CD Skripsi

Klasifikasi Angka Pencurian Di Riau Dengan Multivariate Adaptive Regression Splines (Mars) Dan Bootstrap Aggregating Mars

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ABSTRACT
One of the nonparametric regression methods that can be used for classification is
Multivariate Adaptive Regression Splines (MARS) which is enhanced using
bootstrap aggregating (bagging) with 50 replications. This method is applied to
conventional crime data, namely cases of theft which can be seen based on crime
rates in Riau Province in 2016-2020. The dependent variable used is the theft
crime rate, while the independent variables are population density (!!), poverty
rate (!"), RLS (!#), and PDRB (!$). This study aims to form the best model and
see the results of the classification based on the factors that influence the crime
rate indicators in Riau Province. Bagging MARS method with training data of
68% produces a minimum GCV value is 0.08961, while the MARS method is
0.13993 in obtaining the best model. The MARS method yields 60% for accuracy,
80% for sensitivity and specificity 40%. The best accuracy value is 85% with
sensitivity is 100% and specificity is 70% using bagging MARS with testing data
by 32%. The most influential variable using the MARS method and bagging
MARS on the crime rate indicator of theft cases in Riau Province in 2016-2020
are the poverty rate (!") variable with an importance level of 100%.
Keywords: Crime rate indicator of theft cases, classification, multivariate
adaptive regression splines, bootstrap aggregating.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1803124092

Penerbit

Pekanbaru : Universitas Riau - FMIPA - STATISTIKA., 2022

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1803124092

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

Deti

Versi lain/terkait

Lampiran Berkas

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