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A Web-Based Machine Learning Approach for Standardized Precipitation Index Prediction Hadi, Ahmad Fauzi Faishal; Sinambela, Marzuki; Rachmawardani, Agustina; Trihadi, Edward
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Accurate and user-friendly drought forecasting tools are crucial for mitigating the impact of meteorological droughts, particularly in vulnerable areas such as South Sumatra, Indonesia. This study introduces an interactive web-based application built to anticipate drought conditions by forecasting the Standardized Precipitation Index (SPI). The system relies on deep learning techniques trained using three decades of rainfall data collected from the Climatological Station in South Sumatra. In evaluating model performance, both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures were assessed. While both models delivered comparable short-term predictions, the LSTM experienced a significant decline in accuracy over extended forecasting periods (specifically at SPI-6), primarily due to overfitting. In contrast, the RNN displayed more stable and reliable results, making it the preferable model for this geographical context. Specifically, the RNN achieved a lower Mean Absolute Error (MAE) of 0.4007, a reduced Root Mean Squared Error (RMSE) of 0.4684, and a higher coefficient of determination (R²) of 0.7338. These metrics outperformed those of the LSTM, which recorded a MAE of 0.4115, an RMSE of 0.4840, and an R² of 0.7036. Such results confirm that the RNN offers a more precise and dependable fit for the station’s dataset. The web platform also effectively visualizes the model outputs, providing a dynamic and interactive 24-month forecast view that supports early warning efforts and informed decision-making for regional authorities and stakeholders.

A Web-Based Machine Learning Approach for Standardized Precipitation Index Prediction Hadi, Ahmad Fauzi Faishal; Sinambela, Marzuki; Rachmawardani, Agustina; Trihadi, Edward
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Accurate and user-friendly drought forecasting tools are crucial for mitigating the impact of meteorological droughts, particularly in vulnerable areas such as South Sumatra, Indonesia. This study introduces an interactive web-based application built to anticipate drought conditions by forecasting the Standardized Precipitation Index (SPI). The system relies on deep learning techniques trained using three decades of rainfall data collected from the Climatological Station in South Sumatra. In evaluating model performance, both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures were assessed. While both models delivered comparable short-term predictions, the LSTM experienced a significant decline in accuracy over extended forecasting periods (specifically at SPI-6), primarily due to overfitting. In contrast, the RNN displayed more stable and reliable results, making it the preferable model for this geographical context. Specifically, the RNN achieved a lower Mean Absolute Error (MAE) of 0.4007, a reduced Root Mean Squared Error (RMSE) of 0.4684, and a higher coefficient of determination (R²) of 0.7338. These metrics outperformed those of the LSTM, which recorded a MAE of 0.4115, an RMSE of 0.4840, and an R² of 0.7036. Such results confirm that the RNN offers a more precise and dependable fit for the station’s dataset. The web platform also effectively visualizes the model outputs, providing a dynamic and interactive 24-month forecast view that supports early warning efforts and informed decision-making for regional authorities and stakeholders.

TRANSFORMASI PUBLIKASI STMKG DIGITAL: PENINGKATKAN SUMBER DAYA MANUSIA UNGGUL DAN PERCEPATAN AKREDITASI INSTITUSI Sinambela, Marzuki; Hidayat, Nur; Adi, Suko Prayitno; Sulistya, Widada; Sudarisman, Maman; Riama, Nelly Florida
Majalah Ilmiah METHODA Vol. 13 No. 2 (2023): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol13No2.pp207-216

Improving excellent human resources is an initiative that aims to develop and strengthen individual and collective potential within the BMKG (Meteorology, Climatology and Geophysics Agency) organization to make it a leading global player in the fields of meteorology, climatology, geophysics and instrumentation. Excellent human resources in higher education institutions have the potential to conduct quality research and produce quality scientific publications. In this action of change, the integration of the STMKG Digital publication service program, both updating the E-Journal and building the STMKG PRESS publishing media as a single digital-based and indexed account, has been successfully carried out and is the key to the realization of a comprehensive publication information system. The integration of publication services is aimed at improving efficiency and effectiveness in both indexing and digital documents. This transformation will encourage teams involved in this change action plan to collaborate more, effective communication, and writing literacy. The results of this change action are expected to be useful for STMKG's internal interests, namely to facilitate the accreditation preparation process, academic data collection, and the accreditation assessment simulation process. The benefits for BMKG are the implementation of the BMKG 2022-2024 strategic plan and the improvement of superior human resources towards 500 Doctorates and BMKG Global Player.

A Review: Design and Build Damage Detection Equipment on Sensors and Power Supply Automatic Rain Gauge (ARG) With Long Short Term Memory Integrated sinambela, marzuki
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 2 No. 2 (2023): February 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v2i2.162

The Meteorology, Climatology, and Geophysics Agency (BMKG) technicians have successfully developed automatic rain measuring devices. This tool is called Automatic Rain Gauge - BMKG (ARG-BMKG). The existence of this instrument can replace conventional rain measuring observation systems or public rain stations in Indonesia. ARG – BMKG consists of a tipping bucket sensor, solar panels, GPRS modem, dry battery, and data logger. This repeated operation causes sensor measurement errors due to damage to the sensor due to the sensor voltage supply not meeting specifications, resulting in inaccurate data sent. Predicting sensor damage can be done with predictive maintenance. The results of field tests in previous studies showed that the system could operate properly where the device could measure the voltage of each sensor and send data to the database A sensor damage prediction model was designed and implemented using long-sort term memory (LSTM) by generating root mean square error (rmse). The system can provide damage prediction information on the sensor, and the power supply is displayed through the website properly

VISUAL ANALYSIS OF LOCAL EARTHQUAKE IN NORTH TAPANULI BASED ON DATA SCIENCE Sinambela, Marzuki; Darnila, Eva
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp363-367

Earthquakes are natural phenomena that occur when the Earth's tectonic plates move and release energy. Big Data's emergent epistemological and research paradigms, as well as data science, an increasingly integrated field of data research, are opening up new opportunities. Visualizing earthquake data is all about understanding earthquake characteristics such as size, location and depth. The result show that September was the quietest month in terms of earthquakes, and in this graph we can see the number of earthquakes for each month in 2022. The month of October is the one that has the highest number of earthquakes. We can see the average depth and magnitude of each year on the bubble chart. In addition, the size and color of the bubbles indicate the number of earthquakes that month. In general, most of the earthquakes occurred in the shallow earthquake range and the 1.8-3.85 magnitude range.

Mitigasi Bencana Gempa Bumi dengan Integrasi Analisis Geofisika dan Data Mining Yudha, I Putu Putra Wira Sarwa; Sinambela, Marzuki
Geosfera: Jurnal Penelitian Geografi Vol 3, No 2 (2024): Geosfera: Jurnal Penelitian Geografi
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/geojpg.v3i2.24971

Kabupaten Cianjur merupakan salah satu kabupaten di Provinsi Jawa Barat yang rentan terhadap bencana gempa bumi karena dilewati sesar Cimandiri. Pada akhir tahun 2022, misalnya, telah terjadi insiden gempa bumi besar yang menghantam Kabupaten Cianjur. Penelitian ini bertujuan untuk menyelidiki aktivitas gempa bumi di Kabupaten Cianjur, Jawa Barat, Indonesia, dengan mengintegrasikan analisis multimetode Geofisika dan data resmi dari Portal Satu Data Indonesia. Aspek yang akan diteliti meliputi pola distribusi, frekuensi kejadian, karakteristik gempa bumi, dan faktor-faktor yang mempengaruhi aktivitas seismik di wilayah tersebut. Metode penelitian mencakup pengambilan data episenter dan hiposenter gempa bumi hasil relokasi oleh BMKG, data hasil pengamatan gempa bumi dari BMKG, serta data jumlah kejadian bencana alam yang diunduh dari Portal Satu Data Indonesia. Data hasil pengamatan dari BMKG akan dipadukan dengan data mining pada dataset sumber. Analisis data bertujuan untuk mengidentifikasi sumber, penyebab, dan karakteristik gempa, serta mengelola informasi dari data yang besar menjadi ringkas dan mudah dipahami. Sebagai referensi, penelitian ini akan menyertakan kajian pustaka dari penelitian-penelitian yang membahas kasus gempa bumi dari seluruh dunia. Diharapkan, informasi yang komprehensif tentang karakteristik gempa bumi di wilayah Cianjur ini dapat berkontribusi dalam membangun mitigasi bencana yang efektif.

Pendekatan AI dan Data Sains dalam Bencana Geo-Hidrometeorologi di Sumatera Utara Sinambela, Marzuki; Suharini, Yustina Sri
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 1 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No1.pp152-158

Challenges in the era of society 5.0 in disaster mitigation and management in Indonesia encourage the importance of innovation and adaptation to new technologies in dealing with geo-hydrometeorological disasters in North Sumatra, Indonesia. North Sumatra is one of the provinces in Indonesia located in the northwestern part of Sumatra Island. It is an area that has the potential for Geo-Hydrometeorological disasters, whether it comes from earthquakes, floods, strong winds, drought due to climate change, landslides, and others. In this research, disaster management is needed to provide the widest possible information to the community related to mitigation, preparedness, emergency response, and recovery. The purpose of this research is to provide an understanding of geo-hydrometeorological disasters to the people of North Sumatra, Indonesia with AI and Data Science approaches, namely Prediction and Early Warning, risk and vulnerability analysis, monitoring, real-time response, data and information management, and education and public awareness. In general, the development of informatics engineering in geo-hydrometeorology disasters based on AI and Data Science has had a very good impact, both from the code of ethics and ethics of Professional Engineers, Professional and Safety, Occupational Health and Safety, and the Environment. AI and Data Science approaches in engineering practice in the community encourage the handling of geo-hydrometeorological disasters in North Sumatra has great potential to improve the effectiveness of disaster mitigation, response and recovery. Data analysis with AI and data science approaches helps in making policies that are more targeted and effective in disaster risk mitigation. Data Science can be used to analyse disaster impacts and assist in effective recovery planning. It requires data availability, and collecting data post-disaster can be difficult, but it is essential for impact analysis and model improvement.

Timeseries forecasting for Local Average Temperature in Northern Sumatera Using Long Short-Term Memory Model Sinambela, Marzuki; Sudarisman, Maman; Munawar, Munawar
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 2 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v5i2.385

For better management and planning of water resources in a basin, it is important to understand trends and predict average temperature as one of the parameters of weather and climate data. The study of weather trends using normal and local annual average temperature, comparison and observation. In this study, we will analyse the local and normal average temperature data in the city of Medan, based on the observation station in situ. The main objective of this study is to compare the normal temperature with the local station and to predict the temperature data in the city of Medan, North Sumatra by using the long term short term memory model. 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. The good performance of Kualanamu and better than Deliserdang station had MSE value 0.01 and R2 value 0.98, close to zero represents better prediction quality.

Design and Construction of Low-Cost Seismometer using Geophone Sensor Based on Single Board Computer FAUZAN, RAIHAN AHMAD; RUSANTO, BENYAMIN HERYANTO; SINAMBELA, MARZUKI; TRIHADI, EDWARD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 4: Published November 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i4.381

Salah satu faktor utama dalam upaya peningkatan sistem pemantauan seismik adalah penyebaran dan interkoneksi jaringan seismograf secara menyeluruh. Penggunaan geophone dapat menjadi solusi alternatif yang potensial dalam membuat low-cost seismograf untuk meningkatkan cakupan dan efisiensi jaringan pemantauan seismik. Penelitian ini bertujuan untuk merancang low-cost seismometer yang terdiri dari sensor geophone, rangkaian signal conditioning, modul ADC ADS1256, GPS Ublox Neo-7M, dan Raspberry Pi 3 Model B+. Data ditampilkan melalui antarmuka berbasis website secara realtime menggunakan protokol komunikasi MQTT dan disimpan dalam format miniSEED. Hasil pengujian menunjukkan tingkat background noise pada sensor yang cukup baik (84.8% titik frekuensi berada dalam batasan NHNM/NLNM), dengan kemampuan mendeteksi gempa lokal pada magnitude ≥3.0. Prototipe ini memberikan solusi low-cost seismograf yang efektif untuk pemantauan seismik.

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