THE MODELING OF "MUSTAHIQ" DATA USING K-MEANS CLUSTERING ALGORITHM AND BIG DATA ANALYSIS (CASE STUDY: LAZ)

There are a lot of Mustahiq data in LAZ (Lembaga Amil Zakat) which is spread many locations today. Each has that different type from other LAZ. differences types so large cannot be used together even though the purpose same to determine data. And find out whether still up date (renewable), course it will very difficult due not uniform or different, long time span, and amount To give zakat certainly requires speed information. So, giving Mustahiq, monitor progress Mustahiq. It possible change his condition become Muzaki. This reason for researcher take this theme order help existing make easier cluster Furthermore, already can by managers develop organization. also reference determining recipient those who entitled later. The research "Modeling using K-Means Algorithm Big Data analysis ". We got with random sample online offline survey. Online survey Google form Offline we BAZNAS (National Zakat Agency) Indonesia another agency (LAZ) Jakarta. conducted combining analyzed Algorithm. algorithm an n objects based on attributes into k partitions according criteria determined diverse focuses modeling applies Algorithms Analysis. first made tools grouping simulation test do several experimental scenarios model mapping developed best processing results study displayed tabular graphical form, namely proposed at Agency (LAZ). result total 1109 correspondents, 300 correspondents included 809 Non-Mustahiq have accuracy rate 83.40%. That means system able Result filtering Gender “Male” 83.93%, Age ”30-39” 71,03%, Job “PNS” 83.39%, Education “S1” 83.79%. advantaged expected quickly person meets as mustahik Muzaki (Amil Agency). clustering application program if UIN Syarif Hidayatullah Jakarta want too.