Implementasi Algoritma Apriori untuk Analisis Pola Pembelian Konsumen pada Dataset Market Basket Analysis
DOI:
https://doi.org/10.35473/jamastika.v4i2.4521Abstract
Penelitian ini bertujuan untuk mengidentifikasi pola pembelian konsumen pada data transaksi penjualan retail menggunakan metode data mining dengan algoritma Apriori. Analisis dilakukan terhadap 20 data transaksi produk kebutuhan harian yang telah dikonversi ke dalam format biner. Proses pengolahan data dilakukan secara manual menggunakan Microsoft Excel serta secara otomatis dengan RapidMiner. Hasil penelitian menunjukkan bahwa produk yang paling sering dibeli oleh konsumen adalah Chocolate (42,1%), Yogurt (42,0%), Butter (42,0%), Ice Cream (41,0%), dan Sugar (40,9%). Selain itu, diperoleh beberapa aturan asosiasi dengan nilai support dan confidence yang tinggi, seperti kombinasi Milk dan Dill → Chocolate (confidence 60%) dan kombinasi Chocolate, Onion, Unicorn → Dill (confidence 60,6%). Informasi ini dapat dimanfaatkan untuk menyusun strategi penjualan yang lebih efektif, seperti pembuatan paket bundling produk, penempatan produk secara strategis di rak toko, serta pengembangan sistem rekomendasi berbasis pola pembelian konsumen.
Kata Kunci: Data mining, Apriori, Market Basket Analysis, Association Rules, RapidMiner.
This study aims to identify consumer purchasing patterns in retail transaction data using data mining techniques with the Apriori algorithm. The analysis was conducted on 20 daily product transactions that were converted into binary format. Data processing was carried out both manually using Microsoft Excel and automatically with RapidMiner. The results indicate that the most frequently purchased products by consumers include Chocolate (42.1%), Yogurt (42.0%), Butter (42.0%), Ice Cream (41.0%), and Sugar (40.9%). Furthermore, several association rules with high support and confidence values were discovered, such as the combination of Milk and Dill → Chocolate (confidence 60%) and Chocolate, Onion, Unicorn → Dill (confidence 60.6%). These insights can be utilized to design more effective sales strategies, such as bundled product promotions, optimized shelf arrangements, and the development of recommendation systems based on consumer purchasing behavior.
Keywords: Data Mining, Apriori, Market Basket Analysis, Association Rules, RapidMiner
References
Adri, A., Rumlaklak, N.D. and Sina, D.R. (2021) ‘Implementasi Algoritma Apriori Untuk Analisa Data Penjualan (Studi Kasus: Toko Ud. Suryani)’, Jurnal Komputer dan Informatika, 9(2), pp. 182–188. Available at: https://doi.org/10.35508/jicon.v9i2.5132.
Agustin, W. and Muharmi, Y. (2018) ‘Apriori Algorithm through RapidMiner for Age Patterns of Homeless and Beggars’, Indonesian Journal of Artificial Intelligence and Data Mining, 1(2), p. 86. Available at: https://doi.org/10.24014/ijaidm.v1i2.5670.
Aliyul Amri, M. and Warda Ningsih, S. (2021) ‘Penerapan Data Mining Penjualan Alat Tulis Kantor Menggunakan Algoritma Apriori Di Tiga Balata’, 2(3), pp. 138–146.
Arora, D.Y. and Arora, D.Y. (2022) ‘Market Basket Analysis using Apriori Algorithm’, International Journal of Innovative Research in Computer Science & Technology, 4(3), pp. 62–66. Available at: https://doi.org/10.55524/ijircst.2022.10.3.12.
Fitriana Dewi, H., Hanny Hikmayanti Handayani and Jamaludin Indra (2022) ‘Implementasi Algoritma Apriori Terhadap Market Basket Analysis Pada Data Penjualan Retail’, Jurnal Informatika Teknologi dan Sains, 4(4), pp. 432–436. Available at: https://doi.org/10.51401/jinteks.v4i4.2182.
Kaban, E., Darmawiguna, I.G.M. and Kesiman, M.W.A. (2024) ‘Optimizing Customer Purchase Insights: Apriori Algorithm for Effective Product Bundle Recommendations’, Brilliance: Research of Artificial Intelligence, 4(2), pp. 747–756. Available at: https://doi.org/10.47709/brilliance.v4i2.4981.
Omol, E. et al. (2024) ‘Apriori Algorithm and Market Basket Analysis to Uncover Consumer Buying Patterns : Case of a Kenyan Supermarket’, 5(2), pp. 51–63. Available at: https://doi.org/10.36805/bit-cs.v5i2.6082.
Qoniah, I. and Priandika, A.T. (2020) ‘Analisis Market Basket Untuk Menentukan Asossiasi Rule Dengan Algoritma Apriori (Studi Kasus: Tb.Menara)’, Jurnal Teknologi dan Sistem Informasi, 1(2), pp. 26–33. Available at: https://doi.org/10.33365/jtsi.v1i2.368.
Ridla, M.A., Baijuri, A. and Ahmad, U. (2023) ‘Implementasi Data Mining terhadap Pola Penjualan Bahan Material Bangunan di TB. Murah Rejeki Menggunakan Algoritma Apriori’, Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data), 6(2), pp. 92–103. Available at: https://doi.org/10.30873/simada.v6i2.3800.
Rizaldi, D. and Adnan, A. (2021) ‘Market Basket Analysis Menggunakan Algoritma Apriori: Kasus Transaksi 212 Mart Soebrantas Pekanbaru’, Jurnal Statistika dan Aplikasinya, 5(1), pp. 31–40. Available at: https://doi.org/10.21009/jsa.05103.
Santasup, C. and Tengpongsathon, K. (2020) ‘Consumer Purchased Behavior using Data Mining: A Case Study of Coffee Shop Service Business’, pp. 263–267. Available at: https://doi.org/10.5220/0009998202630267.
Santoso, M.H. (2021) ‘Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom’, Brilliance: Research of Artificial Intelligence, 1(2), pp. 54–66. Available at: https://doi.org/10.47709/brilliance.v1i2.1228.
Studi, P. et al. (2017) ‘Implementasi data mining menggunakan algoritma apriori 1,2’, pp. 372–382.
