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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Natural Science and Engineering Journal of Applied Data Sciences Jurnal Masyarakat Indonesia The Indonesian Journal of Computer Science
Poltak Sihombing
FMIPA USU, Medan
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Journal of Applied Data Sciences

 1 
Mathematical Modeling of Water Quality Dynamics in Aquaculture: A Foundation for IoT Integration and Machine Learning-Driven Predictive Analytics Sarif, Muhammad Irfan; Efendi, Syahril; Sihombing, Poltak; Mawengkang, Herman
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.819

Abstract

Effective water quality management is paramount for sustainable aquaculture, yet conventional methods often fall short in providing timely and predictive insights. This paper details the development and analysis of a comprehensive suite of mathematical models designed to simulate key water quality dynamics in aquaculture systems. These models encompass critical biogeochemical processes, including the nitrogen cycle (ammonia, nitrite, nitrate, organic nitrogen), phosphorus cycle, Dissolved Oxygen (DO) balance, and Biochemical Oxygen Demand (BOD). Simulation results derived from these models illustrate the temporal evolution of these critical parameters, demonstrating their capability to capture complex interactions and provide a mechanistic understanding of the aquatic environment. This foundational modeling approach offers a robust tool for quantitative analysis and prediction of system responses under various conditions. The core contribution of this work is the articulation of these mathematical models, which serve as a crucial foundation for advanced, data-driven aquaculture management. To enhance their practical utility, we propose a conceptual framework for integrating these models with Internet of Things (IoT) sensor networks. Real-time data acquisition via IoT can be essential for model parameterization, continuous calibration, and validation against operational conditions. Furthermore, this paper discusses how outputs from these validated mechanistic models can serve as robust inputs for Machine Learning (ML) algorithms. This synergy enables the development of sophisticated predictive analytics for critical events, such as forecasting water quality deterioration, and supports optimized, proactive management strategies. This research lays the theoretical and methodological groundwork for developing more precise and resilient decision support systems in aquaculture. By emphasizing the synergistic potential of combining foundational mathematical modeling with data science techniques like IoT and ML, this work aims to contribute to transforming aquaculture into a more productive, sustainable, and environmentally responsible industry. Future efforts should focus on empirical validation and the practical implementation of the proposed integrated framework.
An IoT-Enabled Smart System Utilizing Linear Regression for Sheep Growth and Health Monitoring Efendi, Syahril; Sihombing, Poltak; Mawengkang, Herman; Turnip, Arjon; Weber, Gerhard Wilhelm
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.901

Abstract

The global livestock industry faces significant pressures from climate change, land constraints, and rising consumer demand, necessitating greater efficiency and sustainability in production. To address these challenges, there is a critical need for accessible, data-driven tools; however, accessible and individualized tools for monitoring the growth and health of livestock like sheep remain underdeveloped, limiting farmers' ability to transition from reactive to proactive management. This study developed and validated an Internet of Things (IoT) smart system for monitoring sheep using an Arduino and ESP32 platform equipped with a DHT22 sensor for temperature and humidity and a load cell for weight. Weekly weight data from 15 sheep were collected over a six-month period. Simple linear regression was then applied to model the individual growth trajectory of each animal. The IoT system was successfully implemented and deployed in a farm setting. The primary finding was that individualized linear regression models provided a highly accurate method for tracking sheep growth, with R² values consistently exceeding 99% for most animals. The system effectively delivered real-time reports on growth trajectories and health-relevant environmental conditions (e.g., temperature and humidity) to a smartphone interface, confirming its practical utility. The primary implication of this research is a validated framework for practical and interpretable precision livestock farming. The system empowers farmers to shift from reactive to proactive management by using individualized growth curves as baselines for early problem detection. This dual-function system enhances productivity through precise growth tracking while supporting animal welfare via environmental monitoring, offering a valuable tool for modern, sustainable sheep farming.
SAGOMECON: An Adaptive ε-Constraint-Based Optimization Method for Multi-Criteria Decision-Making in Collaborative Industrial Networks Mesran, M; Sihombing, Poltak; Efendi, Syahril; Zarlis, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.941

Abstract

In collaborative industrial networks, decision-making processes often involve conflicting objectives such as minimizing operational costs and risks, while simultaneously maximizing efficiency and inter-organizational collaboration. Existing multi-objective optimization methods, including the ε-constraint and its variants, face significant challenges in handling dynamic constraints and achieving computational efficiency in real-world scenarios. To address these limitations, this study introduces SAGOMECON (Simplified Adaptive Optimization with Modified ε-Constraint), an enhanced optimization approach designed to support adaptive and efficient multi-criteria decision-making in dynamic environments. SAGOMECON extends the conventional SAUGMECON framework by incorporating real-time constraint updates, adaptive slack handling, and iterative refinement mechanisms, enabling it to maintain solution feasibility under shifting priorities and evolving operational conditions. The proposed method was evaluated using simulated datasets representing partner selection scenarios in collaborative networked organizations (CNOs). Comparative analysis against the traditional ε-constraint and SAUGMECON methods demonstrates that SAGOMECON consistently delivers Pareto-optimal solutions with reduced computational time and superior adaptability to dynamic changes. The findings suggest that SAGOMECON offers a practical and scalable solution for decision-makers in collaborative industrial settings, particularly where trade-offs between competing objectives must be navigated under uncertainty. This contribution is significant for industries seeking intelligent optimization strategies that align with agile and data-driven decision-making frameworks.
Co-Authors Andri Budiman, Mohammad Arjon Turnip Efendi, Syahril Erna Budhiarti Nababan Fahmi Fahmi Fauzan Nurahmadi Ghazali, Alfin Gunawan Gunawan Herman Mawengkang Jayawarsa, A.A. Ketut Manurung, Jonson Maya Silvi Lydia Mesran, Mesran Muhammad Zarlis, Muhammad Naemah Mubarakah Opim Salim Sitompul Sarif, Muhammad Irfan Sawaluddin Sawaluddin, Sawaluddin Siti Sarah Harahap Sutarman Sutarman Weber, Gerhard Wilhelm