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IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION Karadeniz, Ahmet Serdar; Karadeniz, Mehmet Fatih; Weber, Gerhard Wilhelm; Husein, Ismail
ZERO: Jurnal Sains, Matematika dan Terapan Vol 3, No 1 (2019): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Abstract Facial expression recognition is one of the challenging tasks in computervision. In this paper, we analyzed and improved the performances bothhandcrafted features and deep features extracted by Convolutional NeuralNetwork (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.Additionally, we developed features based on the distances between faciallandmarks and SIFT descriptors around the centroids of the facial landmarks,leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracywith a CNN model trained from scratch. By combining CNN features withhandcrafted features, we achieved 69.54 % test accuracy.Key Word: Neural network, facial expression recognition, handcrafted features

A Decision-Support Model for Football Squad Optimization: Integrating AHP-WASPAS and Binary Programming Gultom, Parapat; Ezra, Posman; Marpaung, Jonathan Liviera; Weber, Gerhard Wilhelm; Sentosa, Ilham
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 1 (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.v7i1.18531

Team composition in professional football remains a critical challenge due to subjective biases and the complexity of player performance metrics. This study proposes a multi-criteria decision-making (MCDM) approach to optimize the starting lineup of PSIS Semarang. The Analytical Hierarchy Process (AHP) was used to determine the importance of positional attributes, followed by the Weighted Aggregated Sum Product Assessment (WASPAS) to evaluate individual player preferences. A binary integer programming model was developed to construct the optimal eleven-player formation. The proposed formation was validated through simulation in Football Manager 2023. Results showed that a 4-3-3 formation consistently outperformed alternatives, leading to first-place rankings and a President’s Cup win in simulation trials. This integrated decision-support model demonstrates effectiveness in enhancing team selection strategies in professional football.

Job Stress and Work Environment as Determinants of Employee Performance in the Indonesian Private Sector Lubis, Muhammad Arif; Sentosa, Ilham; Weber, Gerhard Wilhelm
Journal Of Management Analytical and Solution (JoMAS) Vol. 5 No. 2 (2025): Journal Of Management Analytical and Solution
Publisher : TALENTA Publisher, Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jomas.v5i2.21039

Employee performance is a critical factor in achieving organizational goals andsustaining competitive advantage. This study investigates the influence of jobstress and work environment on employee performance at PT. Rahayu Permai.Employing a quantitative research design, data were collected through structuredquestionnaires distributed to employees and analyzed using multiple linearregression techniques. The results indicate that job stress has a negative andsignificant effect on employee performance, while the work environment exertsa positive and significant influence. These findings highlight the importance oforganizational efforts to minimize work-related stress and improve workplaceconditions in order to enhance employee productivity and overall performance

Job Stress and Work Environment as Determinants of Employee Performance in the Indonesian Private Sector Lubis, Muhammad Arif; Sentosa, Ilham; Weber, Gerhard Wilhelm
Journal Of Management Analytical and Solution (JoMAS) Vol. 5 No. 2 (2025): Journal Of Management Analytical and Solution
Publisher : TALENTA Publisher, Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jomas.v5i2.21039

Employee performance is a critical factor in achieving organizational goals andsustaining competitive advantage. This study investigates the influence of jobstress and work environment on employee performance at PT. Rahayu Permai.Employing a quantitative research design, data were collected through structuredquestionnaires distributed to employees and analyzed using multiple linearregression techniques. The results indicate that job stress has a negative andsignificant effect on employee performance, while the work environment exertsa positive and significant influence. These findings highlight the importance oforganizational efforts to minimize work-related stress and improve workplaceconditions in order to enhance employee productivity and overall performance

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

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

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.

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