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Application of Himawari-9 and Radiosonde Data in Analyzing Extreme Rainfall Events (Case Study: Malang, November 25, 2023) Rini Arista; Muhammad Alvin Faiz
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): 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/sf6vx516

This study uses Himawari-9 meteorological satellite data and radiosonde data to examine the severe rain event that happened in Malang on November 25, 2023. The Japan Meteorological Agency's (JMA) Himawari-9 satellite collects high-frequency atmospheric data, and radiosonde data provide vertical atmospheric information. This study determines the distribution of major convective clouds and meteorological characteristics that suggest the possibility of severe weather by analyzing satellite photos using the RGB technique and radiosonde data. Convective clouds identified by satellite images at 07:40 UTC started to blanket the Malang City area and spread until they filled the entire East Java region at 09:00 UTC, according to the analysis's findings. Weather metrics including the Showalter Index (SI), Lifted Index (LI), and Convective Available Potential Energy (CAPE) are displayed in radiosonde data to support the possibility of heavy rain. There is a significant chance that flooding in Malang will result from heavy rains due to these unstable atmospheric conditions.

IoT-Based Seismic Sensor Network Design for Early Warning System in Kalimantan : Literature Review Ilham Muthahhari; Muhammad Dzakwan Firdaus
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): 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.v4i2.02

Kalimantan is not commonly associated with significant seismic activity due to its relative distance from major tectonic plate boundaries; however, it remains vulnerable to earthquakes that pose risks to human safety and the integrity of infrastructure. A recent seismic incident in the region has raised alarms about the adequacy of current preparedness and mitigation measures. This review seeks to establish a robust early warning system (EWS) for earthquakes by incorporating seismograph technology and IoT-based sensor networks tailored for Kalimantan. Despite its traditional classification as a low-seismic area, the region is susceptible to risks stemming from nearby active faults and tectonic dynamics. By analyzing recent research, this paper highlights the distinct geographical and environmental factors that must be considered when implementing a seismic sensor network in Kalimantan. It also examines the critical elements of seismographic devices for earthquake detection and discusses the role of IoT in enhancing real-time monitoring and early warning capabilities. The proposed IoT-based EWS utilizes affordable, distributed sensors to improve response times and detection precision, thereby providing timely notifications to vulnerable areas. This strategy presents a scalable and economically viable model for regions at risk of earthquakes, emphasizing the significance of both sophisticated instrumentation and accessible IoT technology for communities.

Unveiling Seismic Patterns in Kalimantan: Insights into Earthquake Events Over the Last Two Decades (2000-2024) Eva Darnila; Ilham Muthahhari; R. Grata Sabdo Yudhopratidino
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): 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.v4i2.03

Seismic activity in Kalimantan, once considered to be relatively minimal, has garnered increased scrutiny due to the presence of active fault lines, including the Mangkalihat, Meratus, and Tarakan faults. This research examines earthquake occurrences in Kalimantan from 2000 to 2024, utilizing seismic data from the USGS and analytical tools such as QGIS and Microsoft Excel. The findings reveal that earthquake occurrences are predominantly located in the northeastern and southeastern parts of the region, with magnitudes varying between 3.9 and 6.1. Notably, the year 2015 experienced a marked increase in seismic events. The results emphasize the critical need for disaster preparedness, the resilience of infrastructure, and the establishment of Early Warning Systems (EWS) to alleviate potential hazards. This study advocates for ongoing monitoring and enhanced public awareness to diminish seismic vulnerability in Kalimantan.

A Literature Review of Low-Cost Accelerometer Sensors for Earthquake Detection: Performance Analysis and Accuracy Assessment Muhammad Rafi Athallah Disastra; Adhe Abdurrafi
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): 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.v4i2.04

This literature review synthesizes findings from 20 studies that explore earthquake detection systems using low-cost accelerometer-based sensors integrated with microcontrollers, such as Arduino, and other IoT technologies. The comparative analysis focuses on sensor selection, sensitivity, noise levels, and system efficacy across various implementations. The ADXL355, LIS3DHH, MPU6050, and ADXL345 accelerometers emerged as commonly tested sensors, each demonstrating unique strengths in seismic activity monitoring. Studies highlight the ADXL355 and LIS3DHH for their low noise and high sensitivity, making them preferred for detecting subtle ground movements, while the MPU6050’s six-axis functionality offers versatility in multi-dimensional motion analysis. Additionally, research underscores the importance of accurate calibration and noise mitigation techniques to enhance data reliability. The review concludes that low-cost accelerometers, particularly when combined with IoT frameworks, provide feasible solutions for scalable earthquake early warning systems. However, challenges persist in balancing sensitivity and stability in noisy environments, indicating a need for further refinement in sensor technology and signal processing algorithms to improve detection accuracy and reduce false alarms in real-world applications.

Implementation of LVGL and LovyanGFX into a Portable Datalogger Embedded System Daffa Naufal Adhira Putra Safriadi
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): 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.v4i2.05

Integrating a Graphic User Interface (GUI) into an embedded system remains difficult due to hardware limitations and the complexity of graphics programming. This study examines the implementation of LVGL (Light and Versatile Graphics Library) and LovyanGFX to create an efficient and user-friendly interface for portable data logging systems developed with the ESP32-S3 microcontroller. The system is intended to accumulate, process and display environmental data such as the MQ-7 carbon monoxide sensor, DHT21 temperature and moisture sensor, and BME280 temperature and humidity sensor. A structured experimental approach was adopted to evaluate the feasibility and performance of the proposed system. The ESP32-S3 was chosen for its superior memory capacity and wireless communication capabilities, while the ILI9488 TFT screen was selected for real-time data visualization. The research focused on optimizing the GUI for responsiveness, data readability, and user interaction. The results appear that LVGL and LovyanGFX work well together to make an intelligently and valuable GUI that can appear real-time sensor information. This also indicate that LVGL and LovyanGFX effectively render graphical elements, enabling smooth transitions and efficient data representation. Furthermore, the system successfully integrates sensor data, demonstrating its potential for real-time monitoring applications. This study contributes to the development of embedded GUI systems by demonstrating a cost-effective approach to graphical interface design in dataloggers. Future research can explore expanding the system’s functionality, optimizing SPI communication, and enhancing graphical rendering capabilities.

Atmospheric Dynamics Analysis of Extreme Rain Events Using Radiosonde Observation Method (Case Study of Extreme Rain for The Period Of 21-31 March 2024 in Probolinggo (Paiton), East Java Zaky Aidhil Azzikry; Eva Darnila
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 1 (2024): 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.v4i1.04

Rain extreme is one of phenomenon weather extreme that can cause disaster like flood and land landslide. Understanding about dynamics the atmosphere that causes the occurrence Rain extremes are very important to predict and anticipate possibility the occurrence disaster This study aims to analyze dynamics the atmosphere that causes incident Rain extreme in Probolinggo (Paiton), East Java in the period 21-31 March 2024 using method radiosonde observations. Research methods used covered rainfall data collection Rain daily, radiosonde data (temperature, humidity, wind), and real data from the numerical model global weather / climate. Data analysis was carried out using method statistics, visualization of skew-T log-P diagrams, analysis pattern wind, distribution humidity, convergence / divergence, and analysis dynamics atmosphere use equality movement and continuity. Expected results from This research is better understanding deep about dynamics the atmosphere that causes Rain extremes in the study area, such as pattern circulation wind, source water vapor, lifting processes, and mechanisms formation Rain extreme. This research can also give contribution in development system warning early and mitigation disaster related Rain extreme in the study area and other areas with similar characteristics.

Model of Lightning Strike Risk to Humans Based on Spatial Analysis and Environmental Factors Ahmad Andru Alfandhi Amarsin; Ahmad Meijlan Yasir
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 1 (2023): 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.v3i1.05

Lightning strikes pose significant threats to human safety and infrastructure, particularly in tropical regions like Indonesia with high lightning activity. This study aims to develop a predictive model of lightning strike risk to humans based on spatial analysis and environmental factors, utilizing data on lightning distribution, land use, population density, and meteorological parameters. Using probabilistic decision trees and tropical lightning formulas, the model identifies key predictors, including rainfall, land use patterns, and humidity, which influence lightning density. The results reveal that densely populated areas with high lightning activity, such as parts of Java and Sumatra, are particularly vulnerable. Spatial risk maps generated from the model highlight high-risk zones, providing critical insights for disaster mitigation planning and infrastructure protection. Furthermore, the study emphasizes the significant correlation between lightning density, land use, and population exposure, offering a comprehensive framework for understanding lightning risks. This predictive model not only serves as a tool for early warning systems and sustainable spatial planning but also underscores the importance of integrating environmental and spatial data for effective lightning risk mitigation. Future research should incorporate temporal lightning variations and field validation to refine the model and enhance its applicability.

Air Quality Prediction System Using Telegram Bot Based on Real-Time Data Akmaludien Ramadhan
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 1 (2023): 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.v3i1.04

Air quality is a crucial aspect that affects public health and the environment. As public awareness of the importance of air quality increases, fast and accurate information about air conditions becomes essential. This research developed a Telegram bot-based system that not only provides current air quality information but also predicts air quality for the next five days. The system uses real-time data from the OpenWeatherMap API and employs a regression-based prediction model to provide more accurate air quality projections. This bot is designed to provide easy access to information for people, especially in Indonesia, regarding air quality in various cities. The results show that the system has a high reliability level with a 98.5% success rate and 99.9% uptime. The prediction model using Linear Regression shows good performance with an R-squared (R²) value of 0.86, Mean Absolute Error (MAE) of 0.24, and Root Mean Square Error (RMSE) of 0.31. The system also demonstrates optimal response time with an average of 0.83 seconds per request. User evaluation shows a satisfaction level of 4.2/5, ease of use of 4.5/5, and feature completeness of 4.0/5.

Literature Review: Development of a Machine Learning-Based Early Warning System for Land and Forest Fires with IoT and Automated Action Recommendations Rini Arista; Daniela Adolfina Ndaumanu
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 1 (2023): 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.v3i1.03

Lightning strikes pose significant threats to human safety and infrastructure, particularly in tropical regions like Indonesia with high lightning activity. This study aims to develop a predictive model of lightning strike risk to humans based on spatial analysis and environmental factors, utilizing data on lightning distribution, land use, population density, and meteorological parameters. Using probabilistic decision trees and tropical lightning formulas, the model identifies key predictors, including rainfall, land use patterns, and humidity, which influence lightning density. The results reveal that densely populated areas with high lightning activity, such as parts of Java and Sumatra, are particularly vulnerable. Spatial risk maps generated from the model highlight high-risk zones, providing critical insights for disaster mitigation planning and infrastructure protection. Furthermore, the study emphasizes the significant correlation between lightning density, land use, and population exposure, offering a comprehensive framework for understanding lightning risks. This predictive model not only serves as a tool for early warning systems and sustainable spatial planning but also underscores the importance of integrating environmental and spatial data for effective lightning risk mitigation. Future research should incorporate temporal lightning variations and field validation to refine the model and enhance its applicability.

Unsupervised Machine Learning for Detecting Seismic Anomalies: Local Outlier Factor Algorithm on Indonesian Ring Fire Data Naufal Riqullah
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 1 (2023): 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.v3i1.02

The Indonesian Ring of Fire, known for its intense seismic and volcanic activity, poses significant challenges for hazard mitigation and risk management. This study applies an unsupervised machine learning approach using the Local Outlier Factor (LOF) algorithm to detect seismic anomalies in historical earthquake data. The LOF method is advantageous for identifying subtle deviations from typical seismic patterns, making it suitable for complex, multidimensional datasets. The research leverages seismic data collected over a multi-year period, focusing on key parameters such as magnitude, depth, and location. Results indicate that the LOF algorithm effectively identifies anomalous seismic events that could signify potential precursors to larger-scale geological occurrences. The findings highlight the potential of unsupervised machine learning techniques in enhancing earthquake monitoring systems, contributing to more proactive disaster preparedness and response strategies in Indonesia’s Ring of Fire. This study provides insights into the integration of machine learning for real-time seismic anomaly detection, offering an advanced tool for researchers and policymakers.

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Marzuki Sinambela

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adm.jocpes@gmail.com

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Journal Mail Official
adm.jocpes@gmail.com

Editorial Address
Jl. Bunga Terompet Komplek Cipta Pesona 2 No.D.25, Simpang Selayang, Medan Tuntungan, Medan, 20131, Medan, North Sumatera, Indonesia

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INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Marzuki Sinambela

Contact Email adm.jocpes@gmail.com

Phone -

Journal Mail Official adm.jocpes@gmail.com

Editorial Address Jl. Bunga Terompet Komplek Cipta Pesona 2 No.D.25, Simpang Selayang, Medan Tuntungan, Medan, 20131, Medan, North Sumatera, Indonesia

Location Unknown, Unknown INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Marzuki Sinambela

Contact Email
adm.jocpes@gmail.com

Phone
-

Journal Mail Official
adm.jocpes@gmail.com

Editorial Address
Jl. Bunga Terompet Komplek Cipta Pesona 2 No.D.25, Simpang Selayang, Medan Tuntungan, Medan, 20131, Medan, North Sumatera, Indonesia

Location
Unknown,

Unknown

INDONESIA