Jobs Visualization and Salary Prediction System Based on Jobstreet Malaysia / Khyrina Airin Fariza Abu Samah ... [et al.] - Abu Samah, Khyrina Airin Fariza; Wirakarnain, Nurqueen Sayang Dinnie; Deraman, Noor Afni; Johari, Siti Nor Amalina; Moketar, Nor Aıza; Hasrol Jono, Mohd Nor Hajar (2021)
Jobs are solemnly proclaimed as a crucial factor that can determine an individual's future and the most precise way of securing one's financial stability, especially towards the populace of new graduates. This study discusses explicitly and dives deeper into helping the graduates with their future careers. Obtaining this application is an ideal website used to discover the wonders of jobs all over the country mentioned above, Malaysia, but identifying the jobs in an organized manner by dividing them into their respective and specific courses is challenging. Therefore, using the modified waterfall consisting of four phases and applying the linear regression and visualization techniques help overcome the problem. It does not merely offer the jobs for the graduates, but they are also provided with the aid of foreseeable salary to make it easier for them to choose based on their expectation towards the wage. The extracted Jobstreet's data runs the pre-processing, develops the model, and runs on real-world data. Linear Regression algorithm was used to predict the salary and tested using mean absolute error to validate the prediction. The system also applies the dashboard in presenting visualization of the data. As a result, it is of significant help to the specific populace of graduates to offer them a thorough overview of their desired work and the companies that bear the same job but juxtapose wages. The functionality of the system has been tested to ensure the system meets the objectives set.