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All Journal Jurnal Sistem Cerdas Jurnal E-Komtek PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Nugraha Priya Utama
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine Bagus Setyawan Wijaya; Rinaldi Munir; Nugraha Priya Utama
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.399

Abstract

In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.
A Machine Learning-Based Early Warning System for Electricity Outage Due to Extreme Weather Putra, Helmy Satria Martha; Alit Kesatria Mendala; Intan Jelita Saragih; Neng Ayu Herawati; Ayu Purwarianti; Nugraha Priya Utama
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025): (In Press)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2484

Abstract

Electricity is a critical resource that supports various sectors in Indonesia, especially during extreme weather. Outages have become serious for operational risks during extreme weather. This study proposes a machine learning-based early warning system to predict electricity outages caused by extreme weather. Historical weather and outage data were combined using spatial alignment. Key innovation of this study involved geospatial feature enrichment via HDBSCAN, Yeo-Johnson transformation, robust scaling, and class resampling using SMOTE, ADASYN, and SMOTE-ENN. Four ensemble classification models (Random Forest, XGBoost, AdaBoost, and LightGBM) were evaluated. LightGBM with SMOTE yielded the highest recall (0.99) and the fewest false negatives. These findings suggest a solution for a proactive early warning system risk mitigation in electricity under extreme weather conditions.
Analysis of Defense Mechanisms Against FGSM Adversarial Attacks on ResNet Deep Learning Models Using the CIFAR-10 Dataset Miranti Jatnika Riski; Krishna Aurelio Noviandri; Yoga Hanggara; Nugraha Priya Utama; Ayu Purwarianti
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.527

Abstract

Adversarial attacks threaten the reliability of deep learning models in image classification, requiring effective defense mechanisms. This study evaluates how defense distillation and adversarial training protect ResNet18 models trained on CIFAR-10 data against Fast Gradient Sign Method (FGSM) attacks. The baseline model achieves 85.01% accuracy on clean data but its accuracy falls to 19.23% when FGSM attacks at epsilon 0.3. The accuracy of defense distillation drops to 23.68% when epsilon reaches 0.3 but adversarial training maintains 0.34% accuracy at epsilon 0.25 although it reduces clean data accuracy to 57.08%. The analysis shows that classes with similar visual characteristics such as cats and dogs remain vulnerable to attacks. The study demonstrates the requirement for balanced defense approaches while indicating additional work needs to improve model robustness.
Co-Authors Alit Kesatria Mendala Ayu Purwarianti Bagus Setyawan Wijaya Intan Jelita Saragih Krishna Aurelio Noviandri Miranti Jatnika Riski Neng Ayu Herawati Putra, Helmy Satria Martha Rinaldi Munir Yoga Hanggara