Perpetrators profiling based on victims atributes using logistic regression model / Siti Nurfatihah Mohamad Azman, Nor Emiliya Fatin Amberi and Nur Farah Amira Mustappa Kamal

Mohamad Azman, Siti Nurfatihah and Amberi, Nor Emiliya Fatin and Mustappa Kamal, Nur Farah Amira (2019) Perpetrators profiling based on victims atributes using logistic regression model / Siti Nurfatihah Mohamad Azman, Nor Emiliya Fatin Amberi and Nur Farah Amira Mustappa Kamal. [Student Project] (Unpublished)

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Abstract

Nowadays people always read about criminal such as raping, killing, robbery and others
especially among people who are have relationship with offender. Murder or rape is
one of common criminal that people often heard in news because these numbers of
cases are increase every day. In order to solve the crime, forensic department and police
will cope together. Additionally, there is a lack of research about victims and offender
relationship when it comes to solving the crime and not all mathematical method can
give the absolute answer to calculate the perpetrators profiling. The objectives of this
research are to calculate the probability ofrelationship between victims and offender of
the murder cases using Logistic Regression Model and to the determined whether the
model is suitable to be used to analyze the data. In this research, there are two types of
logistic regression that are use which are binomial and multinomial. For the binomial,
there are two categories of dependent variable only and four categories of dependent
variables for multinomial regression. The result from shows that the R 2 the model is
less than 60 percent. Even though the R2 is not significant but there are a few tests that
are significant such as Omnibus and Wald test. Therefore, this method is still valid to
use to analyse the data set. For the recommendation data can be analyze by using other
model such as Association Rule Mining (ARM) because ARM is one of the models that
can show the relationship between data items and large data set in various type of
database.

Metadata

Item Type: Student Project
Creators:
Creators
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Mohamad Azman, Siti Nurfatihah
UNSPECIFIED
Amberi, Nor Emiliya Fatin
UNSPECIFIED
Mustappa Kamal, Nur Farah Amira
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Study and teaching
Q Science > QA Mathematics > Mathematical statistics. Probabilities
Q Science > QA Mathematics > Analysis > Analytical methods used in the solution of physical problems
Divisions: Universiti Teknologi MARA, Negeri Sembilan > Seremban Campus > Faculty of Computer and Mathematical Sciences
Programme: Bachelor of Science (Hons.) (Management Mathematics)
Item ID: 37322
Uncontrolled Keywords: Perpetrators profiling based, victims atributes, logistic regression model
URI: https://ir.uitm.edu.my/id/eprint/37322

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