Abstract. Many organisations (government of non-government) use websites to share information of new recruitment for the workers. This information overflows on thousands of sites with various attributes and criteria. However, this availability forms a complex puzzle in the selection process and lead to inefficient runtime. This study proposes a simple method for job searching simplification through a construction and collaboration of web scraping technique and classification using Naïve Bayes on search engine. This study is resulting an effective and efficient application for users to seek a potential job that fit in with their interests.

This study aims to analyse the Key Success Factors (KSFs) of e-learning implementation, so that e-learning can work well and provide maximum benefits for learning activities process in higher education. The method used in this research is begin from investigate the determinant factors based on the results of literature studies and survey techniques. To obtain a more in-depth analysis, then the factors are weighted using the Multi-Attribute Utility Theory (MAUT) method. The results showed that the determinant factors that need to be used as the main consideration in the implementation of e-learning at higher education are organizational factors, technology, and human resources. The dominant factor that needs attention in realizing the effectiveness of the implementation of e-learning in higher education is the organizational aspect which is realized in the form of creating a work culture and setting policies that are binding on the academic community to carry out e-learning.

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