Melola Discovery

← Melola Discovery

Jurnal

SEMIPARAMETRIK MULTILEVEL ZERO-INFLATED GENERALIZED POISSON REGRESSION MODELING ON TRAFFIC ACCIDENT DATA IN TEMANGGUNG REGENCY

Unnes Journal of Mathematics · Vol. 9 No. 2 (2020) · hlm. 26–33

DOI
10.15294/ujm.v9i2.34761
ISSN
2460-5859, 2252-6943
Terbit
2020-12-30
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study aims to model the data of traffic accidents in Temanggung Regency with a multilevel zero-inflated generalized poisson semiparametric regression model. Multilevel zero-inflated generalized poisson semiparametric regression is a regression model for analyzing poisson distribution data with stratified data structures that are overdispersed and there are parametric and nonparametric components in the independent variable. This study uses the variable of many accidents as the response variable, as well as the variable of many traffic light violations, many violations of drivers not having a SIM, many accidents because the vehicle is not fit, many accidents due to damaged roads as the independent variable. The method used to estimate the model parameters is the Maximum Likelihood Ratio (MLE) method with the Maximization Expectation (EM) algorithm. After estimating the parameters and the suitability of the test model with the Wald Test, then the model shape is obtained a semiparametric regression multilevel zero inflated generaized poison with AIC count model 144.0032 and AIC zero-inflation model -63.0016.

Jaringan kolaborasi para penulis

40 penulis · 105 tautan kepenulisan bersama · 4 lintas organisasi · menampilkan 40 yang paling banyak terhubung

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • State University of Semarang
  • International Islamic University Malaysia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Arief AgoestantoAfiliasi tidak diketahui3
Sunarmi SunarmiAfiliasi tidak diketahui2
Mulyono MulyonoAfiliasi tidak diketahui2
Rochmad RochmadInternational Islamic University Malaysia1
Alfany Rahman YuliantoAfiliasi tidak diketahui1
Nuriana Rachmani DewiAfiliasi tidak diketahui1
Fitri RetnowatiAfiliasi tidak diketahui1
Y L SukestiyarnoAfiliasi tidak diketahui1
Nur Laesiyah FitriyantiAfiliasi tidak diketahui1
Hetty OktaviyantyAfiliasi tidak diketahui1
Niken Yulia AstutiAfiliasi tidak diketahui1
Anita AnggraeniAfiliasi tidak diketahui1
Putriaji HendikawatiState University of Semarang1
Laeli Sidik FebriantoAfiliasi tidak diketahui1
Ardhyani Yunika DewiAfiliasi tidak diketahui1
Johan Dwi ArdamaAfiliasi tidak diketahui1
Yustika Rakhma AprilianiAfiliasi tidak diketahui1
Amidi AmidiState University of Semarang1
Maria Metha Dwi PebriyantiState University of Semarang1
Solehuzain SolehuzainAfiliasi tidak diketahui1
Isnarto IsnartoState University of Semarang1
Ari Limay Trisno PutraAfiliasi tidak diketahui1
Hardi SuyitnoAfiliasi tidak diketahui1
Wdyanita Indah ProbosiwiAfiliasi tidak diketahui1
Wardono WardonoAfiliasi tidak diketahui1
Emiliana Elsa JerauAfiliasi tidak diketahui1
Masrukan MasrukanState University of Semarang1
Muhammad Fakqih ZubaediAfiliasi tidak diketahui1
Mohammad AsikinState University of Semarang1
Jody Setya HermawanAfiliasi tidak diketahui1
Sri HandayaniState University of Semarang1
Suryaning Fajar SariAfiliasi tidak diketahui1
Siti AminahAfiliasi tidak diketahui1
Kartono KartonoState University of Semarang1
Uzlifa Khanifatul MuttaqiAfiliasi tidak diketahui1
YL SukestiyarnoState University of Semarang1
Junarti JunartiAfiliasi tidak diketahui1
Lilik SulistyoAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">SEMIPARAMETRIK MULTILEVEL ZERO-INFLATED GENERALIZED POISSON REGRESSION MODELING ON TRAFFIC ACCIDENT DATA IN TEMANGGUNG REGENCY</dc:title>
	<dc:creator>Isa, Bani Muhamad</dc:creator>
	<dc:creator>Dwidayati, Nur Karomah</dc:creator>
	<dc:description xml:lang="en-US">This study aims to model the data of traffic accidents in Temanggung Regency with a multilevel zero-inflated generalized poisson semiparametric regression model. Multilevel zero-inflated generalized poisson semiparametric regression is a regression model for analyzing poisson distribution data with stratified data structures that are overdispersed and there are parametric and nonparametric components in the independent variable. This study uses the variable of many accidents as the response variable, as well as the variable of many traffic light violations, many violations of drivers not having a SIM, many accidents because the vehicle is not fit, many accidents due to damaged roads as the independent variable. The method used to estimate the model parameters is the Maximum Likelihood Ratio (MLE) method with the Maximization Expectation (EM) algorithm. After estimating the parameters and the suitability of the test model with the Wald Test, then the model shape is obtained a semiparametric regression multilevel zero inflated generaized poison  &amp;nbsp;with AIC count model 144.0032 and AIC zero-inflation model -63.0016.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-12-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/ujm/article/view/34761</dc:identifier>
	<dc:identifier>10.15294/ujm.v9i2.34761</dc:identifier>
	<dc:source xml:lang="en-US">Unnes Journal of Mathematics; Vol 9 No 2 (2020); 26-33</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v9i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/ujm/article/view/34761/17893</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Unnes Journal of Mathematics</dc:rights>
</oai_dc:dc>