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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Bulletin of Electrical Engineering and Informatics Jurnal Infinity Journal of Telematics and Informatics SAMUDERA Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi Informasi dan Komunikasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Jurnal Inotera MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISTech (Journal of Islamic Science and Technology) Building of Informatics, Technology and Science Jurnal Mantik MES: Journal of Mathematics Education and Science Jurnal Varian International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Journal of Innovation Information Technology and Application (JINITA) Randwick International of Social Science Journal Journal of Research in Mathematics Trends and Technology Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Journal for Lesson and Learning Studies International Journal of Humanities Education and Social Sciences Jurnal MathEducation Nusantara International Journal of Community Service Implementation Jurnal Infinity
Herman Mawengkang
Faculty Of Matthematics And Natural Science Universitas Sumatera Utara, Medan, 20155, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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THE DIFFERENCE OF STUDENTS' ACHIEVEMENT IN MATHEMATICS BY USING GUIDED-DISCOVERY LEARNING MODEL AND COOPERATIVE LEARNING MODEL JIGSAW TYPE Anna Angela Sitinjak; Herman Mawengkang
Jurnal Infinity Vol 7 No 1 (2018): Volume 7 Number 1, INFINITY
Publisher : IKIP Siliwangi and I-MES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22460/infinity.v7i1.p45-54

Abstract

The type of this study is a quasi-experiment study with its purpose to know any difference in students' achievement in mathematics which using the model of guided discovery learning with cooperative learning model JIGSAW type. The population of this study is all students in SMA N 3 P. Siantar. The sampling technique applied was cluster random sampling. The experimental class I that chosen is X-1 consisted of 36 students, meanwhile, the experimental class II that chosen is X-6 consisted of 36 students. The instrument used to measure the students' mathematics achievement was an essay test. The normality test used was Lilliefor's test, get that data is normal and the homogeneity test by using Fisher test, get that data is homogeny. The data analysis technique was t-test at the level of significance α = 5%.The study result showed that there is the difference of students' achievement in mathematics which using the guided discovery learning model with cooperative learning model JIGSAW type in grade X SMA N 3 P. Siantar where obtained tcalculation = 2.504 at a = 0.05 and ttable = t(0.975,70)= 1.995, then tcalculation = ttable
Combination Multilayer Fuzzy Inference System with K-means for Classification of Dental Diseases Prandana, Randy; Mawengkang, Herman; Suwilo, Saib
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5737

Abstract

This study was conducted to solve the problem of classifying dental diseases such as pulpitis, gingivitis, periodontitis and advanced periodontitis. The method in this study uses a combination of algorithms with a multilayer system where in the first layer a fuzzy inference will be carried out whether a patient is suffering from pulpitis. Early symptoms of pulpitis are characterized by pain with varying levels. Meanwhile, in the second layer a fuzzy inference process will also be carried out to identify other types of dental diseases, but in this second layer the centroid value calculation process is carried out using the K-means algorithm for all input variables. Then the inference process will run to determine the type of disease suffered by the patient following the fuzzy set of other types of diseases. This study is expected to contribute to helping the initial screening process for dental diseases so that it is easier for dentists to carry out further examinations. The results of this study have been proven to be able to help doctors in conducting initial screening to determine dental disease. In this study, the multilayer system is intended to differentiate the results of dental disease classification because pulpitis does not have a relationship between input variables and other types of dental disease. Meanwhile, the use of the fuzzy inference system method in this study showed good results because the FIS method can map the level of pain suffered by a patient with mild, moderate and severe levels into a numeric value that can be classified where the level of pain is a feeling that cannot be calculated, by using the fuzzy method, the linguistic value can be defined into a conclusion. Grouping input values by finding the means value in the second layer and combined with the fuzzy method has been proven to provide good results for determining the type of dental disease.
Discrete optimization model for multi-product multi-supplier vehicle routing problem with relaxed time window Firdaus, Muliawan; Mawengkang, Herman; Tulus, Tulus; Sawaluddin, Sawaluddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp592-603

Abstract

This study examines the complicated logistics optimization issue known as the vehicle routing problem for multi-product and multi-suppliers(VRP-MPMS), which deals with the effective routing of a fleet of vehicles to convey numerous items from multiple suppliers to a set of consumers. In this problem, products from various suppliers need to be delivered to different customers while considering vehicle capacity constraints, time windows, and minimizing transportation costs. We propose a hybrid approach that combines a generalized reduced gradient method to identify feasible regions with a feasible neighborhood search to achieve optimal or near-optimal solutions. The aim of the exact method is to get the region of feasible solution. Then we explore the region using feasible neighborhood search, to get an integer feasible optimal (suboptimal) solution. Computational experiments demonstrate that our model and method effectively reduce transportation costs while satisfying vehicle capacity constraints and relaxed time windows. Our findings provide a viable solution for improving logistics operations in real-world scenarios.
Model of emergence evacuation route planning with contra flow and zone scheduling in disaster evacuation Hartama, Dedy; Mawengkang, Herman; Zarlis, Muhammad; Widia Sembiring, Rahmad
Computer Science and Information Technologies Vol 2, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i1.p1-10

Abstract

Evacuation is characterized by rapid movement of people in unsafe locations or disaster sites to safer locations. The traffic management strategy for commonly used evacuations is the use of Shoulder-Lane, contra-flowing traffic and gradual evacuation. Contra-flow has been commonly used in traffic management by changing traffic lanes during peak hours. To implement the contra-flow operation, there are two main problems that must be decided, namely Optimal contra-flow lane configuration problem (OCLCP) and optimal contra-flow scheduling. Within the OCSP there are two approaches that can be used: zone scheduling and flow scheduling. Problem of contra-flow and zone scheduling problem is basically an Emergence evacuation route planning (EERP) issue. This research will discuss EERP with contra-flow and zone scheduling which can guarantee the movement of people in disaster area to safe area in emergency situation.
Performance Optimization of ERD Designs Using Cost-Based Optimization for Large-Scale Query Processing Lubis, Juanda Hakim; Handayani, Sri; Mawengkang, Herman; Napitupulu, Fajrul Malik Aminullah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4523

Abstract

The rapid growth of stored data, particularly on magnetic disks, is doubling annually for each department within a company, creating a pressing need for efficient database management. While database design is a fundamental step in establishing a high-performance system, it alone is insufficient to ensure optimal efficiency. Query optimization plays a critical role in improving data transaction speed, reducing query execution time, and enhancing overall system responsiveness. This study evaluates various relational database models under different data volumes to analyze their impact on query performance. Using the Cost-Based Optimizer method and access time measurements, we assess query costs and determine the factors influencing performance. The results indicate that among the three database models analyzed, ERD-3 consistently delivers superior performance, especially in handling complex queries. This is attributed to its modular structure, strategic indexing, and reduced full table scans, which collectively minimize query execution costs. Additionally, several key factors significantly affect query performance, including record count, attribute size, query complexity, primary and unique key usage, indexing strategies, order-by clauses, index sequences, and SQL function application. This research contributes to the field of database optimization by demonstrating that ERD structuring and cost-based query analysis significantly improve system efficiency in large-scale environments. These findings emphasize the necessity of a well-structured, scalable database model and efficient query processing techniques to accommodate large-scale data growth. The study’s conclusions provide a foundation for advanced optimization strategies, ensuring that modern database systems remain efficient and adaptable to evolving data demands.
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.
Optimization model of vehicle routing problem with heterogenous time windows Mawengkang, Herman; Syahputra, Muhammad Romi; Sutarman, Sutarman; Weber, Gerhard Wilhelm
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4043-4057

Abstract

This study proposes a novel optimization framework for the vehicle routing problem with heterogeneous time windows, a critical aspect in logistics and supply chain operations. Unlike conventional vehicle routing problem (VRP) models that assume uniform service schedules and fleet capacities, our approach acknowledges the diverse time constraints and vehicle specifications often encountered in real-world scenarios. By formulating the problem as a mixed integer linear programming model, we incorporate constraints related to time windows, vehicle load capacities, and travel distances. To tackle the NP-hard complexity, we employ a hybrid strategy combining metaheuristic algorithms with exact methods, thus ensuring both solution quality and computational efficiency. Extensive computational experiments, conducted on benchmark datasets and real-world logistics data, confirm the superiority of our model in terms of solution quality, runtime, and adaptability. These findings underscore the model’s practicality for industries facing dynamic routing requirements and tight service windows. Furthermore, the proposed framework equips decision-makers with a robust tool for optimizing route planning, ultimately enhancing service quality, reducing operational costs, and promoting more reliable delivery outcomes.
Analysis of Rainfall Transition Probability Using Markov Chain Method Pasaribu, Suhendri; Suwilo, Saib; Mawengkang, Herman
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v7i2.21719

Abstract

This research applies the Markov Chain model to examine daily rainfall data in Medan City. Markov chain is one of the methods used for forecasting in various fields, such as economics, industry, and climate. This research uses secondary data of daily rainfall intensity from the BMKG Station of the Center for Meteorology, Climatology and Geophysics Region I. The purpose of this research is to determine the transition probability (probability of transition). This study aims to determine the chance of transition (displacement) of daily rainfall intensity, There are four conditions of rainfall intensity that are categorized, namely no rain, light rain, moderate rain, and heavy rain. The Markov Chain method used is the Champman- Kolmogorov Equation and the steady state equation. The fixed probability of not raining is 59.16%, the fixed probability of light rain is 17.67%, the fixed probability of moderate rain is 16.28%, and the fixed probability of heavy rain is 6.86%.
A Data-Driven MINLP Approach for Enhancing Sustainability in Blockchain-Enabled e-Supply Chains Badawi, Afif; Efendi, Syahril; Tulus, Tulus; Mawengkang, Herman
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.v6i4.889

Abstract

Modern e-supply chains are characterized by increasing complexity and a critical need for enhanced sustainability, transparency, and traceability. Blockchain technology emerges as a significant enabler, offering decentralized, immutable ledgers and smart contracts that can support more secure, verifiable, and environmentally responsible operations through trustworthy data. Despite blockchain's potential, a notable gap exists in the availability of quantitative, data-driven optimization models that rigorously assess the operational and sustainability impacts of its integration into e-supply chains, particularly for complex, non-linear system interactions. This study aims to address this gap by presenting an in-depth analysis of a specific Mixed-Integer Non-Linear Programming (MINLP) optimization model. The goal is to clarify its structure, evaluate its application for an e-supply chain incorporating blockchain features (like transaction costs and conceptual smart contract enforcement for compliance) and sustainability objectives (such as carbon emission reduction), and derive practical insights. The methodology involves a detailed exposition of the MINLP model, followed by its application to a defined e-supply chain scenario. The analytical approach includes computational experiments focusing on a base case analysis to demonstrate model functionality. The broader evaluative framework for this study encompasses benchmarking the model’s performance against a conventional system and conducting sensitivity analyses on key parameters to understand performance trade-offs. The initial base case analysis demonstrates the model's capability to optimize supplier selection and operational plans while adhering to sustainability constraints, such as maintaining carbon emissions at or below 300 kg CO₂ per period, and accounting for blockchain-specific costs like a per-supplier usage fee of 500. The structure of the model and preliminary insights suggest its potential to achieve improved environmental impact compared to conventional systems, balanced against associated blockchain implementation costs. This research provides a detailed examination of a complex MINLP structure, offering a data-driven analytical approach for assessing blockchain's role in sustainable e-supply chains. It furnishes a foundational framework and insights that can guide managerial decisions and strategic planning for industries transitioning towards greener, more transparent, and digitally advanced supply chain operations.
Co-Authors , Rahmad Sembiring Abi Rafdi Afdhaluzzikri, Afdhaluzzikri Afnaria, Afnaria Ahmad Zaki Mubarak, Ahmad Zaki Al Khowarizmi Anggi Anatasia Kinanti Anugreni, Fera Arjon Turnip Asrianda Asrianda Azmi, Zulfian - Badawi, Afif Buaton, Relita Budhiarti, Erna Christefa, Dea Christian Sinaga, Christian Dadang Priyanto Dedi Siswo Defri Muhammad Chan Deny Jollyta Efendi, Syahril Elly Rosmaini Ermawati Ermawati Erna B N Erna Budhiarti Nababan Fatma Sari Hutagalung Firmansyah Firmansyah Firmansyah Firmansyah Fitrie, Rosa Hadistio, Ryan Rinaldi Handayani, Sri Hartama, Dedy Hasugian , Paska Marto Hengki Tamando Sihotang Hengki Tamando Sihotang Heni Pujiastuti Herawati, Elvina Heri Gustami Husain Husain Husain Husain Ignazio Ahmad Pasadana Iin Parlina Indah Purnama Sari Juanda Hakim Lubis Juanda Hakim Lubis K. M. Nasution , Mahyuddin Lestari, Valencya lili Tanti Lismardiana Lismardiana Lusi Herlina Siagian M Safii M Zarlis Mahyuddin K. M Nasution Mardiningsih Mardiningsih, Mardiningsih Marpongahtun Marwan Ramli Maya Silvi Lydia Mochamad Wahyudi Muhammad Arif Satria Nasution Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis, Muhammad Muliawan Firdaus Napitupulu, Fajrul Malik Aminullah Nuraini Nuraini Oktaviana Bangun Opim Salim Sitompul Ovirianti, Nurul Huda Pasaribu, Suhendri Poltak Sihombing Prandana, Randy Pujiastuti, Lise Putri, Mimmy Sari Syah Rahman, Silvi Anggraini Resti, Lady Ichwana Roma Rezeki Ryan Rinaldi Hadistio Saib Suwilo Saib Suwilo Santoso, Ahmad Imam Sarif, Muhammad Irfan Sawaluddin Nasution Sawaluddin Sawaluddin, Sawaluddin Sri Handayani Sugiyarmasto Sugiyarmasto Sutarman Sutarman Sutarman Sutarman Sutarman Syahmrani, Aghni Syahputra, Muhammad Romi Syahril Effendi Tanjung, Ilyas Tulus Tulus Tulus Tulus Vinsensia, Desi Weber, Gerhard Wilhelm Wiryanto Wiryanto Wisnu Irsandi Pratama Yuliska Zakarias Situmorang Zarkasyi, Muhammad Imam Zarlis, M Zarlis, M Zoelkarnain Rinanda Tembusai Zulfian Azmi