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.
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-COVER_a.pdf Download (923kB) | Preview |
|
![[img]](https://repository.unika.ac.id/style/images/fileicons/text.png) |
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB I_a.pdf Restricted to Registered users only Download (487kB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB II_a.pdf Restricted to Registered users only Download (341kB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB III_a.pdf Restricted to Registered users only Download (450kB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB IV_a.pdf Restricted to Registered users only Download (506kB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB V_a.pdf Restricted to Registered users only Download (1MB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-BAB VI_a.pdf Restricted to Registered users only Download (320kB) |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-DAPUS_a.pdf Download (414kB) | Preview |
|
|
Text
17.K1.0033-KEZIA SHIENNY JULIANA HERTANTO-LAMP_a.pdf Restricted to Registered users only 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: | 18 Sep 2024 02:58 |
| URI: | http://repository.unika.ac.id/id/eprint/31392 |
Actions (login required)
 |
View Item |
