Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal INTI Nusa Mandiri
Marzuki Sinambela
College of Meteorology, Climatology, and Geophysics
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
SISTEM DETEKSI BANJIR DI KABUPATEN BEKASI BERBASIS DATA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE Reza Okta Pratama; Nardi; Anton Widodo; Marzuki Sinambela
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8642

Abstract

Floods are the most common hydrometeorological disasters in Indonesia, and Bekasi Regency is among the areas with a high level of vulnerability. This study aims to develop a Twitter-based flood detection system using Support Vector Machines (SVM), with Logistic Regression (LR) and Random Forest (RF) as comparison models. A total of 4,436 tweets from the 2020–2024 period were collected using Tweet Harvest, manually labeled, and then processed through preprocessing, TF-IDF feature extraction, and data splitting using group-based random splitting (80:20). Evaluation was conducted using 10-fold GroupKFold cross-validation with recall as the primary metric, followed by hyperparameter tuning using GridSearchCV. The evaluation results showed that SVM performed best compared to LR and RF. The tuned SVM model achieved a test recall of 0.8673, exceeding the minimum threshold of 0.80. The model was then integrated into a web-based monitoring dashboard that displays interactive maps, statistics, and temporal trends. Black-box testing across nine scenarios achieved a 100% success rate, while a user experience evaluation using the UEQ-S on 18 respondents yielded an overall score of 1.97, categorized as “Very Good.” The research results indicate that the developed system is capable of effectively supporting social media-based flood monitoring.
Co-Authors Anton Widodo Nardi Reza Okta Pratama