KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART)

*Dwi Ispriyanti -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Alan Prahutama -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Mustafid Mustafid -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tarno Tarno -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Received: 17 Jan 2019; Published: 24 Jul 2019.
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Abstract

Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.

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