Analysis of the Naïve Bayes Method for Determining Social Assistance Eligibility Public
DOI:
10.33395/sinkron.v8i2.12259Keywords:
Confusion Matrix, Data Mining, Naïve Bayes, Orange, Roc Analysis, Social AssistanceAbstract
Economic needs are community needs that are used to meet daily
needs. Therefore, economic needs are very important for the life of every
society. There is a gap in the economic needs of the community, the
government created a social assistance program which is assistance provided
to the community in the form of cash or non-cash. The help is made for
welfare society from inequality, especially economic inequality. So
researchers will carry out a data classification of people who are eligible for
social assistance. The classification will be carried out using the Naïve Bayes
method. The Naïve Bayes method is a simple classification method for
calculating the probability of a combination of certain data. The data to be
used by researchers is community data as much as 62 community data.
research done by using the Naïve Bayes method aims to classify community
data that is feasible to forget social assistance. The first stage of this
classification is the process of collecting community data and determining
community data that will be used as a filtered sample cleaned, furthermore
preprocessing data and then designing the Naïve Bayes Algorithm model.
The results of data classification using the Naïve Bayes method show that the
number of people who are eligible for social assistance is 14 community data
and people who are not eligible for social assistance are 48 community data.
These results can be a reference for determining the eligibility of the
community to receive social assistance.
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Copyright (c) 2023 Adinda Pratiwi Siregar, Deci Irmayani, Mila Nirmala Sari
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