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检索条件"机构=Undergraduate Program in Applied of Geophysics"

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Spatial Cluster Analysis of Land Seismicity in the Northem Part of Sumatra Using the K-Medoids Algorithm

Spatial Cluster Analysis of Land Seismicity in the Northem P...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Arrizal, Hanifullah Hafidz Sinambela, Marzuki Widodo Adi, Suko Prayitno Frianto, Herri Trisna Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics Stmkg Jakarta Indonesia Undergraduate Program in Applied of Geophysics Stmkg Jakarta Indonesia Undergraduate Program in Applied of Electrical Engineering Politeknik Negeri Medan Medan Indonesia

ISBN: (纸本)9798350359633

In the seismic-prone region of Northern Sumatra, where recent events like the Toba earthquake swarm have reshaped the land's seismic dynamics, understanding the spatial distribution and clustering of earthquakes is paramount for disaster mitigation. This study employs the K-Medoids algorithm to analyze seismic data from January 2019 to 2023, providing crucial insights into seismic event clustering. While K=2 emphasizes depth distinctions, K=3 unravels a nuanced perspective, capturing differences in both depth and geographical location of seismic events. The validity analysis supports these findings, contributing significantly to seismic hazard assessment in North Sumatra. This research underscores the need for ongoing study and heightened preparedness in earthquake-prone regions. © 2023 IEEE.

关键词： Earthquakes

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Radiosonde System Using ESP32 and LoRa Ra-02 Web-Based for Upper-Air Profile Observation

Radiosonde System Using ESP32 and LoRa Ra-02 Web-Based for U...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Rizqy Nugraha, Muchamad Chairuniza, Putri Meijlan Yasir, Ahmad Wahyu Aji, Tonny Maghridlo, Aviv Sugiarto Widodo, Anton Munawar Sinambela, Marzuki Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia BMKG Deputy of Instrumentation Calibration Network and Communication Central Jakarta Indonesia Undergraduate Program in Applied of Geophysics STMKG Tangerang Indonesia

ISBN: (纸本)9798350379839

A Study about upper air profile observation, weather forecasting and atmosphere observation is very important information for all people, as it can support or inhibit human activities process that involve measuring and analyzing atmospheric parameters such as temperature, humidity, pressure, and wind speed at various heights above the earth's surface for fields that are related to weather and climate. One of the instruments used to measure weather parameters for the upper air profile is the radiosonde. Radiosonde is a system consisting of a transmitter and receiver where the transmitter or receiver system consists of a temperature, humidity, and air pressure sensor that is equipped with GPS carried by a balloon to the atmosphere, then the data of the observations is transmitted to the observation station. However, the radiosonde system is quite costly. The background was what created the idea for the author to make a low-cost radiosonde system. In this research, we make radiosonde system using the ESP32 microcontroller, DHT 22 and BMP 180 sensors to measure temperature, humidity, air pressure, and altitude and Neo-Blox 6M GPS for navigational and tracking of longitude and latitude. The data will be transmitted via the implementation of LoRa (Long Range) antenna modules. The observation data will then be processed in the database and shown in real time on the website. Several experiments have produced positive results, thus providing incentives for further improvements and applications in the field of meteorology. © 2024 IEEE.

关键词： Wind

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Clustering Analysis of Earthquake Based on K-Means, DBSCAN, and Fuzzy C-Means in North Sumatra

Clustering Analysis of Earthquake Based on K-Means, DBSCAN, ...

引用

2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Sinambela, Marzuki Arrizal, Hanifullah Hafidz Darnila, Eva Munawar Nugraha, Muchamad Rizqy Widodo, Anton Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Universitas Malikussaleh Teknik Informatika Bukit Indah Indonesia Program in Applied of Geophysics STMKG Tangerang Indonesia

ISBN: (纸本)9798350379839

This study investigates the efficacy of three clustering algorithms-K-means, DBSCAN, and Fuzzy C-Means-in classifying seismic events in North Sumatra. Earthquake data spanning from January 2019 to December 2022, provided by the Indonesian Agency for Meteorology, Climatology, and geophysics (BMKG), was analyzed to evaluate how well each algorithm identifies and organizes clusters based on earthquake characteristics such as longitude, latitude, magnitude, and depth. K-means excelled in producing well-separated, spherical clusters;DBSCAN effectively detected clusters of varying densities and identified noise points;Fuzzy C-Means offered insights into overlapping clusters and gradual transitions. Despite some differences in the clustering results, all methods provided similar outcomes, with DBSCAN uniquely highlighting noise points. These insights contribute to a better understanding of seismic activity in North Sumatra and could improve earthquake prediction models and disaster preparedness strategies. © 2024 IEEE.

关键词： K-means clustering

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Predicting Hailstorms Through Machine Learning Approach Using Multiple Source Data Analysis 3rd

Predicting Hailstorms Through Machine Learning Approach Usin...

引用

3rd International Conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Auliya, Marinda Nur Saputra, Agung Hari Kristianto, Aries Qomariyatuzzamzami, Latifah Nurul Undergraduate Program in Applied Meteorology Indonesia State College of Meteorology Climatology and Geophysics South Tangerang15221 Indonesia

ISBN: (纸本)9789819707393

There were 39 hailstorms in the regions of DKI Jakarta, Banten, and West Java from 2010 to 2019. The high frequency of these occurrences underscores the need to study hailstorms as part of extreme weather phenomena, especially considering their potential impact on agriculture, infrastructure, and public safety. Hail events are intimately linked to cloud microphysics and growth processes that can be effectively analyzed using weather radar, Himawari-8 satellite, and ERA-5. This research, conducted from 2015 to 2022, explores an innovative approach by employing a random forest classifier to distinguish three classes of events: no rain, rain, and hail. The input data quality for the prediction model was found to be relatively robust, leading to congruent input and output data. The radar predictor’s significant influence on outcomes was revealed, displaying a higher maximum CSI value on CMAX predictor by 0.33 for event differentiation with threshold 44 dBZ. Although the Himawari-8 predictor was significant for predicting events, its uniform data distribution posed challenges in event differentiation. The combination of multiple source data, coupled with the machine learning approach, makes it possible to greatly improve the robustness of hailstorms prediction compared to any single data commonly used in operational forecasting. Also, that multiple source data produced the most accurate rather than other combinations with accuracy, recall, precision, and F1 scores of 0.9084, 0.9084, 0.9125, and 0.9047, respectively. The accuracy achieved by a random forest model brings encouraging prospects for future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Meteorological radar

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Prediction of Rainfall in North Sumatera Using Machine Learning 7

Prediction of Rainfall in North Sumatera Using Machine Learn...

引用

7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Rumapea, Humuntal Sinambela, Marzuki Jaya, Indra Kelana Sarkis, Indra M Universitas Methodist Department of Computer Indonesia Climatology Geophysics STMKG Undergraduate Program in Applied of Instrumentation Meteorology Indonesia

ISBN: (纸本)9798350360752

Due to the large number of variables involved, weather prediction is one field with a wealth of data but also a higher degree of difficulty. Probabilistic models are used to generate forecasts, which are often not particularly accurate because they have a margin of error. In this article, we report on an exploratory study that looked into using machine learning to predict the rainy season. We employed some of the most significant machine learning methods (multinomial NB, decision tree, random forest, logistic regression, and SGD) in conjunction with a set of data detailing rainfall observations gathered in Northern Sumatera's major cities during the previous ten years. The approach based on logistic regression is the best, according to the data. © 2023 IEEE.

关键词： Rain

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IoT-Based Air Quality Monitoring System Design and Development Using ESP32

IoT-Based Air Quality Monitoring System Design and Developme...

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International Conference on Informatics and Computing (ICIC)

作者： Marzuki Sinambela Munawar Muchamad Rizqy Nugraha Anton Widodo Joko Sugi Purnomo Kadir Ahmad Meijlan Yasir Tonny Wahyu Aji Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Undergraduate Program in Applied of Geophysics STMKG Tangerang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The objective of this study is to develop and construct an ESP32-based Internet of Things (IoT) air quality monitoring system. As part of the study technique, hardware and software that facilitate the monitoring of air quality metrics like $\mathrm{PM}_{2.5}, \mathrm{PM}_{10}, \mathbf{C O}, \mathrm{NO}_{2}, \mathrm{SO}_{2}$, and O 3 . Through an intuitive interface, users may monitor air quality thanks to the hardware’s ability to identify air contaminants and communicate data in real-time to a web-based platform. The results indicate that the system can reliably monitor air quality and supply values for the Air Pollution Index (API). A positive association is seen in the outcomes of sensor testing done using common equipment. According to the results of the regression study, the DFRobot Air Quality sensor for measuring $\mathbf{P M}_{2.5}$ parameters has an $R^{2}$ value of 0.9402 and for $P_{10}$ parameters has an $R^{2}$ value of 0.9175. The MiCS-4514 sensor for measuring CO parameters has an $R^{2}$ value of 0.8315, and NO 2 parameters has an $R^{2}$ value of 0.8778. The MQ-136 sensor for measuring the SO 2 parameter has an $R^{2}$ value of $\mathbf{0. 8 2 4 3}$, and the MQ-131 sensor for measuring the $O_{3}$ parameter has an $R^{2}$ value of 0.7838. This study presents a novel approach by integrating a wider range of pollutants into a single monitoring system and ensuring high accuracy through calibration and validation, thereby addressing existing gaps in current IoT air quality monitoring systems and enhancing the reliability and comprehensiveness of air quality assessments.

关键词： Accuracy Atmospheric measurements Current measurement Air quality Real-time systems Pollution measurement Internet of Things Monitoring System analysis and design Testing

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Clustering Earthquakes in West Java Using Machine Learning Algorithm 7

Clustering Earthquakes in West Java Using Machine Learning A...

引用

7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Saragih, Naikson Fandier Pratiwi, Yolanda Yulianti Jaya, Indra Kelana Sarkis, Indra M. Arrizal, Hanifullah Hafidz Sinambela, Marzuki Universitas Methodist Indonesia Department of Computer Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology and Geophysics STMKG Indonesia

ISBN: (纸本)9798350360752

It is important to understand seismicity in West Java, Indonesia, which lies within the Pacific Ring of Fire and is prone to seismic events. We analyze data from earthquakes that occurred between September 2022 and September 2023. We use the K-medoids clustering method to organize the data into clusters with medoids as central representatives. This provides deeper insights into the patterns of seismic activity. As a result, we identified K=15 as the optimal number of clusters through the silhouette and Dunn index. This segmentation provided a very effective solution. In particular, the K-medoids revealed significant seismic clusters in West Java, which has implications for earthquake monitoring and hazard assessment in the region. © 2023 IEEE.

关键词： Earthquakes

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The Carbon Dioxide Filtration System Using Chlorella Pyrenoidosa Microalgae IoT-based for Air Quality Improvement

The Carbon Dioxide Filtration System Using Chlorella Pyrenoi...

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International Conference on Informatics and Computing (ICIC)

作者： Munawar Tonny Wahyu Aji Nizirwan Anwar Ahmad Meijlan Yasir Aviv Maghridlo Marzuki Sinambela Anton Widodo Muchamad Rizqy Nugraha Putri Chairuniza Undergraduate Program in Applied of Geophysics STMKG Tangerang Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Departement of Computer Science Esa Unggul University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Air pollution is a global problem that affects climate stability and human health. It is mainly caused by increasing carbon dioxide $\left(\mathrm{CO}_{2}\right)$ levels. This project aims to lower CO 2 levels using a novel technique that employs green microalgae, specifically Chlorella pyrenoidosa, and an Internet of Things monitor. This experiment combines an MG-811 CO2 sensor and an ESP32 microcontroller to measure CO 2 levels in a controlled setting. In real-time, the ThingSpeak platform receives and processes the sensor data. During the observation period, the average concentration of CO 2 in the system decreased from 707.07 ppm to 677.90 ppm, representing a $\mathbf{3.15 \%}$ drop in CO 2 levels. A statistical study revealed a significant trend indicating a steady drop in CO 2 levels, with a regression coefficient of $\mathbf{-0.000526}$ and a p-value of $7.8975 \times 10^{-6}$. Nevertheless, the data showed inconsistency, with a standard deviation of 81.44 ppm, indicating differences that may be due to the aerator capacity and volume of Chlorella pyrenoidosa used. These results indicate that it is possible to integrate Chlorella pyrenoidosa with Internet of Things technology to reduce CO 2 and manage air quality effectively. This study shows the prospects of this method for scalable and cost-effective environmental solutions, but indicates that further research is needed to improve the performance and adaptability of the system.

关键词： Statistical analysis Scalability Prevention and mitigation Carbon dioxide Air pollution Real-time systems Pollution measurement Internet of Things Monitoring Standards

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MKG Robotic Ship: Smart Autonomous Surface Vehicle Type Robotic Ship in Oceanographic Parameters Measurement and Early Tsunami Detection

MKG Robotic Ship: Smart Autonomous Surface Vehicle Type Robo...

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International Conference on Informatics and Computing (ICIC)

作者： Anton Widodo Riko Dimas Aditya Wiranata Muchammad Rizqy Nugraha Pandu Hadi Wijoyo Adilaksa Kharisma Muhammad Ramdhani Setyo Nugroho Munawar Marzuki Sinambela Program in Applied of Instrumentation Meteorology Climatology and Geophysics Tangerang Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology and Geophysics Tangerang Indonesia Program in Applied of Meteorology STMKG Tangerang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research explores the innovation of Indonesia’s first ASV robotic ship equipped with ROS technology for measuring oceanographic parameters and early tsunami detection. The robotic ship is designed to offer an efficient and effective solution for realtime data collection and enhancing the tsunami early warning system in Indonesian waters. ROS technology is utilized to optimize navigation, data acquisition, and inter-system communication, enabling autonomous operations under various maritime environmental conditions. The study includes performance testing of the vessel across different water locations, with a particular focus on its water resistance and operational capabilities in extreme weather conditions. The test results demonstrate that the MKG robotic ship achieves high accuracy in measuring oceanographic parameters. Additionally, the ship can detect early signs of tsunamis quickly and precisely, thus reducing disaster risks and providing faster response times for evacuation. This innovation is expected to enhance maritime monitoring and disaster mitigation capabilities in Indonesia through an advanced early warning system. Moreover, the research paves the way for further advancements in autonomous ship technology for oceanographic applications and disaster management, while also offering significant benefits to the community by improving access to maritime monitoring information, including weather, sea conditions, tsunami potential, and other marine data.

关键词： Temperature measurement Temperature sensors Technological innovation Accuracy Sea measurements Tsunami Robot sensing systems Marine vehicles Ocean temperature Monitoring

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Spatial Cluster Analysis of Land Seismicity in the Northem Part of Sumatra Using the K-Medoids Algorithm

Spatial Cluster Analysis of Land Seismicity in the Northem P...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Hanifullah Hafidz Arrizal Marzuki Sinambela Widodo Suko Prayitno Adi Herri Trisna Frianto Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Jakarta Indonesia Undergraduate Program in Applied of Geophysics STMKG Jakarta Indonesia Undergraduate Program in Applied of Electrical Engineering Politeknik Negeri Medan Medan Indonesia

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