Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Penjualan Bisnis Ritel

Authors

DOI:

https://doi.org/10.29408/edumatic.v4i1.2081

Keywords:

Apriori Algorithm, Association Rules, Data Mining, Retail Business, Sales Strategy

Abstract

Retail business in West Java province ranks fourth most. This indicates that there is an increase in retail business competition followed by an increase in information technology implementation. The problem of stacking stocks that could harm retail store entrepreneurs is quite common; to overcome such a precise sales strategy is indispensable. In determining the right sales strategy requires the availability of useful data and information. In order to be more efficient data of sales transaction can be processed by applying data mining technique. The method applied by researchers to design the program is the approach of knowledge discovery in the database, including data analysis to the determination of the a-priori algorithm. The purpose of this research is to obtain information about the relation between products that support the sales strategy of the sales transaction data. The linkage information between the products can later be used to support the decision of the retail business sales strategy by adjusting the product to be packaged, product offerings to the buyer and the placement of the product. The results showed that the priori algorithm can be used to process the data of sales transactions into new information in relation between products based on testing with Orange tools. The associative rules established are tested using the lift ratio, so it is important to know the association rules among the most powerful products.

References

Anggraeni, R. M. (2014). Perbandingan Algoritma Apriori dan Algoritma FP-Growth untuk Perekomendasi Pada Transaksi Peminjaman Buku di Perpustakaan Universitas Dian Nuswantoro. Joins, 1, 1–6. Diambil dari http://eprints.dinus.ac.id/13236/

Djamaludin, I., & Nursikuwagus, A. (2017). Analisis Pola Pembelian Konsumen Pada Transaksi Penjualan Menggunakan Algoritma Apriori. Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 8(2), 671. https://doi.org/10.24176/simet.v8i2.1566

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. Diambil dari https://journal.budiluhur.ac.id/index.php/telematika/article/view/164/158

Harismawan, A. F., Kharisma, A. P., & Afirianto, T. (2018). Analisis Perbandingan Performa Web Service Menggunakan Bahasa Pemrograman Python , PHP , dan Perl pada Client Berbasis Android. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK), 2(January), 237–245. Diambil dari http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/781

Kemenkominfo. (2011). Hasil Survei Penggunaan Teknologi Informasi dan Komunikasi (TIK) di Sektor Bisnis Indonesia 2011. Jakarta: Pusat Data Sarana Informatika Kemenkominfo.

Kusrini, & Emha Taufiq, L. (2009). Algoritma Data Mining (1 ed.; T. A. Prabawati, ed.). Yogyakarta: Andi.

Listriani, D., Setyaningrum, A. H., & Eka, F. (2018). Penerapan Metode Asosiasi Menggunakan Algoritma Apriori Pada Aplikasi Analisa Pola Belanja Konsumen (Studi Kasus Toko Buku Gramedia Bintaro). Jurnal Teknik Informatika, 9(2), 120–127. https://doi.org/10.15408/jti.v9i2.5602

Mckinney, W. (2012). Python for Data Analysis (1 ed.; J. Steele & M. Blanchette, ed.). Sebastopol: O’Reailly Media.

Tscherne, F., Wilke, N., Schachenhofer, B., Roux, K., & Tavlaridis, G. (2016). Orange: Data Mining Toolbox in Python. JMLR, 7(1), 295–300. Diambil dari https://dl.acm.org/doi/10.5555/2567709.2567736

Wicaksana, I. W. S., Ambarwati, L., Baskoro, D. A., & Aprilla, D. C. (2013). Belajar Data Mining dengan RapidMiner (R. Sanjaya, ed.). Jakarta: Dennis.

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Published

2020-06-20

How to Cite

Takdirillah, R. (2020). Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Penjualan Bisnis Ritel. Edumatic: Jurnal Pendidikan Informatika, 4(1), 37–46. https://doi.org/10.29408/edumatic.v4i1.2081