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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Renewable Energy and Smart Device
Ahmad Furqon
Universitas Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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 Meijlan Yasir Aviv Maghridlo Fadhli Luthfiansyah Haryas Subyantara Wicaksana Martarizal Marzuki Sinambela Naufal Ananda Naufal Ananda Santoso Soekirno Suko Prayitno Adi Tonny Wahyu Aji