Analisis Keranjang Belanja pada Data Ritel Non-Toko menggunakan Algoritma FP-Growth
Abstract
Sales transaction data analysis is necessary to uncover relationships between items in the shopping basket, providing strategic insights for business decision-making. This study analyzes associations from a non-store retail sales transaction dataset using the CRISP-DM framework, processed in Google Colab with Python programming language. The model was built using the FP-Growth algorithm with various minimum support values. The results show that the model with a 1% (0.01) minimum support achieved the best balance, forming up to 4-itemsets and producing 954 association rules. The evaluation of the rules revealed a maximum confidence of 93.91% and a minimum of 9.39%, with a lift value > 1, indicating strong relationships between items that are frequently purchased together. These findings can be utilized for product layout arrangement and item recommendations, implemented through a web-based association analysis application.
Keywords: FP-Growth Algorithm; Market Basket Analysis; CRISP-DM; Retail Data; Data Mining
Abstrak
Analisis data transaksi penjualan diperlukan untuk mengungkap hubungan antar item dalam keranjang belanja, sehingga memberikan wawasan strategis bagi pengambilan keputusan bisnis. Penelitian ini menganalisis asosiasi dari dataset transaksi penjualan ritel non-toko melalui kerangka kerja CRISP-DM, yang diproses ke dalam Google Colab dengan bahasa pemrograman Python. Model dibangun menggunakan algoritma FP-Growth dengan berbagai nilai minimum support. Hasilnya, model dengan minimum support 1% (0,01) mencapai keseimbangan terbaik, membentuk hingga 4-itemset dan menghasilkan 954 aturan asosiasi. Evaluasi aturan asosiasi mengungkap nilai confidence maksimum 93,91% dan minimum 9,39%, dengan nilai lift > 1, yang menunjukkan hubungan kuat antar item yang sering dibeli bersamaan. Temuan ini dapat dimanfaatkan untuk pengaturan tata letak dan rekomendasi item yang diimplementasikan melalui aplikasi analisis asosiasi berbasis web.
Keywords
References
F. Provost and T. Fawcett, “Data Science and Its Relationship to Big Data and Data-Driven Decision Making,” Big Data, vol. 1, no. 1, pp. 51–59, 2013, doi: 10.1089/big.2013.1508.
A. Baijuri et al., Analisis Sistem Informasi, I. Padang: CV. Gita Lentera, 2023.
S. Chevalier, “Global retail e-commerce sales 2014-2027", Statista, May 22, 2024. [Online]. Tersedia: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ [Diakses: 5 Mei 2024].
D. Supriyadi, “Penerapan Association Rule Mining Berbasis Algoritma Frequent Pattern Growth untuk Rekomendasi Penjualan,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 135–148, 2020, doi: 10.35957/jatisi.v7i2.339.
U. Sa’adah, M. Yessi Rochayani, D. Wahyu Lestari, and D. Ayu Lusia, Kupas Tuntas Algoritma Data Mining dan Implementasinya menggunakan R, I. Malang: UB Press, 2021.
J. Xu, H. Naseer, S. Maynard, and J. Filippou, “Leveraging Data and Analytics for Digital Business Transformation through DataOps: An Information Processing Perspective,” in Seminar Digital Business Transformation trough DataOps, ACIS - Australasian Conference on Information Systems, Proceedings, pp. 1–11, 2021.
I. Musdalifah and A. Jananto, “Analisis Perbandingan Algoritma Apriori Dan FP-Growth Dalam Pembentukan Pola Asosiasi Keranjang,” J. Ilm. Komput., vol. 18, no. 2, p. 10-21, 2022.
A. F. Ramadhan, N. I. Jaafar, and F. P. Tajudeen, “Data Governance and its Scientific Outlook in Indonesia: A Literature Review,” J. Manag. Inf. Decis. Sci., vol. 24, no. 3, pp. 1–10, 2021.
E. D. Brown, “Drowning in Data, Starved for Information, Big Data Featured,” October 22, 2014. [Online]. Tersedia: https://ericbrown.com/drowning-in-data-starved-for-information.htm [Diakses: 12 Maret 2024]
E. S. A. Sahputra, “Application of Big Data and Analytics to Increase Competitive Advantage,” J. Indones. Sos. Teknol., vol. 5, no. 5, pp. 2383–2390, 2024, doi: 10.59141/jist.v5i5.1084.
A. Kaur and S. Lodhia, “Stakeholder Engagement in Sustainability Accounting and Reporting,” Account. Audit. Account. J., vol. 31, no. 1, pp. 338–368, 2018, doi: 10.1108/aaaj-12-2014-1901.
M. Fitri, L. M. Anastasia, and M. A. Tubagus, Data Mining Konsep Dan Penerapannya, I. Yogyakarta: Deepublish, 2021.
A. T. Y. Al Abd Alazeez, “Data Stream Mining Between Classical and Modern Applications: A Review,” J. Educ. Sci., vol. 30, no. 5, pp. 30–43, 2021, doi: 10.33899/edusj.2021.130093.1158.
P. N. Harahap and S. Sulindawaty, “Implementasi Data Mining Dalam Memprediksi Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus PT.Arma Anugerah Abadi Cabang Sei Rampah),” J. Sist. Inf. Kaputama, vol. 4, no. 1, pp. 1-12, 2020.
L. Indah Prahartiwi, S. Informasi, S. Nusa Mandiri, J. Damai No, and W. Jati Barat Jakarta Selatan DKI Jakarta, “Pencarian Frequent Itemset pada Analisis Keranjang Belanja Menggunakan Algoritma FP-Growth,” Inf. Syst. Educ. Prof., vol. 2, no. 1, pp. 1–10, 2017.
S. W. Romi, “5 Peran Data Mining: Kuasai Konsep dengan Mudah dan Praktek Cepat untuk Data Scientist Galau - YouTube,” 2022. [Online]. Tersedia: https://www.youtube.com/watch?v=VstEO9nkVoM [Diakses: 28 September 2024].
BAKTI, “3 Komponen Sistem Komputer : Hardware, Software dan Brainware”, BAKTI KOMINFO, 2024. [Online]. Tersedia: https://www.baktikominfo.id/id/detail-berita/3-komponen-sistem-komputer-hardware-software-dan-brainware [Diakses: 25 Maret 2024].
I. G. A. Indrawan, M. Sudarma, and L. Jasa, “Implementasi Algoritma FP-Growth Dengan Closure Table Untuk Penemuan Frequent Itemset Pada Keranjang Belanja,” Maj. Ilm. Teknol. Elektro, vol. 17, no. 2, pp. 199-208, 2018, doi: 10.24843/mite.2018.v17i02.p06.
Y. Husain, E.D. Oktaviyani, and S. Christina, “Analisis Perbandingan Algoritma Apriori, FP-Growth, Dan Eclat dalam Menemukan Pola Pembelian Konsumen,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 231–243, 2023, doi: 10.24002/konstelasi.v3i2.7007.
D. Rachmawati, Y. Cahyana, E. E. Awal, and S. Faisal, “Perbandingan Algoritma Apriori dan Algoritma FP-Growth dalam Menentukan Pola Penjualan Pupuk,” vol. 3, no. 1, pp. 21–31, 2024.
S. Yakub, A. Fitri Boy, I. Mariami, W. Stmik, and T. Dharma, “J-SISKO TECH Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Penerapan Data Mining Pengaturan Pola Tata Letak Barang Pada Berkah Swalayan Untuk Strategi Penjualan Menggunakan Algoritma Apriori,” J-SISKO TECH J. Teknol. Sist. Inf. dan Sist. Komput. TGD, vol. 69, no. 1, pp. 69–75, 2019.
A. A. N. Risal, F. Adiba, and A. A. Nurfitri, “Aturan Asosiasi Berbasis Algoritma Apriori Pada Penjualan Retail Online,” J. Mediat., vol. 6, no. 2, pp. 1–4, 2024, doi: 10.59562/mediatik.v6i2.1394.
F. Firmansyah and A. Yulianto, “Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta,” J. Informatics Telecommun. Eng., vol. 4, no. 2, pp. 383–392, 2021, doi: 10.31289/jite.v4i2.4539.
D. H. Suryawan, “Dializer: Aplikasi Simulasi Diagram Alir Berbasis Web,” Jati (Jurnal Mhs. Tek. Inform., vol. 7, no. 5, pp. 3284–3490, 2024, doi: 10.36040/jati.v7i5.7685.
A. I. Idris et al., “Comparison of Apriori, Apriori-Tid and FP-Growth Algorithms in Market Basket Analysis at Grocery Stores,” Ijics (International J. Informatics Comput. Sci., vol. 6, no. 2, pp. 107-116, 2022, doi: 10.30865/ijics.v6i2.4535.
A. H. Nasyuha et al., “Frequent Pattern Growth Algorithm for Maximizing Display Items,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 390-401, 2020, doi: 10.12928/telkomnika.v19i2.16192.
R. Yogasuwara and F. Ferdiansyah, “Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk,” J. Sist. Komput. dan Inform., vol. 4, no. 1, pp. 165-175, 2022, doi: 10.30865/json.v4i1.4894.
D. Aha, “Dataset”, Home - UCI Machine Learning Repository, 2023. [Online]. Tersedia: https://archive.ics.uci.edu/dataset/352/online+retail [Diakses: 25 Maret 2024].
J. Saltz, I. Shamshurin, and C. Connors, “Predicting data science sociotechnical execution challenges by categorizing data science projects,” J. Assoc. Inf. Sci. Technol., vol. 68, no. 12, pp. 2720–2728, Dec. 2017, doi: 10.1002/ASI.23873.
F. E. Gulo, “Penerapan Aturan Asosiasi Di Darvina Mart Untuk Menentukan Pola Pembelian Pelanggan,” J. Ind. Innov. Saf. Eng., vol. 1, no. 2, pp. 92–100, 2023, doi: 10.35718/jinseng.v1i2.897.
J. Ha, M. Kambe, and J. Pe, Data Mining : Concepts and Techniques, III. Amerika: Morgan Kaufmann, 2011.
Lia Farokhah, “Evaluasi Algoritma Asosiasi Menggunakan Lift Ratio | DATA MINING - Youtube,” 2021. [Online]. Tersedia: https://youtu.be/A2jKP2vke2o?si=X3AkL3nv2qLcnQbq [Diakses: 25 Maret 2024].
How To Cite This :
Refbacks
- There are currently no refbacks.
