COMPARISON OF NAÏVE BAYES AND SUPPORT VECTOR MACHINE (SVM) ALGORITHMS IN MASKED FACE DETECTION

HERTANTO, KEZIA SHIENNY JULIANA (2023) COMPARISON OF NAÏVE BAYES AND SUPPORT VECTOR MACHINE (SVM) ALGORITHMS IN MASKED FACE DETECTION. Other thesis, Universitas Katholik Soegijapranata Semarang.

[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-COVER_a.pdf
Download (923kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB I_a.pdf
Download (487kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB II_a.pdf
Restricted to Registered users only
Download (341kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB III_a.pdf
Download (450kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB IV_a.pdf
Download (506kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB V_a.pdf
Download (1MB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB VI_a.pdf
Download (320kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-DAPUS_a.pdf
Download (414kB)
[img] Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-LAMP_a.pdf
Download (270kB)

Abstract

Two machine learning methods that both process images will be compared in this study. The image of human face that wears a is an example of an object that will be used to process with Orange Data Mining software. The system will search through existing datasets using Machine Learning Naïve Bayes and Support Vector Machine (SVM) with image analytics. This research will find out which method is most accurate in detecting facial images of people wearing masks or not. According to the study's findings, the SVM approach can predict class instances accurately with an accuracy level of 1, which makes it more predictive than Naive Bayes.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 005 Computer programming, programs & data
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 04 Apr 2023 06:00
Last Modified: 04 Apr 2023 06:00
URI: http://repository.unika.ac.id/id/eprint/31392

Actions (login required)

View Item View Item
Unika Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.