Visualisasi Jobstreet Berbasis Knowledge Graph

Hidayat, Eva Nur Afny Kurnia (2020) Visualisasi Jobstreet Berbasis Knowledge Graph. Other thesis, Universitas Islam Riau.

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Abstract

Based on data from the central statistics agency, as of August 2019, the number of open unemployed people in Indonesia reached 7.05 million people. With the development of digital information technology, it can make it easier for open unemployed to find work through websites. In this graph knowledge-based jobstreet visualization research, data was taken from the website https://www.jobstreet.co.id/ using web scraping techniques. After analyzing it, it can be concluded that the jobstreet website has not provided a relationship between industry, company, job and the jobstreet website has not provided a search form based on work experience, worker qualifications, date of opening, date of closing vacancies, company website, worker capacity, allowances, language, uniforms. and the length of the application process. This research was conducted to complement those not yet available on the JobStreet website. The data retrieval using web scraping techniques is limited to only between 20 and 200, this is because if you take too much data, it has the potential to cross the threshold of allowing jobstreet website scraping. The job search results displayed on the knowledge-based graphical jobstreet visualization media are in the form of displays in graph form with their relationships and the conclusions of the questionnaire conducted to 20 respondents with 5 question items, namely: the appearance of knowledge-based jobstreet visualization media is very attractive with a percentage of 89% , the information displayed in the media is very clear with a percentage of 88%, the information displayed is very useful with a percentage of 93%, the jobstreet visualization media is also quite easy to use with a percentage of 56% and is suitable for publication with a percentage of 82%.

Item Type: Thesis (Other)
Uncontrolled Keywords: Jobstreet, Knowledge Graph
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: > Teknik Informatika
Depositing User: Mia
Date Deposited: 29 Oct 2021 07:37
Last Modified: 29 Oct 2021 07:37
URI: http://repository.uir.ac.id/id/eprint/3739

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