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All Journal JPKMI (Jurnal Publikasi Kesehatan Masyarakat Indonesia) bit-Tech
Perdana, Muhammad Ricky
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Humanities Computer Science & IT Dentistry Environmental Science Neuroscience Nursing Public Health

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Impact of Air Pollutants (NO2 and CO) on the Incidence of ARI in Toddlers in Palangka Raya 2024: Retrospective Study Djinu, Lentar Adityas; Suhartono, Eko; Noor, Meitria Syahadatina; Syauqiah, Isna; Rahman, Fauzi; Perdana, Muhammad Ricky
Jurnal Publikasi Kesehatan Masyarakat Indonesia Vol 12, No 3 (2025): Jurnal Publikasi Kesehatan Masyarakat Indonesia
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jpkmi.v12i3.23667

Abstract

Palangka Raya, as the capital city of Central Kalimantan, has unique environmental characteristics because it often experiences forest and land fires (karhutla) which result in an increase in air pollutants. This study aims to analyze the association between exposure to Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) pollutants and the incidence of Acute Respiratory Tract Infection (ARI) in toddlers in Palangka Raya in 2024. This retrospective study was conducted in Palangka Raya City, with data on the incidence of ARI in toddlers obtained from the Palangka Raya City Health Office. Determination of NO2 and CO levels using the Google Earth Engine (GEE) platform that analyzes TROPOspheric Monitoring Instrument (TROPOMI) satellite data from the Sentinel-5 precursor. Geographic Information System (GIS) software was used to process spatial data and map layout. The relationship between CO and NO2 levels and ARI incidence was analyzed using linear regression tests, followed by multiple linear regression tests if there was a significant correlation. All statistical tests were conducted using a web-based platform. The conclusion of this study was that the linear regression test showed that NO2 levels only contributed 2.07% (p=0.6555) and CO contributed weakly (p=0.4981) to the incidence of ARI. This indicates that other factors are more dominant in causing ARI in Palangka Raya
Lightweight Model With Hyperparameter Optimization For Classification of Tomato Leaf Diseases Based On Plantvillage Fitriyandhi, Ari; Dwi Cahyani, Atika; Yunita, Risca; Perdana, Muhammad Ricky; Kristian, Taufik Aldri; Kusrini, Kusrini; Artha Agastya, I Made
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3566

Abstract

Tomato cultivation is a vital agricultural commodity in Indonesia, yet leaf diseases continue to pose a serious threat to crop quality and yield. While deep learning–based classifiers have achieved high accuracy in laboratory settings, most existing tomato leaf disease detection models rely on computationally intensive architectures that limit their practical deployment on resource-constrained devices commonly used in agricultural environments. To address this gap, this study proposes a lightweight Convolutional Neural Network (CNN) based on the MobileNetV2 architecture, explicitly combined with systematic hyperparameter optimization, for tomato leaf disease classification. Using 14,529 images from the PlantVillage dataset, the research involves image preprocessing, data augmentation, and structured tuning to improve performance while maintaining computational efficiency. The optimized model achieves an accuracy of 81% using a learning rate of 0.001, 128 units, a dropout rate of 0.3, and an alpha value of 0.35. Although this accuracy is slightly lower than that reported by heavyweight CNN models, it is competitive for lightweight architectures and represents a favorable trade-off between classification performance and computational efficiency. Despite its compact design, the model demonstrates reliable disease recognition and suitability for deployment on devices with limited resources. Furthermore, the trained model was implemented in a desktop-based application as a proof-of-concept system, demonstrating scalability and potential adaptation to mobile or edge-based agricultural decision-support platforms. This study highlights the novelty of integrating lightweight CNN design with systematic hyperparameter optimization and demonstrates that optimized lightweight deep learning models can provide effective, efficient, and deployable solutions for real-world precision agriculture applications.
Co-Authors Artha Agastya, I Made Dwi Cahyani, Atika Eko Suhartono Fauzi, Rahman Fauzi Fitriyandhi, Ari Isna Syauqiah Kristian, Taufik Aldri Kusrini, Kusrini Meitria Syahadatina Noor Yunita, Risca