BACKEND MODULES DEVELOPMENT FOR THE CV SCREENING SYSTEM WITH APPROPRIATE TECHNOLOGY AND AUTHENTICATION & AUTHORIZATION SYSTEMS
The CV screening process is an important stage in the recruitment process for selecting job applicants based on the submitted CVs. Manual processing would be time-consuming and prone to high errors. Therefore, the author's team developed a machine learning-based CV screening system, with the...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/76668 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Institut Teknologi Bandung |
| 語言: | Indonesia |
| 總結: | The CV screening process is an important stage in the recruitment process for
selecting job applicants based on the submitted CVs. Manual processing would be
time-consuming and prone to high errors. Therefore, the author's team developed a
machine learning-based CV screening system, with the focus of this report on the
development of the backend module in the form of an Application Programming
Interface (API) service.
This Final Project analyzes the appropriate technology to build the backend module
of the CV screening system. The Final Project also focuses on the authentication
and authorization processes of the system. The analysis results indicate that the
MERN web stack, comprising components such as Node.js, Express, and
MongoDB, is the best choice for building the backend module. The system also
employs JWT with the HMAC SHA-512 encryption algorithm for the
implementation of authentication and authorization processes.
The backend module was tested using a tool named Postman with four types of
testing: functional testing, system performance, security testing, and integration
with the frontend module and machine learning model. The results demonstrate that
the system performs well, with a response time of approximately 951.71 ms,
meeting the Nielsen Norman standards. The authentication and authorization
system also effectively maintain data security.
The conclusion of this Final Project is that the MERN stack is suitable for the
backend module of the CV screening system, and the security of the authentication
and authorization system meets the requirements effectively. However, there are
several steps that can be taken to improve the quality of this Final Project, including
conducting testing with different technologies, optimizing the code, exploring
alternative security systems, and enhancing web server performance (CPU usage). |
|---|