Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Zero : Jurnal Sains, Matematika, dan Terapan PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Waris Marsisno
Department of Statistical Computing, Politeknik Statistika STIS, Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Oil Spill Detection and Verification in Nothern Bintan Brigitta Aurelia Putri Suhendi; Waris Marsisno
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.25942

Abstract

The Riau Islands Province, particularly northern Bintan Island, is strategically located near countries such as Singapore, making it a gateway to regional and international markets but also vulnerable to oil spills. This study aims to detect oil spill areas using GLCM texture analysis, adaptive thresholding, and various machine and deep learning models, followed by look-alike verification. The XGBoost model achieved the best performance with an accuracy of 0.9772, detecting oil spill areas of 5,400,241 m² and look-alike regions covering 1,333,045 m² on March 23, 2024. The findings also indicate that inland waters are often misidentified as spills, highlighting the importance of verification. This study is the first to integrate several of these methods for Sentinel-1 based oil spill detection in Bintan waters, as a new approach to accurate and efficient regional monitoring.
Detection and Mapping of Invasive Alien Plant Water Hyacinth using Satellite Imagery and Machine Learning (Case Study: Rawa Pening Lake, Indonesia) Adib Sulthon Muammal; waris marsisno
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.580

Abstract

Rawa Pening Lake, one of the 15 national priority lakes in Indonesia, faces a significant threat from invasive water hyacinth (Eichhornia crassipes). This plant once covered up to 70% of the lake's surface and continued to cause ecological and socio-economic impacts as of 2024, necessitating periodic monitoring to prevent future blooms. This study aimed to identify the optimal features to characterize water hyacinth, determine the most effective classification model, and map the plant’s distribution. Adopting the CRISP-DM framework, the study utilized Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery with multispectral band features, radar bands, and composite indexes. Feature selection was performed using Jenks Natural Breaks, and classification modeling was conducted using Random Forest and Convolutional Neural Network (CNN). The results demonstrated that the CNN achieved higher accuracy in distinguishing among land cover classes. The final mapping identified water hyacinth covering 34,775 pixels, 32,627 pixels, and 34,175 pixels in June, July, and August, respectively. This approach offers a reliable method for periodic monitoring of water hyacinths in Rawa Pening Lake.
Co-Authors Adib Sulthon Muammal Brigitta Aurelia Putri Suhendi