COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021)
The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data,...
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| Format: | Article |
| Language: | English |
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Universitas Nahdlatul Ulama Surabaya
2023-11-01
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| Series: | Business and Finance Journal |
| Subjects: | |
| Online Access: | https://journal2.unusa.ac.id/index.php/BFJ/article/view/5226 |
| _version_ | 1797294175835652096 |
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| author | Rina Wahyuningsih Agus Suharsono Nur Iriawan |
| author_facet | Rina Wahyuningsih Agus Suharsono Nur Iriawan |
| author_sort | Rina Wahyuningsih |
| collection | DOAJ |
| description |
The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.
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| first_indexed | 2024-03-07T21:26:26Z |
| format | Article |
| id | doaj.art-ff16b46cee1c4acf804c4a1e1f89b74f |
| institution | Directory Open Access Journal |
| issn | 2527-4872 2477-393X |
| language | English |
| last_indexed | 2024-03-07T21:26:26Z |
| publishDate | 2023-11-01 |
| publisher | Universitas Nahdlatul Ulama Surabaya |
| record_format | Article |
| series | Business and Finance Journal |
| spelling | doaj.art-ff16b46cee1c4acf804c4a1e1f89b74f2024-02-27T04:38:16ZengUniversitas Nahdlatul Ulama SurabayaBusiness and Finance Journal2527-48722477-393X2023-11-018210.33086/bfj.v8i2.5226COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) Rina Wahyuningsih0Agus Suharsono1Nur Iriawan2Institut Teknologi Sepuluh Nopember Institut Teknologi Sepuluh Nopember, IndonesiaInstitut Teknologi Sepuluh Nopember, Indonesia The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions. https://journal2.unusa.ac.id/index.php/BFJ/article/view/5226Retail,Big DataAssociation RulesAprioriFP-GrowthECLAT |
| spellingShingle | Rina Wahyuningsih Agus Suharsono Nur Iriawan COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) Business and Finance Journal Retail, Big Data Association Rules Apriori FP-Growth ECLAT |
| title | COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) |
| title_full | COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) |
| title_fullStr | COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) |
| title_full_unstemmed | COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) |
| title_short | COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) |
| title_sort | comparison of market basket analysis method using apriori algorithm frequent pattern growth fp growth and equivalence class transformation eclat case study supermarket x transaction data for 2021 |
| topic | Retail, Big Data Association Rules Apriori FP-Growth ECLAT |
| url | https://journal2.unusa.ac.id/index.php/BFJ/article/view/5226 |
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