Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Hafilah Hamimi, Hafilah
State University of Padang
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
The Optimized K-Means Clustering Algorithms To Analyzed the Budget Revenue Expenditure in Padang Novaliendry, Dony; Hendriyani, Yeka; Yang, Cheng-Hong; Hamimi, Hafilah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.2.531

Abstract

APBD is a systematic detailed list of receipts, expenditures and local spending within a year arranged in PERMENDAGRI No. 16 of 2006, so that the data of APBD can be used as guidelines for governments and local expenditures in carrying out activities to raise revenue to maintain economic stability and to avoid inflation and deflation. Government financial institutions in areas such as DPKA Padang, experienced difficulties in identifying the relevance of each archive data on APBD, that result in a data warehouse. In addition, to the administration, APBD in the government of Padang have not been effective. To minimize the difficulty in identifying data archive of APBD, then the data warehouse can be used to produce knowledge using the techniques of Data Mining (DM). The method that is used are clustering and forecasting. Clustering performed using the K-Means Algorithm while forecasting is done by using multiple linear regressions. These methods intended to classify and identify the data in the budget that have certain characteristics in common, and can predict the value of APBD for the following years.
Co-Authors Cheng-Hong Yang, Cheng-Hong Dony Novaliendry Yeka Hendriyani