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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) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Natural Science and Engineering Indonesian Physical Review Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknik Informatika (JUTIF) 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Physics

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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.
APPLICATION OF SUPPORT VECTOR MACHINE ON DROUGHT CODE CLASSIFICATION IN NORTH SUMATRA INDONESIA Butar Butar, Kartika Dewi; Sihombing, Poltak; Tulus, Tulus
Indonesian Physical Review Vol. 6 No. 1 (2023)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v6i1.196

Abstract

This study aims to classify the Drought Code in North Sumatra. Drought Code is part of Fire Danger Rating System (FDRS) developed in Canada. One of the sub-systems of FDRS is Fire Weather Index (FWI) that aims to evaluate fire hazards from current and past weather conditions. Drought Code is classified by using Support Vector Machine. Support Vector Machine is widely used in the data classification process. One of the advantages of Support Vector Machine methods is it has ability in classifying large amount of data and classifying more than two classes or multi-classes.  Weather parameters used in this study are rainfall and temperature in North Sumatra. The data used are from 8 (eight) meteorological observation stations in North Sumatra from 2017 to 2021. Drought Code is carried out with several tests using several kernels contained in SVM.
Evaluating the Impact of Model Complexity on the Accuracy of ID3 and Modified ID3: A Case Study of the Max_Depth Parameter Asrianda, Asrianda; Mawengkang, Herman; Sihombing, Poltak; K. M. Nasution , Mahyuddin
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4864

Abstract

The complexity of decision tree structures has a direct impact on the generalization capability of classification algorithms. This study investigates and evaluates the performance of the classical ID3 algorithm and its modified version in the context of tree depth. The primary objective is to identify the optimal accuracy point and assess the algorithms' robustness against overfitting. Experiments were conducted across tree depths ranging from 1 to 20, with accuracy used as the main evaluation metric. The results indicate that both algorithms achieved peak performance at depth 3, followed by a notable decline. While the classical ID3 algorithm exhibited a gradual decrease in accuracy, the modified ID3 showed a sharp drop and performance stagnation between depths 11 and 20. These findings suggest that the modified ID3 algorithm enhances sensitivity in selecting informative attributes but also increases the risk of overfitting in the absence of structural regularization mechanisms. Therefore, the study recommends the implementation of regularization strategies such as pruning and cross-validation to mitigate performance degradation caused by model complexity. This research not only contributes to the theoretical understanding of how tree depth influences classification performance but also offers practical insights for developing adaptive, stable, and accurate decision tree-based classification systems.
Etika Profesionalitas Polres Jayawijaya Dalam Pelayanan Laporan Pengaduan Dan Pelayanan Administrasi Sihombing, Poltak; Ari Juliana; Abner H.Bajari
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 4 (2025): Jurnal Ekonomi Manajemen Sistem Informasi (Maret - April 2025)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i4.4440

Abstract

Potret buruk Polres Jayawijaya berdasarkan indeks kepuasan masyarakat terhadap pelayanan publik seperti pungli dan penelantaran kasus pidana yang berdampak pada rendahnya kepuasan masyarakat terhadap pelayanan publik. Polres Jayawijaya dituntut untuk memastikan penegakan hukum terlaksana secara kondusif seperti menindaklanjuti laporan tindak pidana masyarakat untuk memberikan rasa nyaman secara psikologis dan melayani kebutuhan masyarakat secara inklusif tanpa memandang suku, ras, agama, dan jenis kelamin. Penelitian ini bertujuan untuk menganalisis etika profesionalisme dalam penanganan laporan dan penyelenggaraan layanan SIM serta menganalisis upaya yang dilakukan dalam peningkatan pelayanan Polres Jayawijaya. Penelitian ini menggunakan metode deskriptif kualitatif, pengumpulan data melalui wawancara di Polres Jayawijaya. Sebanyak 13 narasumber dipilih sebagai key informan. Data dianalisis dengan menggunakan teori birokrasi Weber, indikator yang dianalisis terdiri dari etika kepribadian, sosial, kelembagaan, dan bernegara dengan. Data dianalisis dengan proses reduksi data, penyajian, dan penarikan simpulan. Etika profesi dalam penanganan laporan menurut adat istiadat yang berhasil diidentifikasi oleh peneliti terdiri dari etika kenegaraan, etika sosial, etika kepribadian dan etika kelembagaan dimana anggota Polres Jayawijaya tidak terlibat dalam lembaga swadaya masyarakat manapun. Etika profesi dalam penyelenggaraan layanan SIM adalah etika kenegaraan dan etika sosial. Upaya peningkatan pelayanan publik mengacu pada kode etik profesi yang dilakukan oleh penyelenggara organisasi dan menggunakan sistem digitalisasi dalam layanan pengaduan dan layanan SIM, namun upaya tersebut belum mampu meningkatkan pelayanan publik dalam menekan praktik pungli.
Development of distance formulation for high-dimensional data visualization in multidimensional scaling Marto Hasugian, Paska; Mawengkang, Herman; Sihombing, Poltak; Efendi, Syahril
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8738

Abstract

This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Advancements in Detection Top Influencer Marketing in the Airline Industry: A Combination of the Leiden Algorithm and Graph Coloring Handrizal, Handrizal; Sihombing, Poltak; Budhiarti Nababan, Erna; Andri Budiman, Mohammad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3440

Abstract

In recent years, the airline industry has increasingly utilized social media and online platforms to engage customers and enhance brand loyalty. Identifying key influencers within these networks is crucial for optimizing marketing strategies and improving customer engagement. Influencers play a pivotal role in shaping opinions, driving behaviors, and amplifying brand messages within social networks. Consequently, efficient methods for detecting influencers are essential for understanding network dynamics and maintaining a competitive edge. This study introduces a novel contribution to the field of social network analysis by proposing the Leiden Coloring Algorithm, an enhancement of the traditional Leiden algorithm that integrates graph coloring techniques. The scientific contribution of this research lies in improving the precision of community detection and computational performance in large-scale networks. Experimental results on five airline-related datasets demonstrate that the proposed method achieves higher modularity (average 0.9375), faster processing time (average 204.88 seconds), and identifies fewer, more cohesive communities compared to the Louvain Coloring Algorithm. These findings highlight the algorithm's effectiveness in influencer detection and its potential application in community detection, marketing optimization, and strategic decision-making within the airline industry.
Co-Authors Abner H.Bajari Andri Budiman, Mohammad Ari Juliana Arjon Turnip Asrianda Asrianda Budhiarti Nababan, Erna Butar Butar, Kartika Dewi Efendi, Syahril Erna Budhiarti Nababan Fahmi Fahmi Fauzan Nurahmadi Ghazali, Alfin Gunawan Gunawan Handrizal Handrizal Hasugian , Paska Marto Herman Mawengkang Jayawarsa, A.A. Ketut K. M. Nasution , Mahyuddin 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 Tulus Tulus Weber, Gerhard Wilhelm