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Market Basket Analysis Data Groceries Menggunakan Equivalence Class Transformation
(1) Universitas Pembangunan Nasional Veteran Jakarta
(2) Politeknik Elektronika Negeri Surabaya
(*) Corresponding Author
Abstract
Peningkatan volume data, pengelolaan dan analisis data menjadi elemen penting bagi pengambilan keputusan bisnis. Salah satu metode yang popular adalah dengan Market Basket Analysis untuk menganalisis data transaksi yang ada pada sebuah data. Pendeketan dengan metode ECLAT adalah salah satu yang bisa dilakukan, karena keunggulannya pada teknik depth-first search. Data yang diolah pada penelitian ini sebanyak 38.766 record data transaksi. Hasil penelitian menunjukan kombinasi 1 itemset dengan minimal support 20 dan 250 mampu menghasilkan data yang relevan, sementara pada kombinasi 2 itemset hanya mampu menghasilkan analisis pada nilai minimal support 10 dikarenakan keterbatasan data pembelian dengan kombinasi 2 item. Waktu eksekusi algoritma ECLAT juga menunjukan kondisi 1 itemset dengan waktu 0,0402 detik dan eksekusi terlambat pada kondisi 2 item-set 8,773 detik.
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Abbasi, S., & Moeini, A. (2021). BloomEclat: Efficient Eclat Algorithm based on Bloom filter. In Journal of Algorithms and Computation (Vol. 53, Issue 1). http://jac.ut.ac.ir
Akhmad Khabrun Siregar, B. A. K. A. P. K. S. (2018). Perbandingan Algoritme FP-Growth dan Eclat untuk Analisis Pola Pembelian Konsumen pada Toko “X.” CITISEE.
Business Analytics Institute. (2024). Market Basket Analysis for Business Growth.
Galit Shmueli, Peter C. Bruce, Peter Gedeck, Inbal Yahav, & Nitin R. Patel. (2023). Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R. Wiley.
George Dimitoglou, Leonidas Deligiannidis, & Hamid Arabnia. (2024). Big Data, Data Mining and Data Science: Algorithms, Infrastructures, Management and Security (Intelligent Computing Book 2) (George Dimitoglou, Leonidas Deligiannidis, & Hamid Arabnia, Eds.). De Gruyter.
Iqbal Sarker, Alan Colman, Jun Han, & Paul Watters. (2022). Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making. Springer.
Krishnan, M. S., Nair, A. S., & Sebastian, J. (2022). Comparative Analysis of Apriori and ECLAT Algorithm for Frequent Itemset Data Mining (pp. 489–497). https://doi.org/10.1007/978-981-16-3675-2_37
Lin, N., & Win, T. (2024). Optimizing the Performance of the ECLAT Algorithm using K-means Clustering for Frequently Itemset Mining for Large Datasets. 2024 5th International Conference on Advanced Information Technologies (ICAIT), 1–6. https://doi.org/10.1109/ICAIT65209.2024.10754926
Man, M., Jalil, M. A., & Bakar, W. A. W. A. (2023). Fi-Eclat: An enhancement of incremental Eclat algorithm. 060006. https://doi.org/10.1063/5.0110230
Mohapatra, D., Tripathy, J., Mohanty, K. K., & Nayak, D. S. K. (2021). Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 879–882. https://doi.org/10.1109/ICCMC51019.2021.9418049
Omran, O. M. Ben. (2023). Using ECLAT Algorithm for Privacy Preserving on Publishing Data. 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 407–412. https://doi.org/10.1109/MI-STA57575.2023.10169272
Srinadh, V. (2022a). Evaluation of Apriori, FP growth and Eclat association rule mining algorithms. International Journal of Health Sciences, 7475–7485. https://doi.org/10.53730/ijhs.v6nS2.6729
Srinadh, V. (2022b). Evaluation of Apriori, FP growth and Eclat association rule mining algorithms. International Journal of Health Sciences, 7475–7485. https://doi.org/10.53730/ijhs.v6nS2.6729
Yusuf Husain, Enny Dwi Oktaviyani, & Sherly Christina. (2023). Analisis Perbandingan Algoritma Apriori, FP-Growth, Dan Eclat dalam Menemukan Pola Pembelian Konsumen. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 3(2), 231–243. https://doi.org/10.24002/konstelasi.v3i2.7007
Zunan Setiawan, Muhammad Fajar , Arif Mudi Priyatno, Anggi Yhurinda Perdana Putri, Mediana Aryuni , Siti Yuliyanti, Harya Widiputra , Budanis Dwi MeilaZunan Setiawan, Muhammad Fajar , Arif Mudi Priyatno, Anggi Yhurinda Perdana Putri, Mediana Aryuni , Siti, A. W. (2023). Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia.
DOI: https://doi.org/10.53514/jco.v5i1.593
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Computer Science and Informatic Systems:J-Cosys
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