Application of the K-Means Method for Clustering Land and Building Tax Payments Based on Tax Types (Case Study: BPKPAD Binjai City)

Riski Ramadhansyah; Akim Manaor Hara Pardede; Anton Sihombing

doi:10.55227/ijhet.v1i3.56

Authors

Riski Ramadhansyah Teknik Informatika, STMIK Kaputama Binjai
Akim Manaor Hara Pardede Teknik Informatika, STMIK Kaputama Binjai
Anton Sihombing Teknik Informatika, STMIK Kaputama Binjai

DOI:

https://doi.org/10.55227/ijhet.v1i3.56

Keywords:

Clustering, Data Mining, K-Means, PBB

Abstract

Land and Building Tax or abbreviated as PBB is a fee that must be paid for the existence of land and buildings owned by the community or residents. The determination of PBB in Binjai City is based on the application of the Land Value Zone (ZNT) which is close to the market price, which will be able to create equitable development throughout Binjai City. BPKPAD (Regional Revenue and Assets Financial Management Agency) Binjai City is a government agency that receives PBB payments from the community. Data - data on PBB payments for the people of Binjai City have been stored in an existing system and every year it will continue to increase so that it will cause data accumulation in the land and building tax archives. A data processing system is needed to manage these data, one of which can be done with data mining which can process piles of data into useful information and can be utilized by grouping PBB data based on criteria. Clustering is a method in data mining that can be used to automatically detect clusters of adjacent records that have a certain definition in all variables. K-Means algorithm is a simple algorithm to classify or group a large number of objects with certain attributes into groups (clusters). So that this system can be used as input for the Binjai City BPKPAD in finding solutions to increase regional income from PBB payments.

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