Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail

Muhammad Safri Satria Permana; Edy Widodo; Wahyu HadiKristanto

doi:10.47709/cnahpc.v6i3.4239

Authors

Muhammad Safri Satria Permana Universitas Pelita Bangsa
Edy Widodo Universitas Pelita Bangsa
Wahyu HadiKristanto Universitas Pelita Bangsa

DOI:

10.47709/cnahpc.v6i3.4239

Keywords:

Apriori Algorithm, Data Mining, RapidMiner, Retail

Dimension Badge Record

Abstract

Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 105 times

References

Arinal, V., & Melani, M. A. (2023). Penerapan Metode Asosiasi Pada Data Penjualan Transaksi Menggunakan Algoritma Apriori (Studi Kasus Circle’K Apartemen Marabella Jakarta Selatan). Jurnal Sains Dan Teknologi, 5(1), 170–176. https://ejournal.sisfokomtek.org/index.php/saintek/article/view/1366

Ashari, I. A., Wirasto, A., Nugroho Triwibowo, D., & Purwono, P. (2022). Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail. MATRIK?: Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 701–709. https://doi.org/10.30812/matrik.v21i3.1439

Bagus Almahenzar, & Arie Wahyu Wijayanto. (2022). Analisis Intensitas Hujan Provinsi Jawa Barat Tahun 2020 Menggunakan Association Rule Apriori dan FP-Growth. Journal of System and Computer Engineering (JSCE) , 3(2), 258–271.

Elischa Febrivani, Saifullah, & Riki Winanjaya. (2021). Penerapan Data Mining Asosiasi Pada Persediaan Obat. JIKOMSI [Jurnal Ilmu Komputer Dan Sistem Informasi] , 4(1), 25–36.

Elvira Munanda, & Siti Monalisa. (2021). Penerapan Algoritma Fp-Growth Pada Data Transaksi Penjualan Untuk Penentuan Tataletak Barang. Jurnal Ilmiah Rekayasadan Manajemen Sistem Informasi, 7(2), 173–184.

Farah Dewi Ramadani, Bambang Irawan, & Agus Bahtiar. (2024). Analisis Keranjang Pasar Untuk Peningkatan Penjualan Mengunakan Algoritma Apriorii. JATI(Jurnal Mahasiswa Teknik Informatika), 8(6), 2942–2950.

Gumilang, J. R. (2021). Implementasi Algoritma Apriori Untuk Analisis Penjualan Konter Berbasis Web. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 1(2), 226–233. https://doi.org/10.33365/jatika.v1i2.612

Hanani, D., Irawan, B., Bahtiar, A., & Tohidi, E. (2024). Implementasi Algoritma Apriori Untuk Analisis Pola Asosiasi Pada Data Penjualan Umkm Sibucin_Id. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), 3356–3362. https://doi.org/10.36040/jati.v7i6.8196

Harpa Erasmus Simanjuntak, & Windarto. (2020). Analisa Data Mining Menggunakan Frequent Pattern Growth pada Data Transaksi Penjualan PT Mora Telematika Indonesia untuk Rekomendasi Strategi Pemasaran Produk Internet . JURNAL MEDIA INFORMATIKA BUDIDARMA, 4(4), 914–923.

Irnanda, K. F., Hartama, D., & Windarto, A. P. (2021). Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(1), 327. https://doi.org/10.30865/mib.v5i1.2763

Ismiyana Putri, D., & Yudhi Putra, M. (2023). Komparasi Algoritma Dalam Memprediksi Perubahan Harga Saham Goto Menggunakan Rapidminer. Jurnal Khatulistiwa Informatika, 11(1), 14–20. https://doi.org/10.31294/jki.v11i1.16153

Nurislah, B., Dudih Gustian, & Seliwati Ginting. (2024). Penerapan Data Mining Untuk Analisis Pola Pembelian Pelanggan Dengan Menggunakan Algoritma Apriori. Jurnal Rekayasa Teknologi Nusa Putra, 10(1), 44–52. https://doi.org/10.52005/rekayasa.v10i1.427

Rizkiyani, A., & Anwar, N. (2023). Analisis Minat Pelanggan Terhadap Produk Pakaian Dengan Implementasi Algoritma Apriori (Studi Kasus Toko XYZ). G-Tech: Jurnal Teknologi Terapan, 7(3), 1298–1307. https://doi.org/10.33379/gtech.v7i3.2868

Saputra, A., Sari, H. L., & Sartika, D. (2023). Implementasi Metode Association Rule Mining Pada Penjualan Barang Di Toko Bangunan Ada Mas Menggunakan Algoritma Apriori. Jurnal Multidisiplin Dehasen (MUDE), 2(4). https://doi.org/10.37676/mude.v2i4.4805

Winarti, D., Revita, E., & Yandani, E. (2021). Penerapan Data Mining untuk Analisa Tingkat Kriminalitas Dengan Algoritma Association Rule Metode FP-Growth. Simtika, 4(3), 8–22. https://ejournal.undhari.ac.id/index.php/simtika/article/view/553

Zega, M., & Fauzi, R. (2023). Penerapan Data Mining Pada Transaksi Penjualan Menggunakan Algoritma Apriori Di Alfamart Centre Park. Computer and Science Industrial Engineering (COMASIE), 9(5). https://doi.org/10.33884/comasiejournal.v9i5.7783