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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Fathi Athallah Z
Universitas Islam Negeri Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering Mathematics

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A Analysis and Modeling Simulation King Kuphi Cafe Queuing System With Customer Arrival Variations Using Python Nurul Fikria Nurul_Fikria; Risky Ananta Pradana; Jelita Rahmah Zebua; Fathi Athallah Z
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

Queueing system modeling and simulation is an effective approach for analyzing service performance in business environments with dynamic customer arrival rates, such as at King Kuphi Cafe. This study aims to model the queueing system at the cafe with various variations in customer arrival rates using the queueing theory approach and simulate it using the Python programming language. The models used are the M/M/1 and M/M/c queueing systems, which allow analysis of changes in waiting time, queue length, server utilization, and service level based on variations in arrival (λ) and service (μ) parameters. The simulation was run using Python packages such as NumPy and SimPy to represent the arrival and service processes realistically. The results of the study show that an increase in the rate of customer arrivals significantly affects system performance, particularly in terms of an increase in average waiting time and queue length. In addition, adding more servers has been proven to reduce queue congestion and improve overall service quality. These findings are expected to serve as a basis for King Kuphi Cafe managers in making strategic decisions regarding the number of baristas and operational optimization to achieve more efficient service.
Classification of Herbal Plants Based on Leaf Images Using Gray Level Co-Occurrence Matrix and K-Nearest Neighbor Fahmi Nur Alimsyah Purba; Fathi Athallah Z; Alfin Alfarizi; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2291

Abstract

Herbal plants have long been used as traditional medicine. However, many people struggle to tell different herbal leaves apart because they look quite similar. This study tries to build a system that can recognize two types of herbal leaves, Moringa and Katuk, simply from their photos. We used GLCM to extract texture features from the leaves, then classified them using KNN. The dataset came from Kaggle, with 480 leaf images in total. Before processing, we cropped the images, resized them to 256x256 pixels, and converted them to grayscale. GLCM features were taken from four angles (0°, 45°, 90°, 135°) and then averaged. This gave us four texture values: contrast, correlation, energy, and homogeneity. We tested KNN with k values from 1 to 15 and five different distance metrics. The best result we got was 94% accuracy, using Manhattan distance with k=1. This system could help everyday people identify medicinal plants more easily without needing lab tests.
Co-Authors Alfin Alfarizi Fahmi Nur Alimsyah Purba Jelita Rahmah Zebua Lailan Sofinah Harahap Nurul Fikria Nurul_Fikria Risky Ananta Pradana