Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Nurjannah Nurjannah; Hendra  Cipta; Rima  Aprilia

doi:10.20956/j.v20i2.32069

Authors

Nurjannah Nurjannah Universitas Islam Negeri Sumatera Utara
Hendra Cipta University Islam Negeri Sumatera Utara
Rima Aprilia University Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.20956/j.v20i2.32069

Keywords:

PKH, Classification, Weighted Naive Bayes, Laplace Smoothing

Abstract

The Indonesian government sometimes faces difficulties in dealing with poverty problems. The Indonesian government utilizes a number of programs and stimulants to overcome the problem of poverty. The government's PKH program offers conditional assistance to low-income families who have been designated as PKH recipient households. PKH provision is still below optimal standards, this may be because the data used is not updated frequently. To assist village officials in determining which residents are eligible to receive PKH assistance, this research tries to classify the eligibility of recipient residents in Cinta Rakyat Village. With the Weighted Naive Bayes method, classification calculations are not only based on probability distributions but also by adding weights to each attribute to the class. Assisted with Laplace Smoothing to avoid a probability value of 0. As a result, there are eight factors that determine a person's eligibility to receive PKH assistance, including age, occupation, income, number of family members, number of dependent school children, quality of house, type of floor, and type of walls. As well as classification into eligible and non-eligible groups. And obtained test results using the Confusion Matrix with an accuracy value of 95.65%, error rate of 4.34%, sensitivity of 100% and specificity of 94.74%. To identify village communities who deserve PKH assistance, Cinta Rakyat Village administrators can use the findings of this research.

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