CALVIEN, CALVIEN (2019) DATA ANALYSIS OF TRANSACTION ITEMS USING APRIORI ALGORITHM. Other thesis, UNIKA SOEGIJAPRANATA SEMARANG.
|
Text (COVER)
15.K1.0047 CALVIEN (3)..pdf COVER.pdf Download (1MB) | Preview |
|
|
Text (BAB I)
15.K1.0047 CALVIEN (3)..pdf BAB I.pdf Download (62kB) | Preview |
|
![[img]](https://repository.unika.ac.id/style/images/fileicons/text.png) |
Text (BAB II)
15.K1.0047 CALVIEN (3)..pdf BAB II.pdf Restricted to Registered users only Download (69kB) |
|
|
Text (BAB III)
15.K1.0047 CALVIEN (3)..pdf BAB III.pdf Download (61kB) | Preview |
|
|
Text (BAB IV)
15.K1.0047 CALVIEN (3)..pdf BAB IV.pdf Download (257kB) | Preview |
|
|
Text (BAB V)
15.K1.0047 CALVIEN (3)..pdf BAB V.pdf Download (172kB) | Preview |
|
|
Text (BAB VI)
15.K1.0047 CALVIEN (3)..pdf BAB VI.pdf Download (60kB) | Preview |
|
|
Text (DAFTAR PUSTAKA)
15.K1.0047 CALVIEN (3)..pdf DAPUS.pdf Download (76kB) | Preview |
|
|
Text (LAMPIRAN)
15.K1.0047 CALVIEN (3)..pdf LAMP.pdf Download (548kB) | Preview |
Abstract
On the marketplace transaction activities will definitely occur every day. In a weeks or months the transaction data will certainly be very large. This large transaction data can be utilized as a sales strategy. This research aims to utilize the transaction data to find purchasing patterns from consumer habits. This pattern is useful for creating a system for structuring items according to consumer purchasing habits, and also to increase the stock of items on the items that are most often purchased. With this apriori algorithm to search for purchasing patterns by consumers is very possible to be detected. Besides that this research also added a recommendation feature to find out the relationship between items. The result of this project is the time to find a pattern for 100 transactions is under 2 seconds, while in 1.000 transactions it takes approximately 10 seconds. The accuracy of the search for this best seller item is 100%. After the pattern is calculated, the recommendation system can be utilized. Keyword: data mining, apriori, support, confidence
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 004 Data processing & computer science |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mr Lucius Oentoeng |
| Date Deposited: | 16 Jul 2019 07:45 |
| Last Modified: | 01 Oct 2020 01:54 |
| URI: | http://repository.unika.ac.id/id/eprint/19726 |
Actions (login required)
 |
View Item |
