ANALISA POLA DATA HASIL PEMBANGUNAN KABUPATEN MALANG MENGGUNAKAN METODE ASSOCIATION RULE

Dewi Sibagariang, Karina Auliasari

Abstract


Data of development results in an area divided into several sectors. Each sector has a commodity, government use this data to determine potential comodity in ​​small coverage area.
This paper was based on our  research use association rule method,  as we know  this method commonly used in data mining to discover pattern from huge data. Apriori is an algorithm that is implemented on application in this research, this algorithm is used  to  generate strong association information (strong linkage) between commodities in each sector. Support, confidence values and relationship between each commodities in 33 districts Kabupaten Malang displayed by application. From test result showed that more higher  value of confidence and support make the strong relationships between commodity value. Minimum limit value can not support more than 33, because most transaction data which is calculated from the total number of 33 districts in Malang.


Keywords


Development result; Data Mining; Association Rule; Apriori Algorithm

Full Text:

Doc PDF


DOI: https://doi.org/10.18860/mat.v0i0.2419

Refbacks

  • There are currently no refbacks.




Copyright (c) 2013 Dewi Sibagariang, Karina Auliasari

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The journal is indexed by :

Dimensions Sinta CrossRef GoogleScholar
Index Copernicus Moraref Portal Garuda

 

_______________________________________________________________________________________________________________

Editorial Office:
Informatics Engineering Department
Faculty of Science and Technology
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144
Email: matics@uin-malang.ac.id
_______________________________________________________________________________________________________________

Creative Commons License
This work is licensed under a CC-BY-NC-SA 4.0.
© All rights reserved 2015. MATICS , ISSN : 1978-161X | e-ISSN :  2477-2550