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Carbon Emission Simulation at the Slamet Riyadi Three-Way Intersection in Samarinda City Using Urban Mobility Simulation Arsan Kumala Jaya; Fara Triadi; Abdullah Hanif
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8212

This study uses Simulation Urban Mobility (SUMO) to simulate carbon emissions at the Slamet Riyadi Samarinda intersection. One of the big cities in Indonesia, Samarinda, faces major problems related to increasing traffic volume and its impact on air quality, especially carbon emissions. This simulation uses various traffic parameters, such as vehicle density, red light duration, and vehicle type. The purpose of this simulation is to evaluate the level of carbon emissions produced by vehicles passing through the intersection. The simulation begins by collecting traffic data and then converting it into an XML file that can be read by SUMO. This XML file contains information about traffic parameters, road networks, and vehicles. According to the simulation results, trucks contributed the most emissions per vehicle, with a total emission of almost 18,500 grams of CO₂ per hour. Traffic scenarios under real-world conditions were simulated using the SUMO tool with HBEFA-based emission models.. This study is expected to provide a clearer picture of the impact of traffic on the environment as well as recommendations for more effective traffic management strategies to reduce carbon emissions in Samarinda City.

Agile-Based Accreditation Module Design for the P3M Information System at Politeknik Negeri Samarinda Arsan Kumala Jaya; Fara Triadi; Subhan Hartanto; Ahmad Saiful Mutaqi Azis; Priti Shinta
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.v6i4.10320

Accreditation serves as a critical quality assurance mechanism in higher education; however, manual and fragmented data management creates significant challenges in collecting accreditation documentation and reports. This research designs and develops an Accreditation Menu for the P3M Information System at Politeknik Negeri Samarinda to streamline accreditation processes with greater effectiveness, efficiency, and accountability. Using an iterative Agile Scrum methodology across five development sprints, the study implemented integrated CRUD operations, advanced search-filtering capabilities, real-time notification systems, comprehensive user acceptance testing, and Docker-based deployment. Key results demonstrate that the Accreditation Menu reduces document preparation time by 40%, improves data accuracy from 88% to 97%, and achieves 92% user satisfaction (UAT survey, n=25 stakeholders). The system successfully manages accreditation indicators, supporting documentation, and reporting in full compliance with LAM INFOKOM standards while providing real-time data integration between research activities and accreditation requirements. This work improves accreditation efficiency, reduces administrative burden, and supports institutional compliance with national quality assurance standards. The Agile approach enables rapid adaptation to evolving user needs and regulatory changes, with promising opportunities for AI-based predictive monitoring and integration with national accreditation systems.

Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta Fara Triadi; Arsan Kumala Jaya; Fajerin Biabdillah; Abdul Hanif
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

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 Fajerin Biabdillah; Fara Triadi; Aeltri Jeacfky Gozal Go; Muhammad Cahyo Putra Ramadhan
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

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.

Fire Detection and Room Firefighting System Based on IoT Using C4.5 Decision Tree Algorithm Rika Ismayanti; Fara Triadi; Arsan Kumala Jaya; Ade Irawan
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.v6i4.10685

Early fire detection is a critical requirement in indoor safety systems, where delays of only a few seconds can escalate into severe damage and casualties. Conventional devices often rely on single-sensor thresholds, which are highly susceptible to false alarms and unstable performance in dynamic indoor environments. This study develops an Internet of Things (IoT)-based multi-sensor fire detection and autonomous firefighting system integrated with a C4.5 decision tree classifier for real-time hazard recognition and short-term risk prediction. The prototype combines DHT22 temperature, MQ-135 gas, infrared flame, and ultrasonic water-level sensors with an ESP32 microcontroller, servo-controlled nozzle, and pump-based water spraying, all connected to an Android–Firebase platform for remote monitoring. A multivariate time-series dataset of 200 sensor sequences was preprocessed using a five-step sliding-window model and evaluated through 1,000 repeated hold-out trials. The C4.5 classifier achieved a mean accuracy of 84.9%, with peak values exceeding 90%, and clearly separated Safe, Alert, and Danger states, with smoke concentration emerging as the dominant predictor. Experimental tests in a 60 × 40 × 30 cm chamber produced 1–2 s reaction times, eight successful extinguishing events, and four failures attributable to mechanical belt detachment rather than model errors. These findings indicate that interpretable decision-tree models, when combined with IoT sensing and autonomous actuation, can provide a low-cost framework for real-time fire warning and automatic suppression. Future work should address mechanical robustness, extended deployment, and multi-room scalability

Vision-AI Roadside Unit Simulator with SUMO Integration for Visually Impaired Mobility Optimization Bambang Cahyono; Fara Triadi; Karyo Budi Utomo
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2625

Purpose – This study develops and evaluates a low-cost Vision-AI-based roadside unit simulator integrated with SUMO to support adaptive traffic signal control and safer crossing for visually impaired pedestrians. Design/methods/approach – The proposed system combines a Raspberry Pi 4 edge platform, a camera sensor, YOLOv5s-based vehicle detection, and SUMO traffic simulation through the TraCI interface. Three control strategies were compared: fixed-time control, SUMO adaptive control, and the proposed Vision-AI-assisted control. Experiments were conducted under low, medium, and high traffic density scenarios, with 30 simulation runs for each condition. Performance was measured using average waiting time, queue length, travel time, pedestrian crossing success rate, detection accuracy, latency, and statistical significance testing. Findings - The proposed system outperformed both baseline methods across all scenarios. It reduced vehicle waiting time by up to 41%, queue length by approximately 35%, and travel time by around 22% compared with fixed-time control. The assistive crossing mechanism increased pedestrian crossing success from 62% to 93%. The edge platform achieved 18–22 FPS, latency below 85 ms, and mAP@0.5 of 0.87. Research implications/limitations – The findings demonstrate the feasibility of low-cost edge-based intelligent transportation systems, although validation remains limited to simulation and a single-intersection case. Originality/value – This study integrates Vision-AI traffic perception, adaptive signal control, and accessibility-aware pedestrian support within one simulation framework.

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