DEVELOPMENT OF WEB DASHBOARD FOR VISUALIZING MACHINE LEARNING-BASED BEAM TRACKING ON 5G HIGH SPEED TRAIN
At the command center of PT Kereta Api Indonesia, high-capacity services are required to ensure smooth and efficient operations. The performance of beam tracking on the 5G network is crucial to monitor in order to maintain optimal service quality. Therefore, a dashboard capable of visualizing bea...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/82253 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | At the command center of PT Kereta Api Indonesia, high-capacity services are
required to ensure smooth and efficient operations. The performance of beam
tracking on the 5G network is crucial to monitor in order to maintain optimal
service quality. Therefore, a dashboard capable of visualizing beam tracking
performance data is needed to allow operators to monitor and assess network
performance. This way, potential issues can be identified and addressed to reduce
the risk of service disruptions. Additionally, the dashboard is expected to assist in
analyzing historical data to evaluate long-term performance and make decisions
based on observed trends. The ability to customize the data display according to
needs also allows operators to focus on the metrics and parameters most relevant
to current operational conditions. The goal of this final project is to develop a front-
end web dashboard system capable of visualizing beam tracking performance data
generated by machine learning algorithms for high-speed 5G trains. With this
visualization, the predicted performance of beam tracking can be presented in a
way that is easier for users to read and understand. The web dashboard is
developed using Streamlit, a Python framework that allows for the rapid and easy
creation of interactive web applications. This dashboard displays various data
visualizations that enable users to analyze beam tracking performance in real-time
and historically. The interactive dashboard also provides users with the flexibility
to customize visualizations as needed, such as filtering data based on specific
parameters. The results of this final project show that the developed front-end web
dashboard system can be an effective tool for visualizing beam tracking prediction
performance on 5G networks, is interactive for users, easily customized and can be
easily read and understood. |
|---|