Moch. Abdul Mukid
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

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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. 
THE ANALYSIS OF SOCIO-ECONOMIC EFFECT ON CRIMINALITY IN INDONESIA USING FUZZY CLUSTERWISE REGRESSION MODEL Azzarah, Dian Fatimah; Mukid, Moch. Abdul; I Maruddani, Di Asih; Rochayani, Masithoh Yessi
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.221-232

Abstract

Crime in Indonesia has shown a fluctuating trend and has increased significantly in recent years, with striking variations in crime rates between provinces. This phenomenon raises questions about the role of socio-economic factors such as education, poverty, and unemployment in influencing crime rates. Although there have been many studies examining the relationship between these variables and crime, the approaches used often assume that the relationship between variables is homogeneous across regions. In fact, heterogeneity in characteristics between provinces can cause different relationships. Therefore, an analysis approach is needed that can accommodate this diversity. This study proposes the Fuzzy Clusterwise Regression method which not only improves model accuracy compared to classical linear regression (with an increase in the coefficient of determination from 65.72% to more than 90%), but is also able to identify different patterns of relationships between regional groups (clusters). The results from FCR showed that the effect of socio-economic factors on crime varies between clusters and the optimum number of clusters is 4. In cluster 1, cluster 2, and cluster 3 all the variables had a significant influence on the amount of crime. Meanwhile, in cluster 4, the population poverty variable has no significant effect on the crime rate.