KURNIANTO U., HIZKIA KEVIN (2019) FREQUENT ITEM SETS RECOMMENDATION SIMULATION SYSTEM USING FP-GROWTH ALGORITHM. Other thesis, UNIKA SOEGIJAPRANATA SEMARANG.
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Abstract
Keeping records of sales transactions in a business is important. Not only to calculate profits, but sales transactions can find frequently purchased items. Those items are found by counting the sum of items per transaction. Then items are sorted from most purchased times until few purchased items. However, if data is large enough, the items are not easily calculated manually. FP-Growth algorithm could solve this problem. First,data is need to be inputted, Then, minimum support is inputted. After that, system simulation can be worked. After that, data item and transaction data changed into array. And those two array modified and inputted into new temporary table. This processed able to create Conditional Pattern Base until Frequent Patern is founded. The result of this research is Frequent Patern 2-Set and Frequent Patern 3-Set is found. Then for system analysis, time and memory usage table is found by processed 100, 250, 500 and 1000 data. The table is used for comparing data increasing and memory and time usage increasing. Keyword: FP-Growth, Data Mining, Simulation, PHP-based
| Item Type: | Thesis (Other) |
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| Subjects: | 000 Computer Science, Information and General Works > 005 Computer programming, programs & data > Information Systems |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mr Lucius Oentoeng |
| Date Deposited: | 22 Nov 2019 01:12 |
| Last Modified: | 13 Oct 2020 03:55 |
| URI: | http://repository.unika.ac.id/id/eprint/20034 |
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