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Journal of Computation Physics and Earth Science
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 27762521     DOI : https://doi.org/10.63581/JoCPES
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics
Journal of Computation Physics and Earth Science (JoCPES) publishes cutting-edge research in computational physics and earth sciences. It offers a platform for researchers to share insights on computational methods, physical sciences, environmental science, and more. Topics include computational physics, material science, meteorology, climatology, geophysics, scientific computing, numerical analysis, earth sciences and etc. JoCPES accepts original research articles. JoCPES welcomes original research in: Computational Physics Computational Methods Physical Sciences Material Science Meteorology Climatology Geophysics Scientific Computing Numerical Analysis Data Analysis Modeling and Simulation Earth Sciences Interdisciplinary Research Environmental Science Physics Applications Physics Data Science Internet Of Things Digital Signal Processing Computer Science Artificial Intelligence Machine Learning Deep Learning
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 73 Documents
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Multi-Hazard Risk Typology and Temporal Trends of Disaster Events in East Nusa Tenggara Province (2008–2025) Firman Almaliky Gapi Amra
Journal of Computation Physics and Earth Science (JoCPES) Vol. 6 No. 2 (2026): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v6i2.05

Abstract

East Nusa Tenggara (NTT) Province faces complex multi-hazard threats from hydrometeorological and geological disasters. This study developed a multi-hazard risk typology for 22 regencies/cities in NTT and examined temporal trends in disaster frequency over 2008–2025 (complete years; partial 2026 data excluded). The dataset comprised 1,158 records, adjusted to 1,128.5 real events using fractional weights across eight disaster types. Clustering was evaluated across three feature schemes and two algorithms (K-Means and Agglomerative Ward), with the number of clusters validated through a consensus of Silhouette, Davies-Bouldin, and Calinski-Harabasz indices while avoiding degenerate solutions. Temporal trends and change points were evaluated using the Mann-Kendall and Pettitt tests, preceded by lag-1 autocorrelation (ACF) screening. The selected hazard-only Agglomerative Ward model (k = 6; Silhouette = 0.2105, DBI = 1.205, CHI = 5.28) categorized regions into six distinct typologies, from low-risk to high-frequency flood/extreme-weather clusters, with Sabu Raijua identified as an impact-severity outlier. Auto correlation checks confirmed the validity of test assumptions. The Mann-Kendall and Pettitt tests consistently revealed no statistically significant monotonic trend or change point (p > 0.05) across the province, dominant disaster types, and all clusters. This meaningful null result demonstrates that disaster occurrences in NTT are driven by sharp inter-annual fluctuations rather than long-term trends, emphasizing the necessity of continuous preparedness and differentiated, typology-based mitigation strategies.
IoT-Based Instrumentation for Urban–Rural Thermal Comfort Assessment Using Temperature Humidity Index Muhammad Afif; Dibyo Susanto; Muchamad Rizqy Nugraha; Achmad Fahruddin Rais
Journal of Computation Physics and Earth Science (JoCPES) Vol. 6 No. 2 (2026): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v6i2.01

Abstract

Urbanization alters land surface characteristics and may contribute to the Urban Heat Island (UHI) effect, affecting outdoor thermal comfort. This study presents an Internet of Things (IoT)-based instrumentation system for microclimate monitoring and thermal comfort assessment using the Temperature-Humidity Index (THI). The system integrates calibrated DHT22 sensors, an ESP32 microcontroller, a 20×4 LCD, Firebase Realtime Database, and a web dashboard. Sensor calibration against BMKG reference instruments produced maximum deviations of −0.116°C for air temperature and −0.87% for relative humidity. The system was deployed simultaneously for 24 hours at an urban site in Kelurahan Tanah Tinggi and a rural site in Desa Cibodas, with 10-minute sampling intervals. Tanah Tinggi recorded a higher mean air temperature (29.05°C) than Cibodas (26.63°C), lower mean relative humidity (75.77% and 81.93%), and higher mean THI (27.59 and 25.57). Based on Wati and Fatkhuroyan (2017), the mean THI at Tanah Tinggi was classified as “Uncomfortable,” while Cibodas was “Partly Comfortable.” The cumulative distribution showed 58.33% “Uncomfortable” observations at Tanah Tinggi and 65.28% “Comfortable” observations at Cibodas. These results indicate an urban–rural thermal contrast consistent with an urban heat island pattern. However, interpretation is limited by the 24-hour period, two sites, and differences in elevation and local environmental conditions. The proposed system provides a low-cost approach for high-temporal-resolution microclimate monitoring and thermal comfort assessment.
INA-FOREWS: An IoT-Based Multi-Site Indonesia Forest Fire Early Warning System with Real-Time Risk Assessment and Alert Capabilities Tonny Wahyu Aji; Ahmad Meijlan Yasir; Putri Chairuniza
Journal of Computation Physics and Earth Science (JoCPES) Vol. 6 No. 2 (2026): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v6i2.03

Abstract

Despite the limitations of existing forest fire detection solutions, this study proposes the development of an IoT-based INA-FOREWS system to enhance real-time monitoring and risk assessment capabilities in Indonesia's tropical forests. The system aims to mitigate the impacts of forest fires and support progress towards the Sustainable Development Goals (SDGs) by integrating local sensor data with global spatial datasets. The system utilizes a rule-based approach to analyze environmental variables such as temperature, humidity, precipitation, and vegetation index, generating interactive fire risk maps to inform more effective response strategies. The hardware components, including sensors for temperature, humidity, gas, flame, and soil moisture, are deployed across multiple strategic locations and connected to a central ESP32 microcontroller for data transmission to a cloud-based platform. The web-based interface provides visualizations of sensor data, fire risk assessments, and emergency notification features to support decision-making by policymakers, local communities, and emergency responders. The strategic placement of sensors, guided by the rule-based risk assessment model, helps to optimize coverage and enhance the system's capacity for early fire detection. The proposed INA-FOREWS system serves as a model for integrating technological innovation with sustainability-focused objectives, contributing to improved forest fire management practices and supporting ecosystem preservation and community well-being in Indonesia's tropical regions.

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