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M. Khalil Gibran
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jitcos@multimediatekno.org
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JITCoS : Journal of Information Technology and Computer System
Published by CV. Multimedia Teknologi Kreatif
ISSN : -     EISSN : 31096182     DOI : https://doi.org/10.65230/jitcos
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
JITCoS: Journal of Information Technology and Computer System is a peer-reviewed scholarly journal that aims to advance the theory and practice of information technology and computer systems. The journal seeks high-quality contributions from researchers, academics, and industry professionals that enrich the body of knowledge and deliver practical insights. The journal welcomes original articles, comprehensive reviews, and practical case studies in, but not limited to, the following areas: Information systems development and IT governance, Web and mobile application engineering, Big data analytics, data mining, and data science, Cybersecurity, digital forensics and privacy, Digital transformation, E-Government, and Smart Cities, Cloud and edge computing technologies, Geographic Information Systems (GIS), Decision Support Systems (DSS) and business intelligence, Computer architecture and hardware acceleration, Networking protocols and distributed systems, Embedded systems and Internet of Things (IoT), Operating systems and kernel-level development, Parallel, grid, and cloud-based computation, Control systems, robotics, and, intelligent automation, Artificial Intelligence (AI) and Machine Learning (ML). JITCoS encourages interdisciplinary approaches that merge engineering, computing, and data-driven insights to tackle contemporary challenges and foster innovation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
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Search results for , issue "Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025" : 10 Documents clear
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.
Monte Carlo Simulation for Rice Yield Risk Estimation Based on Weather and Soil Quality Factors Nouval Khairi; Muhammad Farhan; Rahman, Muhammad Zhilali
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.v1i2.36

Abstract

This study applies Monte Carlo simulation to estimate rice yield risks in the Medan region during 2024 by incorporating key weather variables (temperature, rainfall, and humidity) and soil quality indicators (pH, water content, salinity, texture, and organic matter). Given the increasing impacts of climate change and land degradation on food security, a probabilistic approach is essential for quantifying uncertainties in crop production. Using 10,000 simulated scenarios based on historical and field-derived parameter distributions, the model estimates an average rice yield of approximately 4.2 tons per hectare with a standard deviation of 0.2 tons per hectare, indicating relatively stable production under normal conditions. However, 20% of the simulations produce yields below 3.9 tons per hectare, reflecting elevated risks of crop failure during adverse environmental situations. Sensitivity analysis identifies rainfall and soil pH as the most influential variables, where extreme deviations may reduce yields by up to 35%. These findings offer critical evidence for policymakers and farmers to develop adaptive management strategies aimed at safeguarding sustainable rice production in the region.
Modeling and Simulation of Indoor Temperature Dynamics Using Random Forest and Multi-Layer Perceptron Methods Risky, T. Tanzil Azhari; Faiza, Nayla; Hasibuan, Mhd Fikry Hasrul; Nasution, Mhd Syahru Ramadhan
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.v1i2.39

Abstract

Modeling and simulating indoor temperature changes is crucial for improving the energy efficiency of HVAC systems in smart buildings. This study created and compared two models, Random Forest and Multi-Layer Perceptron (MLP), to study indoor temperature changes and make 24-hour temperature predictions. The dataset used contained 97,606 readings from IoT sensors on Kaggle, which were then processed into 38,334 observations with a 5-minute interval. The feature engineering process included creating lag features, moving statistics, and temperature differences in order to capture the time patterns and thermal properties of the building. The Random Forest model showed better results with MAE of 0.146°C, RMSE of 0.285°C, and R² of 0.986, far better than the MLP which had MAE of 0.470°C, RMSE of 0.731°C, and R² of 0.907. A 24-hour simulation proved the Random Forest's ability to make step-by-step predictions, achieving an MAE of 0.057°C and an R² of 0.993 without any cumulative errors. Random Forest was able to capture dynamic temperature changes (29.5-35°C), while MLP provided more stable results (32.5-35°C). The results of the study show that Random Forest is more efficient in modeling temperature changes, with the potential for HVAC energy savings of 15-25% through more precise settings based on predictions.
Hybrid Demand Forecasting and Monte Carlo Simulation for Retail Supply Chain Inventory Optimization Kartikasari, Diah Putri; Tambak, Tiara Ayu Triarta; Ridwanto, Aero Rizal
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.v1i2.40

Abstract

Retail inventory optimization must balance service levels against holding, ordering, and stockout costs under uncertain demand and lead time. We develop an integrated framework that couples hybrid demand forecasting with Monte Carlo simulation (MCS) to evaluate continuous‑review policies. Historical daily sales are modeled using statistical baselines (naive and exponential smoothing) and gradient‑boosted trees with quantile objectives to obtain distributional forecasts. Predictive means and residual‑based dispersion calibrate a Negative Binomial demand model; because lead-time is not present in the dataset, we treat it as a scenario parameter in the simulator (baseline mean ~2 days, SD ~1 day) and probe it via sensitivity analyses. Using a representative retail subset, we simulate 90‑day horizons with 300 replications per item across a grid of values. Results reveal a convex cost–service frontier: (15,120) minimizes total cost in the tested grid, while (25,140) achieves the highest fill rate. Sensitivity analyses show costs are most responsive to safety stock and lead‑time variability. The framework links forecast uncertainty to inventory policy selection, offering a reproducible, data‑driven tool for practitioners and a baseline for future multi‑echelon and decision‑focused extensions.
Traffic Congestion Modeling and Simulation in Front of the University of North Sumatra (USU) Campus Using an Agent-Based Modeling Approach Muhammad Alfariz Rasyid; Hutagalung, Syahada Mawarda; Muhammad Fajar Dermawan
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.v1i2.41

Abstract

Traffic congestion around the entrance of the University of North Sumatra (USU) campus represents a major issue influenced by several factors, including the presence of street vendors, illegal vehicle parking, public transport (angkot) that frequently stops without proper order, and the movement of both vehicles and pedestrians crossing the road. This research aims to construct and simulate the traffic situation in that area using an Agent-Based Modeling (ABM) approach, which was manually developed through the Python programming language. Each type of vehicle motorcycle, car, public transport, and pedicab is modeled as an individual agent that exhibits specific behaviors such as varying speed, stopping probability, and pause duration, based on observational data obtained from CCTV recordings of the Medan City Transportation Agency’s ATCS system. The simulation covers two main traffic directions, namely Jalan Setia Budi and Jalan Jamin Ginting, and evaluates several intervention scenarios such as adding designated bus stops, organizing street vendors, and managing pedestrian crossings. The outcomes demonstrate that applying a combination of these interventions increases the average vehicle speed by approximately 15-20% compared to the initial condition, implying that the proper management of roadside activities and environmental control significantly reduce traffic congestion. The ABM method proves capable of realistically illustrating traffic dynamics and can serve as a valuable analytical tool for evaluating transportation policies within campus zones and other urban areas.
Parking Route Modeling Using the A* Algorithm for Density Reduction at the Faculty of Science and Technology, State Islamic University of North Sumatra Berutu, Asro Hayati; Nasution, Salsabila; Rahmadani, Suci
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.v1i2.46

Abstract

The increasing number of vehicles on university campuses has led to significant congestion, particularly around parking areas. This study aims to design an intelligent parking route model using the Density-Aware A* algorithm to minimize vehicle congestion within the Faculty of Science and Technology (FST) at UIN North Sumatra. The proposed approach represents the internal campus network as a weighted graph, where each edge integrates both spatial distance and a density penalty that reflects the occupancy-to-capacity ratio of each parking area. The algorithm was implemented and simulated using Python and the NetworkX library within Google Colab. The results show that the system accurately identifies the optimal parking route based on vehicle type and real-time occupancy data. For motorcycles, the optimal path is A > B > F with a total cost of 23.06, while for cars, the most efficient path is A > B > H with a total cost of 18.21. The findings indicate that incorporating density-based cost adjustments effectively balances travel efficiency and vehicle distribution, contributing to overall congestion reduction in the FST–FKM corridor. Future research should focus on integrating live sensor data and adaptive feedback mechanisms to support large-scale deployment across diverse campus environments.
Swarm Intelligence-Based Workload Management in Computer Clusters Daulay, Novlianun
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.v1i2.47

Abstract

Efficient workload scheduling in heterogeneous distributed systems remains a complex challenge due to variations in computing capacities and unpredictable task arrivals. Traditional scheduling algorithms, including conventional Particle Swarm Optimization (PSO), often suffer from premature convergence and limited adaptability under dynamic workload conditions. To address these limitations, this study proposes an Adaptive Particle Swarm Optimization (APSO) algorithm that dynamically adjusts the inertia weight parameter to maintain an effective balance between exploration and exploitation during the search process. The adaptive mechanism allows particles to respond more effectively to workload fluctuations and prevents stagnation in local optima. Experiments were conducted on a simulated heterogeneous cluster environment consisting of multiple computing nodes with varying processing speeds and workloads. The performance of the proposed APSO was evaluated using three primary metrics: makespan, CPU utilization, and load imbalance. The results demonstrate that APSO successfully reduced makespan from 350.0 s to 287.0 s, achieving an improvement of approximately 18%, and increased CPU utilization from 77.8% to 83.4% compared to the Round Robin baseline. These findings confirm that the adaptive parameter control significantly enhances scheduling efficiency, improves resource utilization, and provides a more robust and reliable solution for dynamic heterogeneous distributed systems.
Implementation of a Favorite Course Search System Based on Students’ Average Grades Using the A* Algorithm Amsyah, Dwiky Oldi; Riansyah, Rusma; Aptanta, Dimas Aqila; Fachrezi, Muhammad Randy; Firdaus, Nasywa Roudhotul
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.v1i2.50

Abstract

Optimal selection of elective courses plays an important role in supporting students’ academic success and ensuring alignment between learning interests and final project preparation. This study aims to develop a favorite course search system based on the A-Star (A*) algorithm by utilizing students’ average grades as the main evaluation variable. The system was implemented using the Java NetBeans platform, supported by datasets consisting of course names, credit weights (SKS), and grade distributions. The A* algorithm was adapted through the integration of heuristic components, including Standard Deviation and Relative Credit Load, to improve accuracy in identifying optimal course recommendations. Experimental results demonstrate that the system is capable of generating recommendations with an accuracy rate of 95%, verified through comparison between system outputs and manual calculations. The results also show that the Mitigation course ranked highest with a score of 6.1, indicating strong student performance in that subject. Overall, the system provides a practical and efficient solution for academic decision-making, enabling students to select elective courses more strategically based on data-driven insights. This study contributes to the development of computational methods in educational recommendation systems and opens opportunities for further enhancement through integration with real academic databases.
Bayesian-Based Weather Prediction and Automated Distribution Simulation for Rice Harvest Optimization Agustina Siregar, Nelly; Siregar, Nur Laila
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.v1i2.62

Abstract

Weather variability creates substantial uncertainty in rice production and distribution, often resulting in discrepancies between predicted harvest quantities and logistical capacity. To address this challenge, this study presents an integrated framework that combines Bayesian weather prediction, climate-based rice yield estimation, and automated distribution simulation to support more reliable and uncertainty-aware agricultural planning. The Bayesian model is employed to generate probabilistic forecasts for key climatic variables rainfall, temperature, and humidity by producing posterior distributions that capture the inherent variability of environmental conditions, yielding stable predictive performance with an RMSE of 0.84 for rainfall and 0.62 for temperature. These probabilistic forecasts are subsequently utilized within a regression-based yield estimation model to quantify the influence of climatic fluctuations on harvest output, resulting in a mean absolute percentage error (MAPE) of 6.7%, which demonstrates strong consistency with actual production data. The estimated yields are then incorporated into an automated distribution simulation constructed as a weighted directed graph, where Dijkstra’s algorithm is applied to determine optimal delivery routes by evaluating distance, predicted load, and weather-related uncertainty. Simulation results reveal improvements in route efficiency and reduced deviation in travel times across varying climatic scenarios. Overall, the integration of Bayesian inference, yield prediction, and automated routing forms an adaptive and robust decision-support system for rice distribution management, offering a more reliable approach to optimizing agricultural logistics in environments characterized by dynamic and unpredictable weather patterns.
Simulation of Budget Allocation for Stunting Reduction Programs in West Aceh Regency using a Linear Regression Model Nurzanah, Laila; Tia Ramadani; Hartomo Ranomiharjo
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.v1i2.63

Abstract

Stunting remains a national priority issue in Indonesia, and West Aceh Regency is one of the regions facing this challenge. The success of stunting reduction programs heavily depends on effective and well-targeted budget allocation. This study aims to develop a budget allocation simulation model for stunting reduction programs in West Aceh Regency using Multiple Linear Regression. Historical program and budget data from the template-data-kabupaten-aceh-barat-stunting.csv dataset were used to train the model. Features such as year, program category (Infrastructure, Empowerment, Health, Other), program name, and a priority score were analyzed to predict the budget amount. The model fitting results show very high performance, with an Adjusted R-squared of 0.989 and an F-statistic of 298.6 (p < 0.001), indicating the model significantly explains the variability in budget allocation. The prioritas (priority) variable was found to be the most statistically significant predictor (p = 0.000). This model was then used to simulate an optimal budget allocation for 2025, with a total recommended budget of IDR 36.50 Billion. The main recommendation focuses on the "Program for Fulfilling Health Efforts" with a simulated allocation of IDR 33.75 Billion. This research demonstrates the potential of predictive modeling as a data-driven decision-making tool for local governments in planning budgets for stunting interventions. Furthermore, this study underscores the critical importance of shifting from historical-based budgeting to evidence-based allocation methods to accelerate national stunting reduction goals effectively.

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