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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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Distribusi Spasial dan Implementasi Metode K-Means Clustering pada Titik Panas di Sumatera Utara Wahyuni, Sri; Dewi, Kartika
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 1 (2021): Jurnal Fisika Komputasi dan Ilmu Kebumian
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/x34r7239

Abstract

Hotspots are indicators of forest and land fires. Hotspot monitoring can be carried out with the help of remote sensing tools and geographic information systems. Hotspot data is obtained from the MODIS sensors from the TERRA and AQUA satellites which contain information on latitude and longitude coordinates and the level of confidence divided by three levels, namely low, medium and high confidence levels. Based on the spatial results, the number of hotspots in North Sumatra Regency is in February, March, June, July, and August. Districts that are dominant with hotspots are Karo Regency, Labuhan Batu Regency, Mandailing Natal Regency, Padang Lawas Regency and South Tapanuli Regency. Based on the results, the process of applying the k-means clustering method to the weka application, the data obtained is in the form of a clustered group and the results can be made into indicators in determining hotspots in districts in North Sumatra province per month.
Prakiraan Suhu Rata-rata Berdasarkan Stasiun Deli Serdang Menggunakan Model Long Short-Term Memory Junaedi, Ilham; Paramita, Endah; Sinaga, Nora Valencia; Wahyuni, Sri; Humaidi, Syahrul
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 1 (2021): Jurnal Fisika Komputasi dan Ilmu Kebumian
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/3nqxkj30

Abstract

An understanding of designs and gage of typical temperature joined of parameter climate and climate data for better water resource organization and orchestrating amid a bowl is uncommonly imperative. Examine climate designs utilizing ordinary and neighborhood every year typical temperatures, compare and make discernments. amid this consider, we'll analyze adjacent and conventional typical temperature data in 96031 Station backed recognition station input. the preeminent objective of this considers to appear the execution of the conventional temperature in an exceedingly single station and to predict the ordinary temperature data utilizing the Long memory Illustrate approach. bolstered the comes about of standard informatics of exploring temperature with adjacent temperature relationship, we got the appear of preparing bend, remaining plot, and thus the diffuse plot is showed up utilizing these codes. the decent execution of 96031 Station had a Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.
Analisis Pemisahan Gelombang Geser di Bawah Daerah Busur Vulkanik Sumatra yang Diperoleh dari Stasiun Jaringan Seismik Broadband. Ristiyono, Lewi; Nurfaizah, Chichi; Purba, Suhenri; Situmorang, Marhaposan
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 1 (2021): Jurnal Fisika Komputasi dan Ilmu Kebumian
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/04wbc932

Abstract

The observation of broadband network seismic had been deployed in Sumatra For-Arc. The waveform data for this study were recorded from January 2014 – December 2016. The earthquake event data were selected with the epicenter of around 950 – 1800 in distance and Magnitude with more than 7 Mw. In this case, we use shear wave splitting to determine an anisotropic structure in Sumatra For-arc. Seismic Anisotropy can perform as a tool to classify and observe anisotropic structures of subsurface deformation processes beneath Sumatra For-Arc. The valid outcomes, in this case, have been gained that they only correspond to the upper layer, which has the delay time duration of 0.5 – 0.8 s is the anisotropic layer located in the Mentawai Island. The fast an anisotropic polarization direction found in Sumatra For-arc are parted into NE-SW direction found on the upper layer.
Kepadatan Probabilitas Spektral untuk Tingkat Noise Sekitar Seismik Broadband Puspita, Endah; Eridawati; Damanik, Nancy
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 1 (2021): Jurnal Fisika Komputasi dan Ilmu Kebumian
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/nc1awf74

Abstract

The time-series approach is commonly utilized to get to the estimation of the likelihood thickness work of control ghostly densities (PDF PSD) of waveform information. This paper is concerned with the introduction of the evaluation of waveform commotion to degree the likelihood thickness work (PDF) be done inside, we utilized the metadata from a stock, a parser occurrence of DNP (Denpasar, Bali, Indonesia), IGBI (Ingas, Bali, Indonesia), and PLAI (Plampang, NTB, Indonesia) from BMKG IA-Networks and computations are based on the schedule utilized by McNamara Demonstrate. The point of this paper to characterize the current and past execution of the stations and recognizing the data on clamor levels at BMKG IA-Networks Station. The result of this paper shows the consistency of the unearthly is displayed the DNP, IGBI, and PLAI organize to confirm the quality of information conjointly acts as a test execution broadband arrange to the time taken by the broadband organize within the field and examination the Lombok earthquake in 2018.
Visualisasi Indeks Cuaca Kebakaran di Aek Godang Berbasis Pendekatan Machine Learning Tarigan, Kerista; Kurniawan, Edison; Wahyuni, Sri
Journal of Computation Physics and Earth Science (JoCPES) Vol 1 No 1 (2021): Jurnal Fisika Komputasi dan Ilmu Kebumian
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/yws6hw85

Abstract

Forest fires are a major natural issue, making temperate and environmental harm whereas dangering human lives. The examined and study for timberland fire had been worn out Aek Godang, Northern Sumatera, Indonesia. There are 26 hotspots in 2017 near Aek Godang, North Sumatera, Indonesia. In this consider, we utilize an information mining approach to prepare and test the information of woodland fire and Fire Weather Index (FWI) from meteorological information. The point of this ponders to anticipate the burned range and distinguish the woodland fire in Aek Godang ranges, North Sumatera. The result of this considers shown the Fire battling and avoidance movement may be one reason for the watched need of relationship. The reality that this dataset exists demonstrates that there's as of now a few exertions going into fire avoidance.
Active Tectonic Segmentation on the Micro Plate of Northern Sumatra Based on Distribution of Earthquake Epicenter in July 2020 Nesia Sabrina Marbun; Melda Panjaitan; Triya Fachriyeni; Eridawati
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: 10.63581/JoCPES.v1i2.01

Abstract

The main goal of this study to increase awareness of earthquake activity due to local faults that have so far received "less attention". Continuous observations can be made on site (on active faults), by using portable seismographs and/or by utilizing the Indonesia Tsunami Early Warning System (Ina-TEWS) network broadband sensors adjacent to these active faults. However, observing using a Portable Seismograph for a long period of time will certainly require a large amount of money. Therefore, it will be more effective to utilize data from seismic sensors that are relatively close to the suspected faults. Based on the analysis that has been carried out, it can be concluded that, in the period from July 1, 2020 to July 31, 2020, there have been 79 earthquakes in the North Sumatra region, with magnitudes between 2.0 – 5.2. The location of the earthquake was dominated by land earthquakes with shallow depths, namely 0-60 km with 54 events and at sea 25 occurrences. The most earthquake occurrences in the period 01 July 2020 - 31 July 2020 occurred around Cluster 1 (local fault Aceh Central, Batee-A, Aceh South, Pidie Jaya and Lot Aceh North, Seulimeum-South), namely 15 earthquake events, so it is classified as a cluster. which is very active in the July 2020 period. In the July 2020 period, seismic activity around the Tripa 2 and Oreng local faults was low compared to other local faults in Northern Sumatra, while in June 2020 there was no seismic activity around the Tripa local faults. 2, and the Oreng fault.
A Literature Review Leveraging Low-Cost MEMS Accelerometers and Raspberry Shake Sensors for Structural Health Monitoring and Seismic Applications Yobel Eliezer Mahardika; Adhe Abdurrafi
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.v4i1.01

Abstract

A viable approach for real-time seismic and structural health monitoring (SHM) applications is the combination of inexpensive MEMS accelerometers with Raspberry Shake sensors. Building on recent developments in electrochemical seismometry and MEMS-based sensor technology, this study assesses the viability of employing these reasonably priced sensors to record seismic waves and structural vibrations, which are essential for determining the integrity of infrastructure and identifying early indicators of structural fatigue. While research on seismic applications emphasizes the requirement for easily accessible, large-scale deployment choices, literature on MEMS applications emphasizes improvements in sensitivity, frequency range, and cost-efficiency. In this investigation, a network of MEMS accelerometers and Raspberry Shake devices is deployed in different structural situations. Custom algorithms are used for data collection and processing. Results indicate that these MEMS-based systems offer adequate accuracy in frequency and amplitude response compared to traditional high-end seismic sensors, demonstrating significant potential in cost-sensitive environments. By leveraging these compact, economical sensors, this approach enables scalable and accessible monitoring solutions, supporting resilient infrastructure management and enhanced seismic hazard assessment.
Integrated of a Real-Time Flood Monitoring System with AI-Based Sensors in North Pantura Java Ahmad Dinan Irsyadi
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.v4i1.02

Abstract

This research focuses on the development and implementation of an Internet of Things (IoT)-based system for predicting tidal flood (banjir rob) using sensor data and machine learning techniques. The system utilizes sensors such as ultrasonic sensors (HC-SR04), DHT11 (for temperature and humidity), connected to an ESP32 module for real-time data collection. The collected data is sent to the ThingSpeak platform for storage and analysis. A machine learning model, specifically a Random Forest Regressor, is trained on historical data from ThingSpeak to predict the flood height based on environmental factors such as temperature and humidity. To enhance the practicality of the system, a Telegram bot is integrated to provide real-time flood predictions directly to users. The system fetches the latest sensor data, predicts the flood height, and sends this information via the Telegram bot. The machine learning model is evaluated using metrics such as R2 score and Mean Squared Error (MSE), ensuring accurate and reliable predictions for flood monitoring. This approach presents a low-cost, real-time, and scalable solution to predict tidal floods in coastal regions. The system's integration of IoT, cloud computing, and machine learning offers a powerful tool for local authorities, disaster management teams, and residents to monitor and prepare for potential flood events. The research highlights the potential of combining IoT technology with AI to enhance environmental monitoring and early warning systems in flood-prone areas.
GP2Y1010AU0F Sensor as Dust Particle Measurement Device: Literature Study on its Efficiency and Application Eva Darnila; Tonny Wahyu Aji; I Made Dwi Pramana Putra
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.v4i1.03

Abstract

Air pollution is an environmental problem that negatively impacts humans and the environment. An air quality monitoring system is required to track the effects of particulate matter (PM), one of the factors that contributes to air pollution. Accurate monitoring equipment is generally expensive and difficult to maintain, so low-cost sensors such as the GP2Y1010AU0F are used as a solution for air quality measurement. This literature review evaluates the efficiency and potential application of the GP2Y1010AU0F sensor by analyzing 20 relevant studies. Based on the review conducted, the GP2Y1010AU0F sensor shows acceptable sensitivity, moderate repeatability, and low error values when measuring air quality. It also showed a good level of correlation with similar devices. The sensor's small size, affordability, and compatibility with microcontrollers make it adaptable to system integration and development into applications and web-based monitoring. However, mass production leads to inconsistency and a reduction in the measurement accuracy of the device. It can be concluded that the GP2Y1010AU0F sensor has potential as a low-cost air quality monitoring equipment with extensive development potential despite its limitations.
Detecting Extreme Weather Patterns Using AI in Bogor Region Fitra Ananda Syahputra
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.v4i1.05

Abstract

Extreme weather events have become more frequent and intense globally, necessitating advanced monitoring and prediction methods. Bogor, Indonesia, known for its complex weather patterns and high rainfall intensity, faces increasing risks of flooding and landslides. This literature review explores the use of Artificial Intelligence (AI) techniques in detecting and predicting extreme weather patterns, with a focus on the Bogor region. Methods such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and hybrid AI models are analyzed for their effectiveness. Key challenges, including data quality, model scalability, and computational requirements, are also discussed. The study highlights AI's potential to revolutionize weather monitoring and disaster mitigation efforts, emphasizing the need for robust and interpretable models tailored to local conditions.

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