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Journal : Media Statistika

KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART) Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid; Tarno Tarno
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.866 KB) | DOI: 10.14710/medstat.12.1.63-72

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.
ANALISIS DISKRIMINAN BERGANDA DENGAN PEUBAH BEBAS CAMPURAN KATEGORIK DAN KONTINU PADA KLASIFIKASI INDEKS PRESTASI KUMULATIF MAHASISWA Nur Walidaini; Moch. Abdul Mukid; Alan Prahutama; Agus Rusgiyono
MEDIA STATISTIKA Vol 10, No 2 (2017): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.333 KB) | DOI: 10.14710/medstat.10.2.71-83

Abstract

Multiple discriminant analysis is one of the discriminant analysis techniques where the dependent variable  are grouped into more than two groups. This paper discussed how to categorize Grade Point Average (GPA) of undergraduate student of Faculty of Sciences and Mathematics Diponegoro University based on categorical and continuous independent variable including gender, internet usage, time per week for learning, average score in national examination, amount of pocket money per month and the way to enter to Diponegoro University. The GPA grouping refers to the Academic Regulations of Diponegoro University i.e. satisfactory GPA (2,00 to 2,75), very satisfactory (2,76 to 3,50) and with honors (cum laude) (3,51 to 4,00). By using the multiple discriminant analysis with mixture variables, the accuration of classification based on training and testing data reach to 71,875% and 41,667% respectively.