Algoritma K-Means Untuk Pengelompokan Bantuan Langsung Tunai (BLT)
Abstract
The problem of poverty is a problem that often occurs in various regions in Indonesia, officials in West Dawuan Village continue to overcome these problems with various government assistance, one of which is Direct Cash Assistance (BLT). Often times in West Dawuan Village, there are obstacles in the distribution of the Direct Cash Aid funds received by parties who do not meet the criteria, there are residents who receive BLT successively and policy-making errors have a negative impact on the use of aid, especially in West Dawuan Village, Karawang. Using data mining clustering techniques, it is possible to group prospective beneficiaries according to the criteria to overcome these problems. This study uses the K-Means algorithm to classify BLT recipients in Dawuan Barat Village. The evaluation results from the resulting K-Means clustering model were then evaluated by using the SSE (Sum of Square Error) test. From the resulting K-Means clustering model, recommendations for prospective BLT recipients will be known according to the applicable criteria. With the help of the elbow method, the value of k=6 is obtained as the optimal k value and the results of the SSE cluster 6 test produce the best value among other clusters, which is 24551994078884.86. As a recommendation for BLT recipients, it can be done based on the best results from the SSE evaluation, namely cluster 6
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