cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
M. Khalil Gibran
Contact Email
jitcoseditor@gmail.com
Phone
+6289524574891
Journal Mail Official
jitcos@multimediatekno.org
Editorial Address
Jln. Bhayangkara, No. 114, Kecamatan Medan Tembung, Kota Medan, Sumatera Utara, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
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 15 Documents
 1   2 
Implementation of Finite State Automata on Pizza Vending Machine System Aulia, Muhammad Fathir; Suryandi, Diky; Nainggolan, Jesron
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.3

Abstract

This study aims to implement Finite State Automata (FSA) on a pizza machine. FSA is a theoretical computational model used to describe the behavior of a system that can change discretely from one state to another. A pizza machine is a machine used to make pizza automatically. In this study, we design and implement FSA on a pizza machine to regulate the pizza making process. FSA consists of a number of states and transitions between those states. Each state represents a certain stage in the pizza making process, such as adding ingredients, mixing dough, and baking. The programming language and algorithm used are appropriate for implementing FSA on a pizza machine. When the machine is turned on, it will start in the initial state. Then, based on the input given, the machine will switch between different states according to the specified transition rules. By implementing FSA, this study successfully automated the pizza making process on the machine. This reduces dependence on human intervention and increases production efficiency. By using FSA, the pizza machine can operate automatically and produce pizza with high accuracy and efficiency. This study contributes to the development of automation in the food industry and improves the understanding of how to apply FSA in the context of real-world applications. In this study, FSA is used to control a muffin machine, but the FSA concept can also be used in various other automation applications.
Image Classification Based On Color Using Thresholding Method Selian, Teguh Agara; Akbar, Niko; Hidayat, M. Irfan
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.5

Abstract

This research aims to categorize images based on color using the method of thresholding. Image classification based on color plays a crucial role in various applications such as object detection, traffic monitoring, and medical image processing. The thresholding method is a popular approach used in image segmentation due to its effectiveness and computational efficiency. In this method, grayscale images are converted into binary images by determining a specific threshold value. This research utilizes the thresholding method to separate pixels based on their color intensity. The research methodology consists of several steps, including dataset collection, image pre-processing, color feature extraction, application of the thresholding method, and class labeling. The study's benefits include object recognition, cost and time reduction in image classification, and improved product quality and income for farmers.
Application Of Naive Bayes Algorithm For Sentiment Analysis On Economic Recession Threat Panggabeanan, Fajar Gilang Ramadhan
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.6

Abstract

Recession is a condition in which real economic growth becomes negative, or in other words, there is a decline in Gross Domestic Product (GDP) for two consecutive quarters in one year. A recession is characterized by a weakening of the global economy that has an impact on the domestic economy in various countries. The greater the dependence of a country on the global economy, the more likely the country is to experience a recession. An economic recession can cause a simultaneous decline in all economic activities, including corporate profits, employment, and investment. In this study, data was collected from YouTube using a crawling technique, with a total of 200 comments analyzed. These comments were then labeled with a lexicon-based method using an Indonesian dictionary. The preprocessing stage was carried out to prepare the data before sentiment analysis. In addition, the TF-IDF word weighting method was applied with the bigram feature (n = 1) in the analysis. The system was evaluated using a confusion matrix, and the results showed that the prediction model, which was based on 200 opinion data with a 9:1 split ratio between training data and test data, achieved an accuracy of 75.00%. However, the precision, recall, and F1-score values each show 0.00%. The performance of the system model built in this study shows less than satisfactory results and may require improvements to increase its effectiveness.
Application of K-Means Cluster Algorithm to Determine Student Achievement Yahya, Rafly Aulia; Siregar, Reza Abdillah; Sitompul, Boy Arnol
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.9

Abstract

The application of the K-Means Algorithm by dividing the data into one or more groups, with data included in one group representing similarities and other groups representing differences. To assess whether or not student motivation is superior, the data from the student achievement evaluation shows the average value of each topic. Analysis, design, coding, and system testing are all included in the research steps. Evaluation is carried out to be used as a basis for the characteristics used in the calculation to ensure higher values. Information systems can achieve successful clustering classification results by including the k-means clustering method. This technique rotates the centroid distance at each iteration, forming cluster points and reducing clustering time
Design of a Mobile-Based Attendance System Integrated with Android-Based Geolocator Simatupang, Aidil Akbar; Maulana, Alfian
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.11

Abstract

The development of information technology has significantly impacted various aspects of life, including employee attendance administration. This study aims to design a mobile-based attendance system integrated with geolocation on Android devices to enhance the efficiency and accuracy of attendance recording. The system utilizes geolocation technology to validate user locations in real-time, reduce data manipulation, and improve employee accountability. The system was developed using the System Development Life Cycle (SDLC) approach with the Waterfall model, which includes analysis, design, implementation, and testing using the Black-box method. Testing results indicate that all features—such as login, attendance recording, profile management, employee addition, and location validation—function as specified. The implementation of this system offers a modern solution for more transparent and effective attendance management. These findings are expected to serve as a reference for organizations seeking to adopt reliable digital attendance systems aligned with the demands of the information technology era. Future research is recommended to develop additional features such as facial recognition or integration with performance management systems
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.

Page 1 of 2 | Total Record : 15

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025 Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025