Muhammad Yusuf Azmi
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Analisis Sistem Aplkasi Pengolahan Citra Pada Pertanian Cerdas Untuk Pemantauan Tanaman Supiyandi Supiyandi; Mona Donaon; Muhammad Yusuf Azmi
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 3 (2024): Juli : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v2i3.1443

Abstract

Smart farming has become a rapidly growing research area with the aim of increasing agricultural productivity and efficiency through advanced technologies. One of the key technologies in smart agriculture is image processing, which enables real-time monitoring and analysis of crop conditions. This article reviews image processing applications in smart agriculture, with a focus on the methods and techniques used for crop monitoring. Image processing methods discussed include pest and disease detection, measuring plant growth, as well as monitoring soil moisture and plant health. Image processing techniques such as convolution-based image analysis (Convolutional Neural Networks/CNNs), image segmentation, and pattern recognition are applied to obtain accurate and relevant information. Case studies and field experiments show that image processing can provide accurate and real-time data, enabling farmers to make more informed and efficient decisions. In conclusion, the application of image processing technology in smart agriculture has great potential to increase crop yields, reduce resource use, and advance sustainable agricultural practices.
A Discrete-Event and Monte Carlo-Based Simulation Model for Multi-Server Call Center Queueing Systems Nur Bainatun Nisa; Dafa Ikhwanu Shafa; Muhammad Yusuf Azmi; Parinduri, Armayanti Akhiriyah
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.35

Abstract

This study presents the implementation and performance evaluation of a multi-server queueing system model for call center operations using discrete-event simulation combined with Monte Carlo analysis. The objective is to analyze system performance under varying numbers of service agents to identify the optimal configuration that balances service efficiency and customer satisfaction. The model assumes that customer arrivals follow a Poisson distribution, while service times are exponentially distributed to represent realistic call handling behavior. Simulation experiments were conducted over eight-hour operational periods with server counts ranging from one to eight, each replicated 500 times for statistical robustness. Performance indicators such as average waiting time, server utilization, and Service Level Agreement (SLA) compliance were analyzed to measure system efficiency. Results show that increasing the number of servers significantly reduces average waiting time and enhances service level compliance. Configurations with five or more servers achieved average waiting times close to zero and over 99% compliance with the SLA, while maintaining moderate server utilization levels between 70% and 80%. These findings demonstrate that integrating discrete-event simulation with Monte Carlo methods provides an effective and reliable framework for evaluating service system performance, optimizing resource allocation, and supporting decision-making in call center management.