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All Journal Indonesian Journal of Data and Science Journal of Soft Computing Exploration
Alfian, Muhammad Arif
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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An Optimization Strategy for Reducing CO₂ in Livestock Farming with IoT Integration and Decision Support System Approach Using Linear Programming Shimbun, Annisa Fikria; Alfian, Muhammad Arif; Jati, Agam Saka; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.204

Abstract

Introduction: Livestock waste mismanagement contributes significantly to CO₂ emissions, adversely affecting animal health and environmental sustainability. This study aims to develop an optimization strategy for reducing CO₂ levels in livestock environments through the integration of Internet of Things (IoT) technology and a Decision Support System (DSS) using Linear Programming. Methods: IoT sensors were deployed to monitor environmental parameters such as CO₂ levels, temperature, and humidity in real time. A Linear Programming (LP) model was formulated to determine the optimal frequency of two CO₂-reducing actions: spraying Effective Microorganisms (EM4) and performing waste dredging. The objective was to maximize CO₂ reduction under cost and time constraints. The model iteratively updated its parameters based on sensor data feedback, ensuring dynamic and adaptive optimization. Results: Simulation results indicated that the LP model successfully identified optimal actions within predefined constraints. The optimal strategy was spraying EM4 eight times over eight days, achieving a CO₂ reduction of 800 ppm with a total cost of Rp 400,000—within the Rp 500,000 budget limit and 8-hour duration constraint. Validation through simulation confirmed the model’s accuracy, with prediction deviations consistently falling within an acceptable threshold (±20 ppm). Conclusions: The integration of IoT with an LP-based DSS offers a practical and efficient solution for CO₂ reduction in livestock farming. This system enhances decision-making for environmental management, demonstrating potential for scalable application in sustainable agriculture. Future work should incorporate more environmental variables and broader validation to improve model generalizability and precision.
LSTM with temporal encoding for irregular time series forecasting in power consumption Verianto, Eko; Alfian, Muhammad Arif
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.2

Abstract

Power consumption data obtained from sensors are often recorded at irregular time intervals due to network disruptions, device errors, or power outages, resulting in irregular time series that make forecasting difficult. This study aims to develop an electricity consumption forecasting model based on Long Short-Term Memory (LSTM) and Temporal Encoding.  LSTM was chosen because it has an effective gating mechanism for capturing temporal dependencies in time series data, while Temporal Encoding explicitly represents time information to handle irregular time intervals without data imputation.  The methods in this study include data collection via four electrical current sensors, followed by data aggregation every 10 minutes, and feature engineering using sinusoidal encoding and a time difference encoder. The features were normalized using min-max scaling, organized into a multivariate sequence using a sliding window, and divided using a holdout scheme. The model was trained using LSTM and evaluated using Mean Squared Error (MSE). The results show training MSE values of 9.89210-4, 7.34910-4, 9.53510-4 and 1.90610-3, while the testing MSE values are 4.56610-3, 2.99310-3, 1.09410-2 and 1.20910-2 for each node. These findings indicate that temporal encoding performs well on the training data, but the model's generalization ability remains limited.
Co-Authors Edi Faizal Jati, Agam Saka Shimbun, Annisa Fikria Verianto, Eko