Implementasi Metode Asosiasi Transaksi Penjualan Menggunakan Algoritma Apriori pada Studi Kasus Toko Sembako Ibu Siti
DOI:
https://doi.org/10.35870/jtik.v10i2.5245Keywords:
Transaction Association, Apriori Algorithm, Sales, Grocery Store, Association RulesAbstract
This research aims to implement the transaction association method using the Apriori Algorithm in the case study of Ibu Siti's Grocery Store. In a retail environment, understanding customer purchasing patterns is crucial for effective marketing strategies and product arrangement. The Apriori Algorithm was chosen for its capability to discover association rules from large transaction datasets, which will yield valuable information regarding relationships between sales items. The implementation process includes data pre-processing, candidate itemset generation, and the calculation of support, confidence, and lift to extract significant association rules. The results of this research are expected to provide strategic recommendations for Ibu Siti's Grocery Store in arranging product layouts, planning package promotions, and managing product inventory more efficiently. Thus, the application of this algorithm is expected to increase the store's profit and competitiveness.
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Akhtar, M. F., et al. (2025). Implementation and performance evaluation of machine learning-based Apriori algorithm to detect non-technical losses in distribution systems. IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3541722.
Alwendi, Mandopa, A. S., & Hasibuan, E. A. (2023). Aplikasi data mining untuk menentukan masa studi mahasiswa menggunakan metode association rule. Jurnal Pendidikan Dewantara, 2(1). https://doi.org/10.58222/dewantara.v2i1.24
Andika, A. M., Suarna, N., & Dana, R. D. (2023). Analisa dataset asosiasi penjualan menggunakan metode FP-Growth. Jurnal Teknologi Ilmu Komputer, 2(1). https://doi.org/10.56854/jtik.v2i1.108.
Asana, I. M. D. P., et al. (2022). Aplikasi data mining asosiasi barang menggunakan algoritma Apriori-TID. Informatics Journal, 7(1).
Baetulloh, U., Gufroni, A. I., & Rianto. (2019). Penerapan metode association rule mining pada data transaksi penjualan produk kartu perdana kuota internet menggunakan algoritma Apriori. Jurnal SIMETRIS, 10(1).
Barkah, N., Sutinah, E., & Agustina, N. (2020). Metode asosiasi data mining untuk analisa persediaan fiber optik menggunakan algoritma Apriori. Jurnal Kajian Ilmiah, 20(3).
Erwansyah, K., Andika, B., & Gunawan, R. (2021). Implementasi data mining menggunakan asosiasi dengan algoritma Apriori untuk mendapatkan pola rekomendasi belanja produk pada Toko Avis Mobile. J-SISKO TECH, 4(1).
Febrivani, E., Saifullah, & Winanjaya, R. (2021). Penerapan data mining asosiasi pada persediaan obat. JIKOMSI, 4(1).
Lewis, A., Zarlis, M., & Situmorang, Z. (2021). Penerapan data mining menggunakan task market basket analysis pada transaksi penjualan barang di AB Mart dengan algoritma Apriori. Jurnal Media Informatika Budidarma, 5(2), 203–210. https://doi.org/10.30865/mib.v5i2.2934.
Liandi, Z. (2020). Research on e-commerce potential client mining applied to Apriori association rule algorithm. In Proceedings of ICITBS. https://doi.org/10.1109/ICITBS49701.2020.00146.
Maulidiya, H., & Jananto, A. (2020). Asosiasi data mining menggunakan algoritma Apriori dan FP-Growth sebagai dasar pertimbangan penentuan paket sembako. Prosiding SENDIU.
Muchlis, M. M., Fitri, I., & Nuraini, R. (2021). Rancang bangun aplikasi data mining pada penjualan Distro Bloods berbasis web menggunakan algoritma Apriori. Jurnal JTIK, 5(1). https://doi.org/10.35870/jtik.v5i1.197.
Mushleh, M., & Testiana, G. (2023). Studi kasus asosiasi pembelian produk teknologi pada toko elektronik dengan metode Apriori. JDMIS, 1(2). https://doi.org/10.54259/jdmis.v1i2.1718.
Nst, A. H., Munthe, I. R., & Juledi, A. P. (2020). Implementasi data mining algoritma Apriori untuk meningkatkan penjualan. JTIUST, 6(1).
Parinduri, R. D., Defit, S., & Nurcahyo, G. W. (2024). Implementasi algoritma Apriori dalam data mining untuk optimalisasi stok obat di apotik. KomtekInfo, 11(3). https://doi.org/10.35134/komtekinfo.v11i3.544.
Riszky, A. R., & Sadikin, M. (2019). Data mining menggunakan algoritma Apriori untuk rekomendasi produk bagi pelanggan. Jurnal Teknologi dan Sistem Komputer, 7(3). https://doi.org/10.14710/jtsiskom.7.3.2019.103-108.
Sahara, W., Saragih, S. D., & Windarto, A. P. (2022). Teknik asosiasi data mining dalam menentukan pola penjualan dengan metode Apriori. Terapan Informatika Nusantara, 2(12). https://doi.org/10.47065/tin.v2i12.1577.
Siddik, R., Juledi, A. P., & Sihombing, V. (2024). Memanfaatkan algoritma Apriori: Aplikasi berbasis web untuk penambangan aturan asosiasi. JIKOMSI, 7(1).
Sinaga, S., & Husein, A. M. (2019). Penerapan algoritma Apriori dalam data mining untuk memprediksi pola pengunjung pada objek wisata Kabupaten Karo. Jurnal Penelitian Teknik Informatika, 2(2).
Takdirillah, R. (2020). Penerapan data mining menggunakan algoritma Apriori terhadap data transaksi penjualan bisnis ritel sebagai pendukung informasi strategi penjualan. Edumatic, 4(1).
Triayudi, A., & Iskandar, A. (2022). Penerapan data mining dalam penentuan prioritas pemesanan produk berdasarkan data penjualan barang menggunakan algoritma Apriori. JoSYC, 4(1). https://doi.org/10.47065/josyc.v4i1.2523.
Zoelfiandi, F., & Budiyanto, U. (2022). Penerapan data mining menggunakan algoritma Apriori pada Toko Adelia Frozen Food. TICOM: Technology of Information and Communication, 11(1), 13–19.
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