KLASIFIKASI TINGKAT KESEJAHTERAAN KELUARGA DI KECAMATAN SIDEMEN MENGGUNAKAN BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL

Gusti Ngurah, Sentana Putra, I. Putu, Winada Gautama
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Abstract

This research was conducted to determine the variables that have a significant impact on the stages of a well-off family in Sidemen Sub-district based on indicators obtained from the BKKBN and to classify the stages of a well-off family. This study used secondary data obtained from the stage of well-being data, Sidemen Sub-district, Karangasem Regency from BKKBN, totaling 1796 families. The method used is ordinal logistic regression and bagging ordinal logistic regression. Based on the logit regression model of ordinal logistic regression and ordinal logistic regression bagging, there are fourteen variables that have a significant effect on the dependent variable, namely marital status, type of insurance, age of head of household, occupation of head of household, having a source of income, eating a variety of food, having savings, accessing information from online media, families have ever recreated together, families have ever participated in social/community activities, the largest type of floor, main source of drinking water, ownership of a house/building, and children are still in school. The classification accuracy level in testing data using the ordinal logistic regression method was 79.4%, while the classification accuracy level using the bagging ordinal logistic regression method with 50,000 replications was 82.78%, so bagging showed an increase in classification by 3.38%..
本研究基于BKKBN获得的指标,确定对四门街道小康家庭阶段有显著影响的变量,并对小康家庭阶段进行分类。本研究使用的二手数据来自BKKBN幸福感数据阶段,来自Karangasem Regency sidmen街道,共计1796个家庭。使用的方法是有序逻辑回归和bagging有序逻辑回归。基于有序逻辑回归和有序逻辑回归套袋的logit回归模型,有14个变量对因变量有显著影响,分别是婚姻状况、保险类型、户主年龄、户主职业、是否有收入来源、是否吃各种食物、是否有储蓄、是否从网络媒体获取信息、是否与家人一起再创造、家庭是否参加过社会/社区活动,最大的楼层类型,饮用水的主要来源,房屋/建筑物的所有权,儿童是否还在上学。采用有序逻辑回归方法对试验数据的分类精度水平为79.4%,而套袋5万次重复的有序逻辑回归方法对试验数据的分类精度水平为82.78%,套袋的分类精度提高了3.38%。
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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