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All Journal Elektron Jurnal Ilmiah AL KAUNIYAH TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Renewable Energy and Smart Device
Santoso Soekirno
Universitas Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan R. Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto
Elektron : Jurnal Ilmiah Volume 14 Nomor 1 Tahun 2022
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.14.1.278

Abstract

As a vast archipelagic country with diverse topographic conditions and has an annual average rainfall of more than 2000 mm, Indonesia is prone to hydrometeorological disasters. Based on Indonesia's disaster data, throughout 2021 there were 3,658 incidents of floods and landslides distributed throughout Indonesia. This makes real-time rainfall monitoring with high density indispensable. Indonesia currently has a rainfall monitoring system about 1000 automatic rain gauges, so an increase in the spatial resolution of network is necessary. The increasing density of monitoring equipment using rain gauges and weather radar poses the problem of high procurement and operational costs. Therefore, several alternative rainfall monitoring systems are needed. In this article, we review several studies that focus on the utilization of terrestrial and satellite communication link operating in high frequency bands as an alternative for measuring rainfall. Optimization of the satellite communication system network is more suitable than terrestrial networks to be applied in Indonesia with archipelagic areas because it has a large number of point distributions with wider coverage. The use of artificial intelligence with deep learning techniques such as one dimensional convolutional neural network (1D-CNN) is also very promising to estimate rainfall intensity because it has a high accuracy of 93%..
Communication Satellite-Based Rainfall Estimation for Flood Mitigation in Papua Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8409

Abstract

Papua, an equatorial region in Indonesia, faces unique geographical and natural challenges, including heavy annual rainfall. This heavy rainfall increases flooding risks and impacts infrastructure, the economy, and daily life. Despite the importance of rain gauges for monitoring floods and climate change, Papua's difficult geography and limited transportation infrastructure hinder their installation and maintenance. In this work, we investigate a deep learning one-dimensional convolution neural network (1DCNN) model to estimate rainfall intensity using energy per symbol to noise power density ratio (Es/No) of the signals received from a communication satellite signal coupled with additional data representing satellite daily movement. The findings of this study demonstrate that the performance of the proposed model has a higher accuracy for moderate to heavy rainfall than for light rainfall. The NRMSE values for light rain, moderate rain, and heavy rain are 47.09, 31.78, and 33.58%, respectively. These results show that this method is promising for monitoring heavy rainfall as a flood mitigation effort. However, there is still room to improve the accuracy of the estimation such as using other secondary data that is highly correlated with rain at the satellite transceiver location.
Flatline Anomaly Detection in Automatic Weather Station Air Temperature Sensor Data Using LSTM Autoencoder Supriyatna; Santoso Soekirno; Martarizal; Djati Handoko
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14486

Abstract

The quality of air temperature data from Automatic Weather Stations (AWS) is crucial for meteorological analysis, climatology, and early warning systems. However, flatline anomalies, a condition where sensor values ​​tend to remain constant over a period of time, can degrade data quality and are often not optimally detected by conventional rule-based quality control (QC) methods. Previous research is also limited in specifically examining flatline detection, with most studies focusing on general anomalies and not integrating deep learning approaches with operational quality control systems. This study proposes a data-driven approach using a Long Short-Term Memory Autoencoder (LSTM-AE) combined with Level-1 QC. The novelty of this study lies in the use of a normal-only training scheme, anomaly threshold determination based on the reconstruction error distribution, and post-detection diagnosis to identify flatline characteristics. The methods include QC filtering, sliding window formation, model training, threshold determination, and anomaly detection. The results show stable model performance with an anomaly threshold value of 0.01177 (MSE). Of the 985,730 data windows, approximately 0.578% were detected as anomalies, indicating that flatline occurrences are relatively small but still significant to data quality. Most anomalies are short-lived and discontinuous, indicating localized sensor noise. This study demonstrates that LSTM-AE is effective as an adaptive flatline detection method and has the potential to be implemented as an automated QC module in AWS systems to improve data reliability.
Community Structure of Microalgae in Urban Small Lakes (Babakan, Kenanga, and Puspa) Under Anthropogenic Noise Variation: Implications for Biofuel Raw Material Rubiantin Mesha Nauli Tambunan; Nining Betawati Prihantini; Santoso Soekirno; Nasruddin
Al-Kauniyah: Jurnal Biologi Vol. 19 No. 2 (2026): AL-KAUNIYAH: JURNAL BIOLOGI
Publisher : Department of Biology, Faculty of Science and Technology, Syarif Hidayatullah State Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/kauniyah.v19i2.47095

Abstract

The global energy transition relies on renewable solutions such as microalgae. However, the diversity of microalgae in urban freshwaters, especially how anthropogenic noise affects them, remains unexplored. Our study explores three small lakes in Greater Jakarta—Babakan (South Jakarta), Puspa, and Kenanga (Universitas Indonesia, Depok, West Java)—that are becoming more exposed to anthropogenic noise, an evolving stressor with uncertain ecological effects on microalgae. Microalgae were identified from water samples collected from Babakan, Kenanga, and Puspa lakes, which had sound levels of -69.37 dB, -81.49 dB, and -99.43 dB, respectively. The results show that Kenanga Small Lake has the highest diversity (H' 2.44), while Puspa Small Lake has the highest dominance (D = 0.16) and evenness (E = 0.88). The "silent" Puspa Lake has the highest lipid percentage, which was 51.66%. While all environmental factors are interconnected to affect microalgal community structure and lipid percentage, these data point to a possible link between lower noise levels and better lipid yields, implying that indigenous microalgae from calmer urban contexts could be a feasible source of sustainable biofuel production.
Rainfall detection on a tropical island using a communication satellite downlink signal and artificial intelligence models Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto; Ferry Budhi Susetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.26012

Abstract

The development of rainfall estimates based on satellite links in several high latitude regions shows good results. However, its development for tropical regions is still very less promising, leaving the question of which method is worth using. In this work, we investigate artificial intelligence (AI) models by utilizing the Ku-band satellite downlink signal for rainfall detection on a tropical island. Long-term evaluation of all the models is performed using one year of rainfall intensity data. The findings of this study demonstrate that one-dimensional convolution neural network (1DCNN), long short-term memory (LSTM), and random forest (RF) models can effectively identify rainfall occurrences on tropical islands, with the area under the receiver operating characteristic curve (ROC) value of 0.84, 0.84, and 0.83, respectively. These three models consistently provide better accuracy in detecting the region’s diverse rainfall patterns throughout the year. The 1DCNN has better performance compared to the other models, achieving accuracy values of 0.76 and 0.82 during the periods of lowest and highest rainfall, respectively. Considering the presence of numerous potential mislabeled rain samples in the dataset, this result is excellent. In addition, the utilization of higher resolution rainfall intensity data can be a great potential for real-time rainfall monitoring in the tropical region.
Forecasting particulate matter concentration using deep learning and weather data Ahmad Furqon; Santoso Soekirno; Naufal Ananda; Haryas Subyantara Wicaksana; Ahmad Meijlan Yasir; Tonny Wahyu Aji; Aviv Maghridlo; Fadhli Luthfiansyah; Marzuki Sinambela; Suko Prayitno Adi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27967

Abstract

Particulate matter (PM2.5) concentration is a critical air quality indicator with severe health impacts. Predicting its dynamics is challenging due to complex interactions with meteorological variables. This study evaluates long short-term memory (LSTM) and multilayer perceptron (MLP) networks to identify the most accurate forecasting architecture. We integrated continuous data from automatic weather stations (AWS) and air quality sensors. Data preprocessing involved temporal synchronization, outlier removal, and multivariate imputation by chained equations (MICE). Key input features past particulate matter, temperature, and relative humidity were selected via Pearson correlation and trained using a 24-hour sliding window. Results demonstrate that the single LSTM model outperforms both the MLP and hybrid architectures. The LSTM achieved the lowest root mean square error (RMSE) of 6.862 µg/m³ and a mean absolute percentage error (MAPE) of 37.38%. Although the MLP yielded a slightly higher coefficient of determination (0.810), it exhibited significant magnitude bias by treating sequential data as static features. Ultimately, this research provides a robust, data-driven framework for real-time air quality early warning systems in tropical regions.
An IoT-Based Smart Onboard Weather Station for Maritime Situational Awareness: Calibration, Field Representativeness, and Exploratory Usability Evaluation Santoso Soekirno; Martarizal; Ahmad Furqon; Naufal Ananda
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1263

Abstract

Rapidly varying marine weather requires onboard observations that combine measurement quality, spatial context, continuous data delivery, and clear operational interpretation. This study evaluates an Internet of Things (IoT)-based Smart Onboard Weather Station through controlled calibration, moving-vessel field comparison, preliminary data-continuity assessment, and exploratory usability evaluation. The platform is intended to support bridge officers during onboard weather monitoring and shore-based BMKG personnel reviewing vessel observations; the present usability sample represents the latter professional group. The system integrates a Campbell Scientific ClimaVUE sensor, CR1000X datalogger, GPS receiver, digital compass, camera, LTE/4G communication, time-series storage, and a web dashboard. Calibration across three to five set points produced near-linear responses for air temperature (R² = 0.999), relative humidity (R² = 0.995), atmospheric pressure (R² = 0.990), wind speed (R² = 0.999), and wind direction (R² = 0.990), together with parameter-specific correction and dispersion information. A seven-day deployment on the Merak–Bakauheni route from 21 to 27 December 2025 showed the strongest spatial coherence for atmospheric pressure and useful temporal agreement for air temperature, while wind speed produced the largest vessel-to-coastal differences. Data completeness reached 96.75%, representing preliminary operational data continuity under the tested conditions. The archived distance-summary dataset preserves interval-level statistics, while paired-observation counts fall outside the retained metadata and are carried as a reporting limitation. An exploratory SUS assessment with nine meteorological professionals produced a mean score of 81.39. The next readiness step is a controlled crewed pilot on a second vessel, with wind processing and bridge-user evaluation as the primary operational gates.
Co-Authors Ahmad Furqon Ahmad Meijlan Yasir Aviv Maghridlo Budhy Kurniawan Danang Eko Nuryanto Danang Eko Nuryanto Fadhli Luthfiansyah Handoko, Djati Haryas Subyantara Wicaksana K, Aprilia Sakti Martarizal Marzuki Sinambela Nasruddin Naufal Ananda Naufal Ananda Nining Betawati Prihantini R Yudha Mardyansyah Raden Yudha Mardyansyah Rubiantin Mesha Nauli Tambunan Suko Prayitno Adi Supriyatna Susetyo, Ferry Budhi Tonny Wahyu Aji