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All Journal Scientica: Jurnal Ilmiah Sains dan Teknologi ITIJ
Tarangga, Bagas
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences Other

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Journal : ITIJ

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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 Dhian Satria Yudha Kartika Fitri, Anindo Saka Trafika, Evania