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Trafika, Evania
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Computer Science & IT Electrical & Electronics Engineering

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Designing UI/UX for CareerSite Website Using LeanUX Method Najib, Mochammad; Trafika, Evania
Information Technology International Journal Vol. 1 No. 1 (2023): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v1i1.3

Abstract

One of the causes of the increasing number of unemployed in Indonesia is due to fresh graduates who do not have sufficient information about careers so they are still unsure about choosing a job that suits their interests. Based on these problems, a CareerSite website was created which will equip the community in choosing and developing their interests and talents according to the job they want. Creating a CareerSite website interface design will make it easier for the public to develop their interests and talents. The research method used in this research is using LeanUX and with Usability Testing. 10 respondents were taken to try the prototype from the CareerSite website then continued to provide an assessment and feedback through a questionnaire. The results obtained from usability testing using the UEQ-S obtained a scales value on the pragmatic quality aspect of 1.900 and on the hedonic quality value aspect of 0.925. 60% of respondents stated that the appearance of the CareerSite website is efficient, fun and creative when used. Feedback obtained from respondents can be used as further research to develop this CareerSite website better.
The Accuracy of Supervised Learning Algorithm on Machine Learning Implementation: a Literature Review Tarangga, Bagas; Trafika, Evania
Information Technology International Journal Vol. 1 No. 2 (2023): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v1i2.17

Abstract

Machine Learning has become an integral element in technological development, having a significant impact on various sectors of life. This study explores the contribution of Machine Learning in big data processing, automated decision making, and predictive system development. The advantages of Machine Learning, especially in supervised learning, are emphasized by discussing algorithms such as regression, Support Vector Machines (SVM), and Neural Networks. Literature research includes five journals related to supervised learning applications, highlighting findings such as the effectiveness of the Random Forest algorithm in diagnosing pregnancy, the contribution of the SVM model in predicting student study periods, and the level of accuracy with the hybrid LSH and k-NN methods for weather prediction. The practical implementation of fruit detection using cameras shows real application in facilitating price checks and fruit recognition. In conclusion, the literature review confirms the potential and relevance of Machine Learning techniques, especially supervised learning, in providing solutions to various challenges in various sectors. It is recommended that further research explore different industrial sectors or specific case studies to gain a more comprehensive and relevant perspective on current trends in the development of Machine Learning techniques
Co-Authors Mochammad Najib Tarangga, Bagas