Pemanfaatan Algoritma FP-Growth pada Teknik Data Mining untuk Mengidentifikasi Pola Stok Produk Elektronik
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Irawaty Irawaty
*
Universitas Gunadarma, Depok,
Indonesia
(*) Corresponding Author
Abstract
Managing the availability of electronic product stock is a crucial issue in the retail world due to the high variety of products and dynamic consumer purchasing patterns. Inaccuracy in determining the amount of stock can lead to excess inventory or product shortages, which impacts on decreasing operational efficiency. This study aims to apply the FP-Growth algorithm in the data mining process to determine the pattern of electronic product stock availability based on purchase transaction data. The dataset used in this study consists of 150 electronic product purchase transaction data. The main problem faced is the lack of optimal utilization of transaction data to determine the relationship between products that are frequently purchased together. As a solution, this study applies the Frequent Pattern Growth (FP-Growth) algorithm because of its ability to find association patterns without the need to generate candidate itemsets, making it more efficient in data processing. The research process begins with calculating the frequency of item occurrences, determining the minimum support value of 20% (30 transactions), forming an FP-Tree, and mining frequent itemsets and association rules. The results show that Mouse, Laptop, and Keyboard are the items with the highest frequency, respectively 80%, 73%, and 70% of the total transactions. The Mouse–Laptop–Keyboard purchasing pattern has a support value of 55% with a confidence level of 80%. While the Mouse → Keyboard rule yields the highest confidence level of 85%. Based on these results, it can be concluded that the FP-Growth algorithm is effective in identifying purchasing patterns for electronic products and can be used as a basis for decision-making in prioritizing stock availability more precisely and data-driven.
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References
Y. S. Putra, R. Kurniawan, Y. A. Wijaya, T. Informatika, S. Informatika, and D. Mining, “Penerapan Data Mining Menggunakan Algoritma Fp-Growth Pada Data Penjualan Sembako,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 561–567, 2024.
R. N. Dianti and J. Zeniarja, “Implementasi Algoritma Apriori Untuk Analisis Pola Pembelian Konsumen Pada Toserba Yusuf Semarang,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 2, pp. 1013–1021, 2024, doi: 10.29100/jipi.v9i2.5421.
L. M. Lestari, I. Ali, S. Tinggi, M. Informatika, and S. Ikmi, “Penerapan Algoritma FP-Growth Untuk Menentukan Pola Penjualan Toko Ellia Umami,” J. Student Res., vol. 1, no. 3, pp. 367–378, 2023.
N. D. Eva Nurarofah, Ruli Herdiana, “Penerapan Asosiasi Menggunakan Algoritma Fp-Growth Pada Pola Transaksi Penjualan Roti,” JATI, vol. 7, no. 1, 2023.
P. Konsumen, D. I. Warung, and M. Dede, “Penerapan Algoritma Fp-Growth Untuk Mengetahui Pola Pembelian Konsumen Di Warung Makan Dede,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 1221–1228, 2024.
A. Pastika and V. Widya, “Implementasi Data Mining Sebagai Penentu Persediaan Produk Dengan Algoritma Fp-Growth Pada Data Penjualan Sinarmart,” J. Publ. ILMU Komput. DAN Multimed., vol. 1, no. 2, 2022.
A. F. Boy, S. Yakub, I. Ishak, and Z. Azmi, “Implementasi Data Mining Pada Pengaturan Distribusi Barang Dengan Menggunakan Algoritma Fp-Growth,” J. Sci. Soc. Res., vol. 5, no. 2, p. 431, 2022, doi: 10.54314/jssr.v5i2.947.
P. Salsabila, E. Wahyudin, G. Dwilestari, K. Kaslani, and F. Subhiyanto, “Penerapan Algoritma Fp-Growth Untuk Mengetahui Pola Pembelian Konsumen Di Warung Makan Dede,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 1221–1128, 2024, doi: 10.36040/jati.v8i1.8964.
L. Oktaviani, Tri Anelia, Hegarmanah Muhabatin, Yudhistira Arie Wijaya, and Dian Ade Kurnia, “Penerapan Algoritma Fp-Growth Untuk Menganalisis Pola Belanja,” KOPERTIP J. Ilm. Manaj. Inform. dan Komput., vol. 5, no. 1, pp. 29–35, 2021, doi: 10.32485/kopertip.v5i1.153.
R. Wahyusari, “Penerapan Algortima FP-Growth Untuk Menemukan Pola Peminjaman Alat Pada Workshop Teknik Mesin,” Log. J. Ilmu Komput. dan Pendidik., vol. 1, no. 3, pp. 406–411, 2023, [Online]. Available: https://journal.mediapublikasi.id/index.php/logic/article/view/2745%0Ahttps://journal.mediapublikasi.id/index.php/logic/article/download/2745/1211
B. S. Pranata and D. P. Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bull. Inf. Technol., vol. 1, no. 2, pp. 83–91, 2020.
R. Aditiya, S. Defit, and G. W. Nurcahyo, “Prediksi Tingkat Ketersediaan Stock Sembako Menggunakan Algoritma FP-Growth dalam Meningkatkan Penjualan,” J. Inform. Ekon. Bisnis, vol. 2, pp. 67–73, 2020, doi: 10.37034/infeb.v2i3.44.
J. Hutagalung, A. F. Boy, and D. Nofriansyah, “Pemilihan Komandan Komando Distrik Militer Menggunakan Metode WASPAS,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 420–429, 2022.
A. Putera, U. Siahaan, N. A. Harahap, and R. Y. Simanullang, “Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset,” J. BIT (Bulletin Inf. Technol., vol. 6, no. 1, pp. 33–39, 2025, doi: 10.47065/bit.v5i2.1830.
R. Y. Simanullang, P. Wanny, and S. M. Rambe, “Implementation of the K-Means Algorithm on Smart Systems in Grouping Gadget Accessory Purchase Patterns,” JITCSE (Journal Inf. Technol. Komput. Sci. Electr. Eng., vol. 2, no. 3, pp. 22–31, 2026, doi: 0.61306/jitcse.
R. Fauzi, A. W. Aranski, and E. Hutabri, “Implementasi Data Mining Pada Penjualan Pakaian dengan Algoritma FP-Growth,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 2, 2023, doi: 10.30865/jurikom.v10i2.5795.
L. Al-Alawi, J. Al Shaqsi, A. Tarhini, and A. S. Al-Busaidi, “Using machine learning to predict factors affecting academic performance: the case of college students on academic probation,” Educ. Inf. Technol., vol. 28, no. 10, pp. 12407–12432, 2023, doi: 10.1007/s10639-023-11700-0.
Harlan Kurnia AR and Nurmaliana Pohan, “Implementasi Algoritma FP-Growth untuk Mengukur Tingkat Kemampuan Siswa dalam Prestasi Belajar,” SATIN - Sains dan Teknol. Inf., vol. 8, no. 1, pp. 138–146, 2022, doi: 10.33372/stn.v8i1.843.
U. Soleha, M. Widyastuti, L. Khairani, R. Maghfirah, and A. Fauzan, “Penerapan Algoritma Fp-Growth Dalam Penentuan Pola Pembelian Konsumen 212 Mart Pekanbaru,” IJIRSE Indones. J. Inform. Res. Softw. Eng., vol. 2, no. 2, pp. 93–99, 2022.
A. Fergina, P. A. Negara, A. Sujjada, and I. Sanjaya, “Implementasi Algoritma Apriori dan FP-Growth untuk Menganalisis Pola Pembelian Produk Skincare dan Kosmetik,” J. Ilm. KOMPUTASI, vol. 23, no. 3, pp. 433–442, 2024.
M. Informatika and S. I. Cirebon, “Penerapan Algoritma Fp-Growth Dalam Analisis Pola Transaksi Application Of Fp-Growth Algorithm In Transaction Pattern Analysis For Optimizing Transaction Data Management In Lia ’ S,” J. Kecerdasan Buatan dan Teknol. Inf., vol. 3, no. 1, 2024.
R. Gustrianda and D. I. Mulyana, “Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids,” J. Media Inform. Budidarma, vol. 6, no. 1, pp. 27–34, 2022.
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