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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Journal of Information Technology and Computer Science Jurnal Teknik Industri Terintegrasi (JUTIN) Kawanua International Journal of Multicultural Studies Journal of Embedded Systems, Security and Intelligent Systems Journal of Mathematics and Applied Statistics
Biabdillah, Fajerin
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta Triadi, Fara; Jaya, Arsan Kumala; Biabdillah, Fajerin; Hanif, Abdul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10595

Abstract

Urban traffic emissions continue to escalate in Southeast Asian megacities, particularly along oversaturated central business district corridors where chronic congestion amplifies pollutant accumulation. Previous research often separates statistical emission modelling from microscopic simulation, limiting the ability to evaluate policy impacts under real-world saturation conditions. This study aims to assess whether lane-level transport interventions specifically bus-only lanes and motorcycle restrictions can reduce emissions in a hyper-congested Jakarta corridor through an integrated analytical approach. A hybrid regression–microsimulation framework was developed by combining multiple linear regression with SUMO-based traffic simulation. An hourly dataset of traffic flow and CO emissions (n = 8,760) from the Thamrin–Bundaran HI corridor was used to construct a regression model enriched with temporal and lagged predictors. The resulting emission profiles were embedded into SUMO to simulate baseline, bus-lane, and motorcycle-restriction scenarios. The regression model achieved strong predictive performance (R² = 0.692, RMSE = 0.252), with CO_lag1 confirmed as the dominant predictor. Simulation results showed fully overlapping CO₂ emission trajectories across all scenarios, indicating that lane-based interventions do not alter traffic states or emissions under oversaturated conditions. Structural congestion constrains the effectiveness of lane-level policies. Meaningful emission reductions require systemic strategies such as demand management, modal shift, or network redesign. The proposed hybrid framework provides a replicable tool for evaluating transport policies in dense urban corridors
Sentiment-Aware Transformer for Cryptocurrency Volatility Prediction Using Multi-Source Market and News Sentiment Biabdillah, Fajerin; Triadi, Fara; Go , Aeltri Jeacfky Gozal; Ramadhan, Muhammad Cahyo Putra
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10604

Abstract

The cryptocurrency market has grown into a multi-trillion-dollar domain with extreme volatility. This paper addresses the forecasting of crypto price movements and volatility by integrating market metrics with sentiment analysis. We identify a gap in existing studies, which often ignore multi-source sentiment and thus miss early warning signs of volatility. We propose a Sentiment-Aware Transformer model inspired by the Temporal Fusion Transformer (TFT). The model ingests daily price, volume, and market cap features from CoinMarketCap alongside aggregated sentiment scores from Twitter, Reddit, and financial news (extracted via FinBERT). We train and evaluate the model on 5 years of data for 10 major cryptocurrencies (2020–2024), comparing performance against LSTM and GRU baselines with identical inputs. The proposed Transformer achieves 83.2% volatility prediction accuracy with an F1-score of 0.81, exceeding the LSTM (79% accuracy) and GRU (80%) baselines. It also shows the lowest RMSE in price forecasting and a higher return correlation (0.72) with actual prices, indicating improved trend alignment. These gains are statistically significant (p<0.01). We also discuss how attention weights offer interpretability, as the model focuses on sentiment spikes during impending volatility.
Co-Authors Agusma Wajiansyah, Agusma Awaluddin A., Muhammad Fitra Abdurrachman Bachtiar Go , Aeltri Jeacfky Gozal Hanif, Abdul Hanif, Abdullah Hartanto, Subhan Herman Tolle Ismayanti, Rika Jaya, Arsan Kumala Jeacfky Gozal Go, Aeltri Maesaroh, Dwi Titi Maknuniah, Jauharatul Ramadhan, Muhammad Cahyo Putra Syahidi, Aulia Akhrian Tajuddin, Muhammad Triadi, Fara