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Peningkatan efisiensi strategi penjualan dan pengelolaan stok produk menjadi kebutuhan utama dalam bisnis retail, termasuk dalam penjualan pakaian seragam sekolah. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian konsumen melalui penerapan metode Market Basket Analysis menggunakan dua algoritma data mining, yaitu Apriori dan Frequent Pattern Growth (FP-Growth). Pendekatan yang digunakan adalah CRISP-DM, terdiri dari enam tahap utama, dengan dataset sebanyak 365 transaksi penjualan dan parameter minimum support sebesar 2% serta confidence sebesar 60%. Hasil penelitian menunjukkan bahwa algoritma Apriori menghasilkan aturan asosiasi dengan tingkat akurasi sebesar 63,19%, rata-rata confidence 75%, dan support 4,5%, sementara FP-Growth hanya mencapai akurasi 2,92%. Temuan ini menunjukkan bahwa dalam konteks data transaksi penjualan seragam sekolah, Apriori lebih unggul dalam menggali pola pembelian konsumen. Kontribusi praktis dari penelitian ini adalah rekomendasi strategi bundling produk dan optimasi stok berbasis pola asosiasi aktual, yang dapat diterapkan oleh pelaku usaha retail pendidikan untuk meningkatkan efisiensi dan efektivitas bisnis.
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References
- Asha Osman, Oluwatomisin Olawale Fowowe, Rasheed Agboluaje, and Precious Ozemoya Orekha, “Integrating machine learning in business analytics consulting for proactive decision-making and innovation,” World Journal of Advanced Research and Reviews, vol. 25, no. 1, pp. 1817–1836, Jan. 2025, doi: 10.30574/wjarr.2025.25.1.0251.
- H. K. Bhuyan and V. Ravi, “Analysis of Subfeature for Classification in Data Mining,” IEEE Trans Eng Manag, vol. 70, no. 8, pp. 2732–2746, Aug. 2023, doi: 10.1109/TEM.2021.3098463.
- M. Alawadh, A. Barnawi, M. M. Alawadh, and A. M. Barnawi, “A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule The Kingdome of Saudi Arabia *Corresponding Author,” A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule Article in Journal on Big Data, 2022, doi: 10.32604/jbd.2022.02174.
- M. J. Hoque, Md. S. Islam, and S. A. Mohtasim, “Optimizing Decision-Making Through Customer-Centric Market Basket Analysis,” Journal of Operational and Strategic Analytics, vol. 2, no. 2, pp. 72–83, Apr. 2024, doi: 10.56578/josa.020201.
- F. A. Saputra and A. Iskandar, “Data Mining Penerapan Asosiasi Apriori Dalam Penentuan Pola Penjualan,” Journal of Computer System and Informatics (JoSYC), vol. 4, no. 4, pp. 778–788, Aug. 2023, doi: 10.47065/josyc.v4i4.4043.
- R. Amelia, D. Darmansyah, and A. M. Rismadin, “Perbandingan Algoritma Apriori dan Fp-Growth dalam Pengaplikasian Market Basket Analysis untuk Strategi Bisnis Retail,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 1, Jun. 2024, doi: 10.47065/bits.v6i1.5388.
- H. Hery and A. E. Widjaja, “Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales,” Journal of Digital Market and Digital Currency, vol. 1, no. 1, pp. 63–83, Jun. 2024, doi: 10.47738/jdmdc.v1i1.2.
- J. G. P. Salsabilla, W. Y. Rochmah, and D. B. Baskara, “Implementation of Data Mining Using Apriori and FP-Growth Algorithms to Determine Product Bundling Strategy in Sakinah Supermarket Keputih,” in 2024 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), 2024, pp. 193–198. doi: 10.1109/AGERS65212.2024.10932906.
- V. Srinadh, “Evaluation of Apriori, FP Growth and Eclat Association Rule Mining Algorithms,” Int J Health Sci (Qassim), no. II, pp. 7475–7485, 2022, doi: 10.53730/ijhs.v6nS2.6729.
- H. Xie, “Research and Case Analysis of Apriori Algorithm Based on Mining Frequent Item-Sets,” Open J Soc Sci, vol. 09, no. 04, pp. 458–468, 2021, doi: 10.4236/jss.2021.94034.
- Y. S. Putra, R. Kurniawan, and Y. A. Wijaya, “Penerapan Data Mining Menggunakan Algoritma Fp-Growth Pada Data Penjualan Sembako,” 2024.
- B. Aktavera and H. Oktafia Lingga Wijaya, “Analisis Association Rule Menggunakan Algoritma Apriori Dan Algoritma Fp Growth,” 2024.
- R. Lorentiana Wijayanti, R. Kurniawan, R. Herdiana, and H. Susana, “Komparasi Algoritma Apriori Dan Fp-Growth Untuk Memberikan Strategi Diskon,” 2024.
- C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
- R. A. Casonatto, T. D. P. G. Souza, and A. M. Mariano, “Quality and Risk Management in Data Mining: A CRISP-DM Perspective,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 161–168. doi: 10.1016/j.procs.2024.08.257.
- Y. A. Singgalen, “Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW,” Journal of Information System Research (JOSH), vol. 4, no. 4, pp. 1343–1353, Jul. 2023, doi: 10.47065/josh.v4i4.3917.
- J. A. Solano, D. J. Lancheros Cuesta, S. F. Umaña Ibáñez, and J. R. Coronado-Hernández, “Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 512–517. doi: 10.1016/j.procs.2021.12.278.
- A. M. Shimaoka, R. C. Ferreira, and A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Reviews on Computer Science, vol. 4, no. 1, pp. 28–43, Oct. 2024, doi: 10.5753/reviews.2024.3757.
- S. N. Luqman et al., “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM,” 2021.
- M. H. Malik et al., “Market Basket Analysis for Next Basket Item Prediction Using Data Mining and Machine Learning,” 2024.
References
Asha Osman, Oluwatomisin Olawale Fowowe, Rasheed Agboluaje, and Precious Ozemoya Orekha, “Integrating machine learning in business analytics consulting for proactive decision-making and innovation,” World Journal of Advanced Research and Reviews, vol. 25, no. 1, pp. 1817–1836, Jan. 2025, doi: 10.30574/wjarr.2025.25.1.0251.
H. K. Bhuyan and V. Ravi, “Analysis of Subfeature for Classification in Data Mining,” IEEE Trans Eng Manag, vol. 70, no. 8, pp. 2732–2746, Aug. 2023, doi: 10.1109/TEM.2021.3098463.
M. Alawadh, A. Barnawi, M. M. Alawadh, and A. M. Barnawi, “A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule The Kingdome of Saudi Arabia *Corresponding Author,” A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule Article in Journal on Big Data, 2022, doi: 10.32604/jbd.2022.02174.
M. J. Hoque, Md. S. Islam, and S. A. Mohtasim, “Optimizing Decision-Making Through Customer-Centric Market Basket Analysis,” Journal of Operational and Strategic Analytics, vol. 2, no. 2, pp. 72–83, Apr. 2024, doi: 10.56578/josa.020201.
F. A. Saputra and A. Iskandar, “Data Mining Penerapan Asosiasi Apriori Dalam Penentuan Pola Penjualan,” Journal of Computer System and Informatics (JoSYC), vol. 4, no. 4, pp. 778–788, Aug. 2023, doi: 10.47065/josyc.v4i4.4043.
R. Amelia, D. Darmansyah, and A. M. Rismadin, “Perbandingan Algoritma Apriori dan Fp-Growth dalam Pengaplikasian Market Basket Analysis untuk Strategi Bisnis Retail,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 1, Jun. 2024, doi: 10.47065/bits.v6i1.5388.
H. Hery and A. E. Widjaja, “Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales,” Journal of Digital Market and Digital Currency, vol. 1, no. 1, pp. 63–83, Jun. 2024, doi: 10.47738/jdmdc.v1i1.2.
J. G. P. Salsabilla, W. Y. Rochmah, and D. B. Baskara, “Implementation of Data Mining Using Apriori and FP-Growth Algorithms to Determine Product Bundling Strategy in Sakinah Supermarket Keputih,” in 2024 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), 2024, pp. 193–198. doi: 10.1109/AGERS65212.2024.10932906.
V. Srinadh, “Evaluation of Apriori, FP Growth and Eclat Association Rule Mining Algorithms,” Int J Health Sci (Qassim), no. II, pp. 7475–7485, 2022, doi: 10.53730/ijhs.v6nS2.6729.
H. Xie, “Research and Case Analysis of Apriori Algorithm Based on Mining Frequent Item-Sets,” Open J Soc Sci, vol. 09, no. 04, pp. 458–468, 2021, doi: 10.4236/jss.2021.94034.
Y. S. Putra, R. Kurniawan, and Y. A. Wijaya, “Penerapan Data Mining Menggunakan Algoritma Fp-Growth Pada Data Penjualan Sembako,” 2024.
B. Aktavera and H. Oktafia Lingga Wijaya, “Analisis Association Rule Menggunakan Algoritma Apriori Dan Algoritma Fp Growth,” 2024.
R. Lorentiana Wijayanti, R. Kurniawan, R. Herdiana, and H. Susana, “Komparasi Algoritma Apriori Dan Fp-Growth Untuk Memberikan Strategi Diskon,” 2024.
C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
R. A. Casonatto, T. D. P. G. Souza, and A. M. Mariano, “Quality and Risk Management in Data Mining: A CRISP-DM Perspective,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 161–168. doi: 10.1016/j.procs.2024.08.257.
Y. A. Singgalen, “Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW,” Journal of Information System Research (JOSH), vol. 4, no. 4, pp. 1343–1353, Jul. 2023, doi: 10.47065/josh.v4i4.3917.
J. A. Solano, D. J. Lancheros Cuesta, S. F. Umaña Ibáñez, and J. R. Coronado-Hernández, “Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 512–517. doi: 10.1016/j.procs.2021.12.278.
A. M. Shimaoka, R. C. Ferreira, and A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Reviews on Computer Science, vol. 4, no. 1, pp. 28–43, Oct. 2024, doi: 10.5753/reviews.2024.3757.
S. N. Luqman et al., “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM,” 2021.
M. H. Malik et al., “Market Basket Analysis for Next Basket Item Prediction Using Data Mining and Machine Learning,” 2024.