Growth algorithm implementation with Weka open source software to measure the retail marketing strategy of hydroponic product. There are two measurement used in the association analysis. They are support and confidence. Support is used to measure the level of domination of product in each transaction and Confidence is used to measure the level of confidence of the product that are sold altogether with other products. This research used minimum support 0,05 and minimum confidence 0,9 ,resulting in 21 rules that can be used as marketing strategy for PT. HAB.
Keywords: FP-Growth Algorithm, Data Mining, Hydroponic Retail.


Abstrak.

PT. Hidroponik Agrofarm Bandungan (HAB) perusahaan yang bergerak di bidang budidaya sayur dan buah hidroponik. PT. HAB mengalami permasalahan dalam menjual hasil panen. Permasalahan pertama, produk PT. HAB adalah sayur dan buah segar tanpa pengawet yang tidak dapat bertahan lama. Permasalahan kedua, perilaku pelanggan sulit ditebak di pasar sehingga barang tidak terjual maksimal. PT. HAB memerlukan strategi pemasaran yang tepat. Kebiasaan belanja pelanggan dapat dicari polanya menggunakan teknik data mining asosiasi. Ada dua algoritma dalam analisis asosiasi, yaitu Apriori dan FP-Growth. Penelitian ini mengusulkan implementasi algoritma FP-Growth dengan software open source Weka untuk strategi pemasaran ritel hidroponik. Ada dua ukuran yang digunakan dalam analisis asosiasi, yaitu support dan confidence. Support untuk mengetahui tingkat dominasi suatu barang dalam sebuah transaksi, confidence untuk mengetahui tingkat kepercayaan suatu barang yang terjual bersama-sama. Penelitian ini menggunakan minimum suport 0,05 dan minimum confidence 0,9, menghasilkan 21 rule yang dapat digunakan sebagai strategi pemasaran PT. HAB.
Kata Kunci: Algoritma FP-Growth, Data Mining, Ritel Hidroponik.


Full Text:

PDF

References


M. Arbi, “Kajian Sebaran Produksi Dan Perdagangan Serta Karakteristik Konsumen Sayuran Hidroponik Di Kota Palembang,” Agriekonomika, vol. 5, no. 1, pp. 54–63, 2016.

H. Jafarkarimi, A. T. H. Sim, and R. Saadatdoost, “A Naive Recommendation Model for Large Databases,” International Journal of Information and Education Technology, vol. 2, no. 3, pp. 216–219, 2012.

A. S. A. Alghamdi, “Efficient Implementation of FP Growth Algorithm-Data Mining on Medical Data,” International Journal of Computer Science and Network Security, vol. 11, no. 12, pp. 7–16, 2011.

W. A. Triyanto, “Association Rule Mining Untuk Penentuan Rekomendasi Promosi Produk,” Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 5, no. 2, pp. 121–126, 2014.

R. Fitria, W. Nengsih, and D. H. Qudsi, "Implementasi Algoritma Fp-growth dalam Penentuan Pola Hubungan Kecelakaan Lalu Lintas," Jurnal Sistem Informasi, vol. 13, no. 2, pp. 118–124, 2017.

A. G. B. Ariana and I. M. D. P. Asana, “Analisis Keranjang Belanja Dengan Algoritma Apriori Pada Perusahaan Retail,” in Seminar Nasional Sistem Informasi Indonesia, pp. 2–4, 2013.

H. Setiawan, Kiat Sukses Budidaya Cabai Hidroponik. Yogyakarta: Bio Genesis, 2017.

K. W. Lin and Y.C. Lo, “Efficient algorithms for frequent pattern mining in many-task computing environments,” Knowledge-Based Systems, vol. 49, pp. 10–21, 2013.

J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” ACM sigmod record, vol. 29, no. 2, ACM, 2000.

H. Sulastri and A. I. Gufroni, “Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 3, no. 2, pp. 299–305, 2017.

X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda,… and Z. H. Zhou, “Top 10 algorithms in data mining,” Knowledge and information systems, vol. 14, no. 1, pp. 1–37, 2008.




DOI: https://doi.org/10.24002/jbi.v10i1.1746

Refbacks

  • There are currently no refbacks.