Klasifikasi Jenis Penyandang Masalah Kesejahteraan Sosial Menggunakan Naïve Bayes

Nurhaeni Nurhaeni
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Abstract

The main need in formulating policies, programs, and social welfare development activities is the data of Persons with Social Welfare Problems, known as PMKS. Every person in a very poor household has more than one category of PMKS type. This will have an impact on the type of assistance that deserves, while each individual can only be categorized into one type of PMKS. Thus, each person will only receive one type of assistance. The type of PMKS with family approval is determined based on 12 criteria namely age, gender, marital status, employment, education, adequate breastfeeding, immunization, healthy food, medication, disability conditions, single parents, and attention from the family. In an effort to optimize variations in the types of PMKS, the Naïve Bayes method is used. Based on research conducted, the completion rate from the use of the naïve bayes method was 83.87%. Thus it can be announced that the Naïve Bayes classification method can be used for the classification of PMKS types.
社会福利问题观测者类型的Naéve Bayes分类
制定政策、计划和社会福利发展活动的主要需要是有社会福利问题的人的数据,即PMKS。在一个非常贫穷的家庭中,每个人都有不止一类PMKS类型。这将对应该得到的援助类型产生影响,而每个人只能被归类为一种类型的PMKS。因此,每个人只能得到一种援助。经家庭批准的PMKS类型是根据12个标准确定的,即年龄、性别、婚姻状况、就业、教育、充分母乳喂养、免疫接种、健康食品、药物、残疾状况、单亲和家庭关注。为了优化PMKS类型的变化,使用了朴素贝叶斯方法。根据所进行的研究,使用朴素贝叶斯方法的完成率为83.87%。因此可以宣布,朴素贝叶斯分类方法可以用于PMKS类型的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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