Skripsi

PREDIKSI KEPADATAN LALU LINTAS BERDASARKAN DATA ELECTRONIC TRAFFIC LAW ENFORCEMENT (ETLE) DIRLANTAS POLDA SUMSEL DAN MEDIA SOSIAL MENGGUNAKAN RANDOM FOREST MELALUI ELIMINATING SKEWED DISTRIBUTION

---

Traffic density is a complex issue in Palembang City that impacts time efficiency, fuel consumption, and environmental quality. This study aims to predict traffic density using Electronic Traffic Law Enforcement (ETLE) data as an objective source and Instagram social media data as a representation of public perception, applying the Random Forest method with an eliminating skewed distribution technique. The process includes data preprocessing, lexicon-based sentiment analysis, feature engineering, and data transformation. The results show that the ETLE-based model achieves 100% accuracy, while the Instagram-based model reaches 77.89% accuracy with varying precision, recall, and f1-score due to the subjective nature of the data. The comparison between both data sources shows a conformity level of 67.86%, indicating that public perception is fairly accurate in reflecting actual traffic conditions. These findings demonstrate that integrating ETLE and social media data provides a more comprehensive approach to traffic density prediction and supports data-driven decision making. Keywords: Traffic Density, ETLE, Instagram, Random Forest, Skewed Distribution, Sentiment Analysis.

---

Availability

Detail Information

Series Title

-

Call Number

T1968812026

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xv, 85 hlm.; ilus.; tab, 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.015 07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Sistem Komputer
Prediksi lalu lintas

Specific Detail Info

-

Statement of Responsibility

TUTI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment