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All Journal Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Parameter: Jurnal Matematika, Statistika dan Terapannya
Hutapea, Risca Octaviani
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Mathematics Physics

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ANALISIS REGRESI LOGISTIK BINER TERHADAP FAKTOR-FAKTOR RISIKO KECELAKAAN LALU LINTAS DI INDONESIA Amalia, Seila; Putri, Amelia; Sianturi, Michael Dolly; Hutapea, Risca Octaviani; Ndruru, Albert Servant; Arnita
Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 5 No. 2 (2024): Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3483/trigonometri.v5i2.9021

Abstract

Penelitian ini menganalisis faktor-faktor yang memengaruhi tingkat keparahan kecelakaan lalu lintas di Indonesia menggunakan regresi logistik biner. Data dari Kaggle mencakup variabel usia, jenis kelamin, pengalaman berkendara, pencahayaan, dan cuaca. Hasil menunjukkan bahwa pencahayaan buruk dan cuaca buruk signifikan meningkatkan peluang kecelakaan fatal masing-masing sebesar 88,8% dan 96,5%. Model regresi memiliki akurasi 76% dengan kecocokan data yang baik (p-value Hosmer-Lemeshow = 0,144). Temuan ini memberikan wawasan penting untuk kebijakan keselamatan dan pengembangan infrastruktur.
BINARY LOGISTIC REGRESSION ANALYSIS OF TRAFFIC ACCIDENT RISK FACTORS IN INDONESIA Amalia, Seila; Putri, Amelia; Sianturi, Micahel Dolly; Hutapea, Risca Octaviani; Ndruru, Albert Servant; Arnita, Arnita
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp63-72

Abstract

This study analyzes the factors influencing the severity of traffic accidents in Indonesia using binary logistic regression. Data from Kaggle includes variables such as age, gender, driving experience, lighting conditions, and weather conditions. The results indicate that poor lighting and adverse weather significantly increase the likelihood of fatal accidents by 88.8% and 96.5%, respectively. The logistic regression model achieves 76% accuracy with a good data fit (Hosmer-Lemeshow p-value = 0.144). These findings provide valuable insights for traffic safety policies and infrastructure development.
Co-Authors Amalia, Seila Arnita Arnita Arnita Ndruru, Albert Servant Putri, Amelia Sianturi, Micahel Dolly Sianturi, Michael Dolly