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Information Technology and Computing (ICITCOM), International Conference on

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

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

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.

关键词： Temperature measurement Temperature sensors Wireless sensor networks Atmospheric measurements LoRa Weather forecasting Terrestrial atmosphere Humidity Receivers Monitoring

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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, ...

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Information Technology and Computing (ICITCOM), International Conference on

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

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

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.

关键词： Climatology Disasters Computational modeling Earthquakes Noise Clustering algorithms Predictive models Classification algorithms Information technology Meteorology

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Comparative Analysis of AutoGluon and Bayesian Optimized LSTM for Long-Term Temperature Forecasting

Comparative Analysis of AutoGluon and Bayesian Optimized LST...

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International Conference on Informatics and Computing (ICIC)

作者： Hanifullah Hafidz Arrizal Munawar Aditya Mulya Dyah Prihatini Djenla Muchamad Rizqy Nugraha Marzuki Sinambela Anton Widodo Diah Aryani Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Program in Applied of Geophysics STMKG Tangerang Indonesia Program in Applied of Meteorology STMKG Tangerang Indonesia Deputy of Instrumentation Calibration Network and Communication BMKG Cental Jakarta Indonesia Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Departmen of Informatic Esa Unggul University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In this paper, two models for long-term temperature forecasting-AutoGluon and Bayesian Optimized Long Short-Term Memory-are compared. Because reliable temperature forecasts are essential for industries like agriculture and disaster preparedness, this study compares the effectiveness of an automated machine learning (AutoML) framework called AutoGluon versus a Bayesian tuned LSTM model that was tuned using Bayesian techniques. The evaluation focuses on key metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and $R^{2}$ values. The results show that the Bayesian Optimized LSTM performs better than AutoGluon on all criteria, especially when it comes to handling significant temperature fluctuations. It additionally indicates improved accuracy and resilience. The Bayesian Optimized LSTM is perfect for applications needing high precision, despite its longer training times and greater processing requirements. By comparison, AutoGluon offers a more expeditious and user-friendly solution, but its accuracy is inferior to that of the Bayesian Optimized LSTM. The findings imply that the Bayesian Optimized LSTM performs better in high-precision tasks, while AutoGluon is appropriate for cases that prioritize ease of use and quick outcomes.

关键词： Training Measurement Accuracy Predictive models Automated machine learning Bayes methods Root mean square Informatics Long short term memory Resilience

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Prediction of Rainfall in North Sumatera Using Machine Learning

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

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International Conference of Computer Science and Information Technology

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

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.

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Rainfall Prediction in North Barito Regency with Recurrent Neural Network and Support Vector Machine Models

Rainfall Prediction in North Barito Regency with Recurrent N...

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International Conference on Informatics and Computing (ICIC)

作者： S Indra M Sarkis Yolanda Yulianti Pratiwi Rumapea Ivan Auyudy Farida Gultom Marzuki Sinambela Indra Kelana Jaya Department of Informatic Universitas Methodist Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Department of Informatic Universitas Efarina

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Riveroverflows in North Barito Regency often cause flooding, which occurs especially during high rainfall events. Currently, many attempts have been made to utilise technology to predict rainfall. This research uses historical rainfall data of Beringin Meteorological Station, Montallat Rain Post, Kandui Rain Post, Teweh Tengah Rain Post and Beriwit Rain Post from 2010 to 2023. The models used are Recurrent Neural Network (RNN) and Support Vector Machine (SVM). These two models are tested for performance and will be compared using the RMSE, MAE, and MSE values. The evaluation results show that the RNN model at Beringin Meteorological Station has low RMSE, MAE, MSE evaluation metric values and has the smallest difference in metric values between training data and testing data compared to RNN models at other rainfall observation locations and SVM models at all other rainfall observation locations. The RNN evaluation metric value of Beringin Meteorological Station when the data is trained produces an RMSE of 0.0571, MAE of 0.0299, and MSE of 0.0033 and when the data is tested produces an RMSE of 0.0587, MAE of 0.0304, and MSE of 0.0034. The SVM evaluation metric value of Beringin Meteorological Station when the data is trained produces an RMSE of 0.0933, MAE of 0.0393, and MSE of 0.0087 and when the data is tested produces an RMSE of 0.1118, MAE of 0.0594, and MSE of 0.0125.

关键词： Support vector machines Measurement Rain Recurrent neural networks Training data Predictive models Data models Floods Informatics Testing

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Clustering Earthquakes in West Java Using Machine Learning Algorithm

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

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International Conference of Computer Science and Information Technology

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

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 $\mathrm{K}=15$ 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.

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Rainfall Prediction in Central Java Based on Comperatif Analysis of LSTM and BiLSTM

Rainfall Prediction in Central Java Based on Comperatif Anal...

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International Conference on Informatics and Computing (ICIC)

作者： Dyah Prihartini Djenal Hanifullah Hafidz Arrizal Eva Darnila Munawar Nelly Florida Riama Muchamad Rizqy Nugraha Anton Widodo Shindyko Wibowo Marzuki Sinambela Deputy of Instrumentation Calibration Network and Communication BMKG Cental Jakarta Indonesia Undergraduate Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Tangerang Indonesia Department of Informatic Universitas Malikussaleh Bukit Indah Aceh Indonesia Program in Applied of Geophysics STMKG Tangerang Indonesia Director Of Education And Training BMKG Cental Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Predicting rainfall accurately is essential to many facets of daily life. Often, rainfall exhibits complex and nonlinear patterns that are difficult for conventional statistical models to fully represent. Due to their effective capacity to describe temporal relationships, Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks have recently drawn attention as possible alternatives. This study uses historical data from Central Java, Indonesia, to assess the accuracy of LSTM and BiLSTM models in predicting seasonal rainfall. The evaluation measures include prediction accuracy under various scenarios, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results indicate that, while both the LSTM and BiLSTM models perform competitively, the BiLSTM model shows somewhat better accuracy metrics under various climatic circumstances and parameter configurations. This work advances rainfall forecasting techniques by demonstrating how BiLSTM networks can improve predictive skills.

关键词： Java Rain Accuracy Neural networks Measurement uncertainty Bidirectional long short term memory Predictive models Root mean square Informatics Forecasting

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The Subsurface Modelling of Karaha-Telaga Bodas Geothermal System using Gravity Method 2

The Subsurface Modelling of Karaha-Telaga Bodas Geothermal S...

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2nd Southeast Asian Conference on geophysics, SEACG 2018

作者： Yosephin, Missela Santoso, Djoko Setianingsih Undergraduate Program of Geophysical Engineering Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung40132 Indonesia Applied and Exploration Geophysics Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung40132 Indonesia

The Complete Bouguer Anomaly (CBA) map from 1997 gravity survey at Karaha-Telaga Bodas, West Java, Indonesia is used to make a model of the subsurface geothermal system. A two-dimensional cross-section of the system is produced using a 2.5-D forward modeling. Initially, the print CBA maps of the Karaha-Telaga Bodas geothermal system is digitized. Next, the digital map is separated by using the moving average and spectral filtering methods, so a regional and residual anomaly maps are obtained. The subsurface modelling is made based on geological information of the area to produce a model that can represents the real condition as similar as possible. The line chosen for modelling has a North - South orientation, passing through Karaha and Telaga Bodas to foresee both of the geothermal system in one cross-section. Subsurface model interpretation shows that there are two separate granodiorite intrusion body with density contrast of 0.5 g/cc below Karaha - Telaga Bodas. This conclusion coincides with the intrusion body found during drilling operation. The intrusion body is possibly the heat source of the system. The reservoir of this system is interpreted as volcanic breccia with thickness of two kilometres in Telaga Bodas and keeps thinning towards Karaha. The fluids of the Karaha-Telaga Bodas Geothermal System are estimated to come from acid sulphate water and meteoric water entering from Telaga Bodas, as well as meteoric water entering from Karaha. There is an upflow in the South, Telaga Bodas. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Sulfur compounds

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Subsurface Modeling Based on Gravity Data of Rendingan-Ulubelu-Waypanas (RUW) Geothermal Field, Lampung, Indonesia 2

Subsurface Modeling Based on Gravity Data of Rendingan-Ulube...

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2nd Southeast Asian Conference on geophysics, SEACG 2018

作者： Widya Permatasari, Ana Santoso, Djoko Setianingsih Undergraduate Program of Geophysical Engineering Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung40132 Indonesia Applied and Exploration Geophysics Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung40132 Indonesia

Indonesia is located along the archipelago which has abundance of volcanoes along the arc. Therefore, Indonesia has a very high geothermal energy potential, such as the Rendingan-Ulubelu-Waypanas Geothermal Field in Tanggamus Regency, Lampung Province. Geophysical exploration is required to characterize the subsurface geothermal system. One of the methods that can be used is gravity method. The data used in this study is an analog Rendingan-Ulubelu-Waypanas Geothermal Field Complete Bouguer Anomaly Map of 2005. The analog map was then converted into digital map for further data processing. Separation of regional and residual Bouguer anomalies has been performed using moving average and spectral filter methods, in which the spectral analysis was conducted in 1D and 2D. The value of Bouguer background density used for 2.5D subsurface modeling is 2.67 g/cc. The gravity anomalies obtained in this study can be interpreted as follow. The existence of high anomalies under Mount Waypanas was interpreted as a Tertiary intrusion of granite with a density contrast of 0.22 g/cc. While low anomalies in the northern part was interpreted as a Quaternary intrusion of diorite with a density contrast of -0.22 g/cc, which acts as the heat source of the system. The reservoir of the system is the Quaternary young volcanics exposed on the surface with averaged thickness of 1500 m. We concluded that the fluid flow in this geothermal system is the meteoric water from Mount Rendingan and Mount Tanggamus, which has a higher topography, flows to a lower topography area in the Mount Waypanas, The fluid flow also creates an upflow under Mount Rendingan. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Topography

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Subsurface Modeling Based on Gravity Data of Rendingan-Ulubelu-Waypanas (RUW) Geothermal Field, Lampung, Indonesia

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IOP Conference Series: Earth and Environmental Science 2019年第1期318卷

作者： Ana Widya Permatasari Djoko Santoso Setianingsih Undergraduate Program of Geophysical Engineering Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung 40132 Indonesia Applied and Exploration Geophysics Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung Ganesha 10 Bandung 40132 Indonesia

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