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Hafilah Hamimi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Analisis Data Anggaran Pendapatan Belanja Daerah Menggunakan Clustering K-Means dan Forecasting (Studi Kasus pada DPKA Kota Padang) Hafilah Hamimi; Yeka Hendriyani; Dony Novaliendry
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 2, No 1 (2014): Januari - Juni 2014
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v2i1.3279

Abstract

APBD is a systematic detailed list of receipts, expenditures and local spending within a certain period ( 1 year ) arranged in Permendagri No. 16 of 2006, so that the data 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 a APBD that so much, that results 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 heap data archive APBD, then the data warehouse can be used to produce a knowledge that by using the techniques of Data Mining ( DM ), the method used is clustering and forecasting, clusterisasi performed using the K-Means Algorithm while for forecasting with multiple linear regression. With this method intended to classify and identify the data in the budget that have certain characteristics in common, and can predict the value of APBD in the future .Keywords : Clustering, K - Means, Forecasting.
Co-Authors Dony Novaliendry Yeka Hendriyani