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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 55 Documents
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Design of a Radiosonde System for Air Quality Monitoring in the Tangerang City Area Muhammad Asa Kardora A
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 2 (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.v3i2.01

Abstract

Tangerang City, located in Banten Province, Indonesia, experiences high rainfall levels that often lead to damaging floods. Climate change has exacerbated these disasters with increasingly unpredictable weather extremes. To address these challenges, upper-air observations using radiosondes are crucial for real-time atmospheric monitoring. Radiosondes effectively measure meteorological parameters such as air pressure, temperature, and humidity when flown into the atmosphere by balloons. This study developed a cost-effective and easily implementable radiosonde system to enhance meteorological data collection in Tangerang City. Thus, the system supports accurate weather analysis and improves disaster management and environmental decision-making in the region.
Utilization of Himawari-9 and Radiosonde Weather Satellite Data in Heavy Rainfall Analysis(Case Study: Semarang, 14 March 2024) M. Alvin Faiz
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 2 (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.v3i2.02

Abstract

The heavy rainfall event that hit Semarang City on 14 March 2024 caused flooding at several points. To understand the cause of this event, a comprehensive meteorological analysis is required. This research utilises Himawari-9 weather satellite data with RGB, day convective storm and 24-hour microphysics methods, as well as upper air observations using radiosonde at 12 UTC in the Semarang area. The use of this method is effective in knowing the atmospheric conditions in the Semarang area. The results of observations with the RGB method show cloud conditions that cause heavy rain with high intensity. Observations with the day convective storm method detect convective clouds that have the potential to cause heavy rain. The 24-hour microphysics method identifies High Cumulonimbus, Dense Cirrus, and Thick Cirrus cloud types that can cause heavy rain. Upper air observations with radiosonde at 12 UTC showed the early phase of severe weather in the Semarang area. The results of this study confirm the importance of satellite and radiosonde data integration in predicting and analysing heavy rain events for hydrometeorological disaster risk mitigation.
Literature Review: Performance Analysis of CNN, LBP, and Haar Cascade using FER-2013 for Facial Emotion Recognition Fahar Rafif Arganto; Daffa Aly Meganendra
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 2 (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.v3i2.03

Abstract

The rapid progress in artificial intelligence is transforming how humans and computers interact, with facial expressions being key markers of human emotions. Since facial expressions change dynamically during communication, they offer insights into emotional states and have attracted significant research interest. However, detecting emotions through facial recognition is challenging due to individual differences in expressions, varied lighting conditions, and different facial orientations. These challenges highlight the need for models that can effectively address these issues to improve detection accuracy. This literature review explores several commonly used algorithms for emotion detection via facial recognition, including Convolutional Neural Networks (CNN), Haar Cascade, and Local Binary Pattern (LBP), with the FER2013 dataset serving as the basis for analysis.
Predictive Maintenance for Automatic Weather Station (AWS) Based on Anomaly Detection Using Autoencoder: A Literature Review Muhammad Afif; Daffa Aly Meganendra
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 2 (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.v3i2.04

Abstract

Automatic Weather Station or AWS is an instrument for measuring weather parameters automatically. The results of measuring weather parameters are very useful in the fields of meteorology and climatology, such as weather prediction, aviation and climate change. Especially in Indonesia, the Meteorology, Climatology and Geophysics Agency or BMKG has main tasks and functions in this field. Currently, data with accurate results is needed to produce accurate weather and climate predictions. However, sometimes there are anomalies in the data caused by AWS damage, resulting in inaccurate data. This will have an impact on modeling results in the fields of meteorology and climatology, where the modeling results are less precise. To overcome this problem, predictive maintenance is needed to avoid data errors in AWS operations. This research aims to build predictive maintenance at an Automatic Weather Station Based on Anomaly Detection using a Machine Learning Autoencoder. The anomaly data can be detected by machine learning autoencoders for monitoring AWS performance and conditions, that methodology applied in this study for build predictive maintenance in AWS. Finally, the expectation of this research is to make accurate predictive maintenance on AWS so perhaps that can reduce maintenance costs and increase the lifespan of the instrument before it breaks.
The Utilization of IoT in Real-time Temperature and Humidity Monitoring Using Microcontroller: A Literature Review Fachrul Rizky Syaputra
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 2 (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.v3i2.05

Abstract

Temperature and humidity must be considered in various fields such as weather, agriculture, technology and industry. The Internet of Things (IoT) can be applied in temperature and humidity monitoring systems so that the data obtained is real time. The research uses a literature review method that has the main goal of searching journals through Google Scholar. This study analyzes the use of IoT for temperature and humidity monitoring using various sensors such as DHT22, DHT11, and DS18B20 in various fields. Based on several journals from 2017 to 2024, it was selected to provide an understanding of the effectiveness and accuracy of different types of sensors in environmental monitoring. The results of the study show that IOT makes it easier to monitor environmental conditions efficiently and accurately, helping to make decisions quickly and automatically. The DHT22 sensor is effective for monitoring due to its low cost and good quality, while DS18B20 excels in accuracy. This research is useful in the development of IoT in various environmental monitoring needs.
Time Series Forecasting for Average Temperature with the Long Short-Term Memory Network in Deli Serdang Geophysics Station Nora Valencia Sinaga; Feriomex Hutagalung; Martha Manurung; Eva Darnila
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 2 (2021): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/x2tzpb02

Abstract

An understanding of trends analysis, and prediction of time series of average temperature as one of parameter weather and climate data for climate variables. It is the central process in assessing the state of the climate of a region and provides an overall estimate about the variations in the climate variables. Explore weather trends using normal and local yearly average temperatures, compare and make observations. In this study, we try to analyze local and normal average temperature data in Deli Serdang geophysc Station based on observation station in situ. The main goal of this study to compare the normal temperature to local station and to predict the average temperature data in BMKG Geophysics Station, Deli Serdang, North Sumatra using Long Short-Term Memory Model (LSTM). Based on the result of normal data science of exploring temperature with local temperature correlation, we got the display of training curve, residual plot and the scatter plot are shown using these codes. Based on the temperature series data from Geophysic station, the MSE value is 0.83 and the R2 value is 0.86.
A Review: Prototype Gyro-Stabilizer for Buoys Amir Aziz Al Awwabin; Adi Widiatmoko; Nardi
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 2 (2021): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/prjfdk42

Abstract

With buoys, tsunami waves caused by underwater earthquakes can be detected. The buoy will monitor and record changes in the sea level in the ocean. Indonesia has already installed several tsunami buoys. Nine buoys were made by Indonesia, two buoys made by America, one buoy from Malaysia, and the other nine buoys donated by Germany. These buoys are placed at all points in the Indonesian seas, such as in the Sumatra, Java, Flores, Maluku, and Banda Seas, so they can assist the Meteorology, Climatology and Geophysics Agency (BMKG) in providing tsunami early warnings. But unfortunately, the tsunami buoy network was not functioning from 2012 to 2018, because it was damaged and lost. Therefore, a tool is needed to maintain the presence, function, and performance of the buoy system so that it can operate properly. A Gyro-Stabilizer device can maintain the stability of the buoy. The Gyro-Stabilizer prototype is made of a simple circuit: a DC motor with a speed of around 18000 rpm and a mechanical gyroscope. The power supply uses the same power supply used by the buoy itself. As long as the Gyro-Stabilizer keeps rotating it will maintain the orientation of the buoy and make it stable. With good buoy stability, the durability and function of the electronic components inside can be maintained because the impact force from waves or sea waves can be reduced by using this Gyro-Stabilizer.
A Review: Information Technology-based Climate Data Dissemination Syalom Alfa Bazeleel Neonane; Adi Bagus Putrantio
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 2 (2021): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/2mnwd629

Abstract

Changes in climate indicators can cause extreme weather and can trigger disasters, such as floods and droughts and even crop failure. it is difficult to predict because farmers and local governments do not understand the importance of climate information, the solution to the problem is to disseminate and disseminate information, but requires an information system that is also inseparable from software, IoT-based applications, and others. With the method used, namely by classifying the climate based on rainfall. In classifying the climate, the oldeman and schmidt-ferguson classifications are used. Then the dataset is formed to calculate the degree or probability of the rainfall category and the Data Normality test. The test results show that the classification of rainfall categories with light, normal, and heavy categories is 79.5%, 40.9%, and 86.4% respectively. While the precision is 96.4%, 42.6%, and 83.3% respectively. Therefore, in making applications as a medium for disseminating information, it is necessary to understand the process of seasonal occurrence, and how to turn these data into information that can be utilized by the wider community.
Analysis of the Use of Telegram Bot for Earthquake Information Dissemination Systems and Weather Forecasting Muhammad Bayu Putra Primary; Nardi
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 2 (2021): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/jgznre15

Abstract

As one method deployment information forecast weather, BMKG has have mobile application based on Android and iOS with the name @infoBMKG since in 2016. Felt urgent for designing system with a deployment model information-based request to stay awake along with growth user application service message instant. Webhook method for Telegram Bot selected because especially effective _ for Bot creators in need guide for finish Settings base Telegram bot creation. Because of the server hosted and should use https then used Webhooks method. So, Telegram bots can respond message with fast. The success rate of Telegram Bot aimed at spreading information varies depending on the menu. A 100% success rate and an average response time of 2.54 seconds were recorded for the menu forecast weather, 100 % and an average response time of 2.76 seconds were recorded for the airport weather information menu, and 100 % and an average response time of 7.28 seconds were recorded for the image information menu satellite.
Prediksi Kualitas Akuakultur Menggunakan Long Short-Term Memory di Wilayah Selat Sunda Aes, John Pieter Sirfefa
Journal of Computation Physics and Earth Science (JoCPES) Vol 2 No 1 (2022): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/fpfhej39

Abstract

Aquaculture plays a vital role in addressing global seafood demands and ensuring food security, particularly in tropical regions like the Sunda Strait, Indonesia. However, aquaculture success depends on key environmental parameters, including sea surface temperature (SST), salinity, ocean heat content, and thermocline depth, which exhibit complex spatiotemporal variability. This study applies a Long Short-Term Memory (LSTM) model to predict aquaculture suitability by analyzing five critical oceanographic parameters: depth of the 26°C isotherm (so26chgt), ocean heat content (sohtc300), mixed layer depth (somxl010), sea surface salinity (sosaline), and sea surface temperature (sosstsst). Using the ORAS5 dataset spanning January 2015 to March 2025, the model achieved high accuracy, with R² scores exceeding 0.89 for all parameters. Spatial prediction maps for November 2024 to March 2025 were generated, highlighting regions with optimal environmental conditions for aquaculture. Results indicate that SST and salinity are the most influential factors affecting aquaculture quality, with favorable conditions predominantly observed from December to May. The findings underscore the potential of deep learning models in supporting sustainable aquaculture management through accurate environmental forecasting.

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