Product Layout Optimization for Fashion Items Using FP-Growth to Enhance UMKM Sales
DOI:
https://doi.org/10.71302/jbidai.v8i1.69Keywords:
Product Layout, FP-Growth, Association Rule, SalesAbstract
The application of data mining techniques in the business sector contributes significantly to strategic decision-making. This study implements the FP-Growth algorithm to analyze consumer purchasing patterns at Zaynthary Store, a fashion retail shop located in Tarakan City. A total of 161 sales transaction records were collected and processed to identify frequent itemsets and association rules that represent relationships between products. The findings reveal that certain item combinations are frequently purchased together, such as {Blouse → Jeans} with a confidence value of 55%, suggesting that these items should be placed near each other in the store display layout. FP-Growth has proven effective in exploring customer purchase patterns and providing layout recommendations that can support increased sales. These results can serve as a strategic reference for designing data-driven store layouts in the fashion retail industry.
References
[1] S. Kusuma, Manajemen Pemasaran di Era Digitalisasi, 1st ed. Padang, Sumatera Barat: Takaza Innovatix Labs, 2024.
[2] A. A. Baskara, N. M. Piranti, and M. F. Romdendine, “Framework Data Mining : Sebuah Survei,” J. Mhs. Tek. Inform., vol. 9, no. 3, pp. 4886–4895, 2025.
[3] M. Shawkat, M. Badawi, S. El-ghamrawy, R. Arnous, and A. El-desoky, “An optimized FP-growth algorithm for discovery of association rules,” J. Supercomput., vol. 78, no. 4, pp. 5479–5506, 2022, doi: 10.1007/s11227-021-04066-y.
[4] W. H. Belkadi, Y. Drias, and H. Drias, “Quantum FP-Growth for Association Rules Mining,” in International Symposium on Quantum Sciences: Applications and Challenges, Springer, 2024, pp. 91–106. doi: 10.1007/978-3-031-59318-5_8.
[5] R. Fitria, W. Nengsih, and D. H. Qudsi, “Implementasi Algoritma FP-Growth Dalam Penentuan Pola Hubungan Kecelakaan Lalu Lintas,” J. Sist. Inf., vol. 13, no. 2, pp. 118–124, 2017, doi: 10.21609/jsi.v13i2.551.
[6] A. Muhamad Andika, N. Suarna, and R. D. Dana, “Analisa Dataset Asosiasi Penjualan Menggunakan Metode FP-Growth,” J. Teknol. Ilmu Komput., vol. 2, no. 1, pp. 80–88, 2023, doi: 10.56854/jtik.v2i1.108.
[7] Rhayatun Aviqah, A. Muhammad, and E. P. W. Mandala, “Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 3, pp. 821–831, 2023, doi: 10.37859/coscitech.v4i3.5487.
[8] M. Maulana, “Penerapan Algoritma Apriori Dan Algoritma Fp-Growth Dalam Menemukan Hubungan Data Nilai Ijazah Matematika dan Bahasa Inggris Dengan Nilai Mata Pelajaran Pemrograman Dan Web Programming (Studi Kasus SMK Sandikta Kelas X Bekasi),” JUPITER J. Comput. Inf. Technol., vol. 1, no. 2, pp. 59–75, 2020, doi: 10.53990/jupiter.v1i2.9.
[9] S. Junaidi et al., Buku Ajar Machine Learning. 2024. [Online]. Available: www.buku.sonpedia.com
[10] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int. Conf. Very Large Data Bases, {VLDB’94}, pp. 487–499, 1994, [Online]. Available: citeseer.ist.psu.edu/agrawal94fast.html
[11] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” ACM Sigmod Rec., vol. 29, no. 2, pp. 1–12, 2000, doi: 10.1145/335191.3353.
[12] M. Tahir and N. Sitompul, “Penerapan Algoritma Fp-Growth Dalam Menentukan Kecenderungan Mahasiswa Mengambil Mata Kuliah Pilihan,” J. Ilm. NERO (Network Eng. Res. Oper., vol. 6, no. 1, pp. 59–66, 2021, doi: 10.21107/nero.v6i1.216.
[13] S. A. Rahmah, Zulham, and I. Rusydi, “Kajian Literatur Masalah - Masalah Yang Dihadapi Dengan Menggunakan Algoritma FP-Growth dan Apriori,” Proceeding Semin. Nas. Ilmu Komput., vol. 1, no. 1, pp. 235–240, 2021.
[14] Y. Syach Putra, R. Kurniawan, and Y. Arie Wijaya, “Penerapan Data Mining Menggunakan Algoritma Fp-Growth Pada Data Penjualan Sembako,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 561–567, 2024, doi: 10.36040/jati.v8i1.8391.
[15] C. H. Chee, J. Jaafar, I. A. Aziz, M. H. Hasan, and W. Yeoh, “Algorithms for frequent itemset mining: a literature review,” Artif. Intell. Rev., vol. 52, no. 4, pp. 2603–2621, 2019, doi: 10.1007/s10462-018-9629-z.
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