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All Journal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rifkial Iqwal
Universitas Malikussaleh
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Food Security Optimization Forecasting Fertilizer Production With Method Weighted Moving Average (WMA) Rifkial Iqwal; Dahlan Abdullah; Nunsina
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This research focuses on optimizing food security through the application of fertilizer production forecasting method at PT Pupuk Iskandar Muda (PIM) using Weighted Moving Average (WMA). Effective food security relies heavily on stable and adequate fertilizer availability, which in turn requires accurate production predictions to ensure efficiency. In this study, historical data of urea and ammonia fertilizer production from January 2019 to December 2023 is used to build a forecasting model that can provide an overview of future production trends. The WMA method was chosen due to its adaptive nature, where greater weight is given to the most recent data, allowing the model to be more responsive to changes and emerging trends. The results showed that for urea production, WMA produced a MAPE value of 1773.8% and MAD of 13,223.2, while for ammonia production, the MAPE was recorded at 3085.5% with MAD of 7,538.5. Total production showed a MAPE of 69.7% with a MAD of 20,568.9, indicating significant fluctuations in production during the period under study. Nevertheless, the WMA method still provides a fairly good prediction and can be used as a reference in future production planning. In addition, the results of this study also provide valuable insights into the production dynamics at PIM, which is critical in supporting the national food security strategy. This research recommends further exploration of other more advanced forecasting methods, such as ARIMA or machine learning techniques, to improve prediction accuracy and better anticipate changes in production patterns. Keywords: Food security, Weighted Moving Average, Fertilizer Production Forecasting, MAPE, MAD.
Development of Portable IoT-Based Fish Pond to Enhance Freshwater Aquaculture Efficiency Rifkial Iqwal; M Ishlah Buana Angkasa; Nazwa Aulia; Subhan Hartanto; Tejas Shinde; Muhammad Fikry; Zara Yunizar
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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This paper presents the development of iPooL, a portable Internet of Things (IoT)-based fish pond system designed to optimize freshwater fish farming, particularly in resource-constrained and urban environments. By integrating real-time monitoring of essential water parameters—such as pH, temperature, dissolved oxygen, and ammonia levels—iPooL ensures that optimal environmental conditions are maintained for fish health and growth. The system employs IoT sensors connected to an ESP32 microcontroller, which processes and transmits data to a cloud platform, enabling farmers to receive real-time alerts and manage their ponds via a mobile app. Field trials demonstrated that the iPooL system reduces fish mortality by 20% and improves fish growth rates by maintaining stable water conditions. Additionally, the automation of feeding schedules and water management reduces operational costs, particularly in labor and feed, resulting in a 30% increase in profitability. With an estimated return on investment (ROI) within one year, iPooL offers a cost-effective solution for both small- and medium-scale fish farmers. The system also promotes environmental sustainability by optimizing water usage and reducing the need for chemical additives. Its portability allows fish farming in non-traditional environments, such as urban rooftops, contributing to decentralized food production and reducing the environmental impact of transporting fish to urban markets. iPooL’s scalability, combined with future integration of artificial intelligence and renewable energy sources, positions it as a transformative tool for the aquaculture industry, supporting both economic development and sustainable farming practices.
Co-Authors Dahlan Abdullah M Ishlah Buana Angkasa Muhammad Fikry Nazwa Aulia Nunsina, Nunsina Subhan Hartanto Tejas Shinde Zara Yunizar