Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Apriori dan FP-Growth Studi Kasus : Polrestabes Surabaya

Firdaus, M. Aldan Adiar (2025) Analisis Pola Kecelakaan Lalu Lintas Menggunakan Algoritma Apriori dan FP-Growth Studi Kasus : Polrestabes Surabaya. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Surabaya is one of the cities with a relatively high rate of traffic accidents. This study aims to identify accident patterns by applying the association rule mining method using the Apriori and FP-Growth algorithms. A preprocessing stage was first carried out to convert raw data into a format suitable for analysis. Subsequently, association rules were generated to uncover relationships between items or patterns in the dataset. Both algorithms were applied using specific minimum support and confidence values, and the results were evaluated. The findings indicate that the number of association rules is significantly influenced by the volume of data and the specified minimum support and confidence values. The higher these values, the fewer rules are generated, thereby limiting the scope of pattern analysis. When comparing different minimum support values, a support value of 10% showed that the Apriori algorithm was 0.0764 seconds faster than FP-Growth. With a support value of 30%, Apriori was 0.011 seconds faster than FP-Growth. This demonstrates that the Apriori algorithm has a shorter data processing time compared to FP-Growth. An analysis of association rules based on accident-related factors was also conducted, producing a variety of rules for each attribute. The results show that attributes with dominant item frequencies tend to have a stronger tendency to associate with other attributes. This study produced a web-based system using the Flask framework, which serves to visualize the association rules and support the evaluation of traffic safety policies. The results of this research are expected to serve as a reference for relevant stakeholders in enhancing monitoring efforts on roads with a high risk of accidents.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Kartika, Dhian Satria Yudha	NIDN0722058601	dhian.satria@upnjatim.ac.id
Thesis advisor	Najaf, Abdul Rezha Efrat	NIDN0029099403	rezha.efrat.sifo@upnjatim.ac.id

Subjects:

Q Science > QA Mathematics > QA75 Electronic computers. Computer science

Divisions:

Faculty of Computer Science > Departemen of Information Systems

Depositing User:

Aldan Adiar

Date Deposited:

10 Jul 2025 04:28

Last Modified:

10 Jul 2025 04:28

URI:

https://repository.upnjatim.ac.id/id/eprint/39275