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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

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.

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Cluster Analysis and Seismicity of The Samosir Using Machine Learning Approach 8

Cluster Analysis and Seismicity of The Samosir Using Machine...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Sinambela, Marzuki Darnila, Eva Jaya, Indra Kelana Sarkis, Indra M Rikki, Alex STMKG Program in Applied of Instrumentation Meteorology Climatology Geophysics Indonesia Universitas Malikussaleh Department of Computer Aceh Indonesia Universitas Methodist Indonesia Department of Computer Indonesia Universitas Katolik Santo Thomas Indonesia

ISBN: (纸本)9798350342604

The earthquake activity in the past six months, 2021 in the island of Samosir, North Sumatra is quite active, as it's located in the seismic region on the Toba Tuft. In this case, we have used a clustering approach to look at the likelihood of seismic activity in Samosir, North Sumatra, recorded by sensors in the seismic network installed in either the broadband or the new mini-region. Samosir quake cannot be classified as unidentified source based on earthquake source. In particular, it identifies a distribution of seismicity in Samosir by segmented activities using a cluster method. The result showed that the dominance of inland earthquakes in Samosir earthquake, North Sumatra varies swarm. The magnitude frequency distribution for the period from the 23rd of January to the 31st of May 2021 was dominated by earthquakes with small magnitude less than 4. A series of mostly small earthquakes without a discernible mainshock. They are usually of short duration, but may last for days, weeks or sometimes months. They frequently occur in the same places. Geothermal activity is associated with most of the swarms. © 2023 IEEE.

关键词： Earthquakes

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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 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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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 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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Cluster Analysis and Seismicity of The Samosir Using Machine Learning Approach

Cluster Analysis and Seismicity of The Samosir Using Machine...

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International Conference on Informatics and Computing (ICIC)

作者： Marzuki Sinambela Eva Darnila Indra Kelana Jaya Indra M Sarkis Alex Rikki Program in Applied of Instrumentation Meteorology Climatology Geophysics STMKG Indonesia Department of Computer Universitas Malikussaleh Aceh Indonesia Department of Computer Universitas Methodist Indonesia Universitas Katolik Santo Thomas Indonesia

The earthquake activity in the past six months, 2021 in the island of Samosir, North Sumatra is quite active, as it’s located in the seismic region on the Toba Tuft. In this case, we have used a clustering approach to look at the likelihood of seismic activity in Samosir, North Sumatra, recorded by sensors in the seismic network installed in either the broadband or the new mini-region. Samosir quake cannot be classified as unidentified source based on earthquake source. In particular, it identifies a distribution of seismicity in Samosir by segmented activities using a cluster method. The result showed that the dominance of inland earthquakes in Samosir earthquake, North Sumatra varies swarm. The magnitude frequency distribution for the period from the 23rd of January to the 31st of May 2021 was dominated by earthquakes with small magnitude less than 4. A series of mostly small earthquakes without a discernible mainshock. They are usually of short duration, but may last for days, weeks or sometimes months. They frequently occur in the same places. Geothermal activity is associated with most of the swarms.

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Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

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Remote Sensing 2024年第3期16卷

作者： Nainggolan, Lamtupa Ni, Chuen-Fa Darmawan, Yahya Lo, Wei-Cheng Lee, I-Hsian Lin, Chi-Ping Hiep, Nguyen Hoang Earth System Science Program Academia Sinica Taipei 11529 Taiwan Graduate Institute of Applied Geology National Central University Taoyuan City32001 Taiwan Education and Culture Board of Medan City Government North Sumatra 20233 Indonesia Center for Environmental Studies National Central University Taoyuan City32001 Taiwan Indonesian College of Meteorology Climatology and Geophysics STMKG Banten 15221 Indonesia Department of Hydraulic and Ocean Engineering National Cheng Kung University Tainan City 701 Taiwan

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as "moderate" to "very good" potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management

关键词： Aquifers

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