Implementasi Data Mining Pada Penjualan Pakaian dengan Algoritma FP-Growth
DOI:
https://doi.org/10.30865/jurikom.v10i2.5795Keywords:
Data Mining, Clothes, FP-Growth, Rapid MinerAbstract
The amount of competition in the business world, especially in the sales industry, required developers to find a strategy that could increase sales and marketing of products sold, one of which was using clothing sales data with data mining. Data Mining was an iterative and interactive process to find new patterns or models that can be generalized for the future, valuable, and understandable in a massive database. HAS Stores in the arrangement of goods layout still place goods according to groups and types of goods, so that it has an impact on service and item search when consumers want to buy more than one item and are located far apart. Therefore, this study aims to apply the FP-Growth Algorithm to find out the most sold clothing sales at HAS Stores in Batam city. This study uses the Association Rule method by utilizing the FP-Growth Algorithm. This study aimed to apply the FP-Growth Algorithm to determine the most sold clothing sales at HAS Stores in Batam city. Through the mining process with the FP-Growth (Frequent Pattern Growth) algorithm, the types of clothes sold will be obtained, and how much inventory the store needs to provide the clothing stock. The results showed that the most sold clothing products were Gamis and Jilbab through the calculation of support 53,33% and confidence 100%. Regarding these results, marketing strategies can be focused on the product and set a layout that customers can easily see.
References
Z. Allam and Z. A. Dhunny, “On big data, artificial intelligence and smart cities,†Cities, vol. 89, no. 1, pp. 80–91, Jun. 2019.
Kemenperin RI, “Industri Pakaian Jadi Catatkan Pertumbuhan Paling Tinggi,†Kementerian Perindutrian Repulik Indonesia, 2019. [Online]. Available: https://kemenperin.go.id/artikel/20641/Industri-Pakaian-Jadi-Catatkan-Pertumbuhan-Paling-Tinggi. [Accessed: 10-Dec-2022].
Kumala Sari, M. Syahril, and Suharsil, “Penerapan Data Mining Untuk Pengelompokkan Data Member Restoran Dengan Menggunkan Metode Hierarki Agglomerative Single Lingkage Pada Restoran Al-Jazeerah Medan,†J. CyberTech, vol. 4, no. 2, pp. 1–8, 2022.
J. R. Saura, “Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics,†J. Innov. Knowl., vol. 6, no. 2, pp. 92–102, Apr. 2021.
N. W. Wardani, Penerapan data mining dalam analytic CRM. Yogyakarta: Yayasan Kita Menulis, 2020.
P. Brous, M. Janssen, and P. Herder, “The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations,†Int. J. Inf. Manage., vol. 51, p. 101952, Apr. 2020.
C. Zai, “Implementasi Data Mining Sebagai Pengolahan Data,†Portal Data, vol. 2, no. 3, pp. 1–12, 2022.
M. R. Fanan, “Penggabungan Forward Selection Untuk Pemilihan Fitur Pada Prediksi Bimbingan Konseling Siswa Dengan Menggunakan Algoritma Naïve Bayes,†Smart Comp, vol. 9, no. 2, pp. 85–88, 2020.
F. Nabyla and Mukrodin, “Data Mining Pengelompokan Biaya Pengeluaran Rumah Sakit Oleh BPJS Kesehatan Menggunakan Algoritma K-Means,†Smart Comp, vol. 10, no. 2, pp. 79–84, 2021.
R. W. Suryaman and G. Wang, “Penerapan Data Mining Untuk Prediksi Slot Time di Bandara Internasional di Indonesia : Algoritma J48,†Smart Comp, vol. 11, no. 3, pp. 332–343, 2022.
E. Bu’ulolo and R. Syahputra, “Pemanfaatan Pembobotan Kriteria Dalam Metode AHP Untuk Memprediksi Infeksi Covid-19 Dengan Algoritma Nearest Neighbor,†Smart Comp, vol. 11, no. 1, pp. 14–18, 2022.
F. Kana, M. Ramadhan, and R. Mahyuni, “Implementasi Data Mining Menganalisa Pola Penjualan Rempah-Rempah Menggunakan Metode Fp-Growth,†J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 4, pp. 557–564, 2022.
N. Salu, A. Michael, S. Y. Padang, and M. S. Adda, “Prediksi Persediaan Sepeda Motor Pada Dealer Yamaha Jaya Baru Motor Mengunakan Metode Frequent Pattern (FF-Growth),†Infinity, vol. 2, no. 1, pp. 29–36, 2022.
A. Anggrawan, M. Mayadi, and C. Satria, “Menentukan Akurasi Tata Letak Barang dengan Menggunakan Algoritma Apriori dan Algoritma FP-Growth,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 125–138, Nov. 2021.
Y. P. Bunda, “Algoritma FP-Growth untuk Menganalisa Pola Pembelian Oleh-Oleh,†RJoCS (Riau J. Comput. Sci., vol. 6, no. 1, pp. 34–44, 2020.
Y. Wu and J. Zhang, “Retraction Note: Building the electronic evidence analysis model based on association rule mining and FP-growth algorithm,†Soft Comput., vol. 27, no. 1, pp. 621–621, Jan. 2023.
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, Mar. 2022.
A. Fajri, “Perancangan Rrelokasi Tata Letak Gudang Dengan Metode Systematic Layout Planning Pada PT. MKM,†J. Tek. Ind. J. Has. Penelit. dan Karya Ilm. dalam Bid. Tek. Ind., vol. 7, no. 1, p. 27, Jun. 2021.
A. Soetara, M. Machfud, M. J. Affandi, and A. Maulana, “Rancang Bangun Model Strategi Operasional Implementasi Lean Manufacturing Berkesinambungan Untuk Peningkatan Produktivitas Industri Pengolahan Kayu di Indonesia,†J. Apl. Bisnis dan Manaj., vol. 5, no. 2, pp. 187–199, May 2019.
E. Munanda and S. Monalisa, “Penerapan Algoritma FP-Growth pada Data Transaksi Penjualan Untuk Penentuan Tata letak Barang,†J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 7, no. 2, pp. 173–184, 2021.
Y. Li and S. Yin, “Mining Algorithm for Weighted FP-Growth Frequent Item Sets based on Ordered FP-Tree,†Int. J. Eng. Manag. Res., vol. 09, no. 05, pp. 154–158, Oct. 2019.
A. H. Nasyuha et al., “Frequent pattern growth algorithm for maximizing display items,†TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 19, no. 2, p. 390, Aug. 2020.
M. A. Prabukusumo and N. Sidik, “Pemodelan Pola Belanja Pelanggan Produk Infrastruktur dan Security menggunakan Algoritma FP-Growth,†J. Ilm. Komputasi, vol. 21, no. 3, Sep. 2022.
