Designed and developed a centralized VM resource monitoring and alerting system to improve infrastructure visibility, incident response, and operational efficiency. The system integrates Prometheus and Grafana for metrics collection and visualization, with n8n as the automation engine to process alerts, manage workflows, and handle escalation logic.

Implemented customized alerting pipelines where alerts are delivered to Telegram via webhooks, allowing engineers (PIC) to acknowledge incidents, add handling notes directly from Telegram, and automatically store all incident data in a MongoDB database. Alerts are deduplicated and tracked using unique alert identifiers to preserve incident history and prevent loss of handler information when recurring alerts occur.

Developed a web-based alert dashboard connected to the MongoDB backend, providing detailed insights such as alert status classification (handled/unhandled, firing/resolved), PIC performance scoring, alert statistics per VM instance, date-based filtering, search functionality, and interactive UI enhancements including loading animations. Added support for monthly report generation with print-ready formatting for management and operational reviews.

The system was deployed on on-premise servers and partially integrated with cloud environments (AWS EC2). Additional enhancements included storage and resource-specific alerts, role-based access control (admin/editor/viewer), alert escalation flows, and ongoing optimization for scalability and maintainability.

VM Resources Alert & Dashboard System

This project demonstrates a complete deployment of Odoo 13 Community Edition using a modern and production-ready infrastructure. The system is containerized with Docker & Docker Compose, uses PostgreSQL as the backend database, and is secured behind Nginx reverse proxy with Let's Encrypt SSL.
The deployment includes Odoo’s installation, initial database setup, environment configuration, and production optimization such as proxy timeouts, persistent volumes, and reverse proxy routing. The project also resolves common production issues such as Odoo’s long loading time during module initialization (504 Gateway Timeout), by adjusting Nginx proxy parameters.
<Turned Off due to lack of resources>


Odoo 13

A case study on reorganizing a computer network. To see the details of the case study you can open the learning module on page 10 “Tugas Project 2: TUGAS PRAKTEK-DEMONSTRASI/PRAKTEK”.

Junior Network Administrator Final Project

This project visualizes tree crown data in the form of a map, for now the website can only display the ID of each detected tree crown. The detected satellite images will be processed by a machine learning model in Google Colab that will generate geospatial coordinates of each detected tree crown, then each detected tree crown will be assigned an ID and stored in the database. The geospatial data stored in the database will be connected to NextGIS to be processed into a vector layer. The initial image detected by google colab can also be uploaded to NextGIS and published as a raster layer that can be used to visualize the tree data collection in the area.

For now the development is only in the NextGIS stage, because I was not able to display the map on my website due to CORS problems of NextGIS, I need to upgrade my NextGIS to paid mode to set the CORS.

Projects

VM Resources Alert & Dashboard System

Odoo 13

Junior Network Administrator Final Project

GIS : Tree Crown Mapping & Visualization