Identifying types of facial skin / Nur Saina Haji Basri

There are many type of facial skin includes normal, dry, oily, combination and sensitive. In this research, it will be concentrated on three types of facial skin only which are oily, dry and combination skin. Users might have oily skin, dry skin or combination skin. This system will help users to id...

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Main Author: Nur Saina, Haji Basri
Format: Thesis
Language:English
Published: 2012
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/33536/1/33536.pdf
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author Nur Saina, Haji Basri
author_facet Nur Saina, Haji Basri
author_sort Nur Saina, Haji Basri
collection UITM
description There are many type of facial skin includes normal, dry, oily, combination and sensitive. In this research, it will be concentrated on three types of facial skin only which are oily, dry and combination skin. Users might have oily skin, dry skin or combination skin. This system will help users to identify which category the user’s skin belongs to. So, it is easier for the users to choose the right facial skin product that is suit with the user skin due to various products that can be found in the market nowadays. K-Means clustering technique are used for this project to cluster a partition of skin to identify either the regions of skin are oily or dry and determine either the user skin belongs to oily group, dry group or combination group which is combination of oily and dry skin. The result of this technique obtained 94% of precision rate and 4% of error rate which make the accuracy rate is 94%. This result is compared with observation’s result made earlier which is done experimentally to support the accuracy of the result.
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spelling oai:ir.uitm.edu.my:335362020-08-14T02:31:19Z https://ir.uitm.edu.my/id/eprint/33536/ Identifying types of facial skin / Nur Saina Haji Basri Nur Saina, Haji Basri Mathematical statistics. Probabilities Multivariate analysis. Cluster analysis. Longitudinal method Philosophy. Theory. Classification. Methodology There are many type of facial skin includes normal, dry, oily, combination and sensitive. In this research, it will be concentrated on three types of facial skin only which are oily, dry and combination skin. Users might have oily skin, dry skin or combination skin. This system will help users to identify which category the user’s skin belongs to. So, it is easier for the users to choose the right facial skin product that is suit with the user skin due to various products that can be found in the market nowadays. K-Means clustering technique are used for this project to cluster a partition of skin to identify either the regions of skin are oily or dry and determine either the user skin belongs to oily group, dry group or combination group which is combination of oily and dry skin. The result of this technique obtained 94% of precision rate and 4% of error rate which make the accuracy rate is 94%. This result is compared with observation’s result made earlier which is done experimentally to support the accuracy of the result. 2012-07-01 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/33536/1/33536.pdf Identifying types of facial skin / Nur Saina Haji Basri. (2012) Degree thesis, thesis, Universiti Teknologi MARA Cawangan Perak. <http://terminalib.uitm.edu.my/33536.pdf>
spellingShingle Mathematical statistics. Probabilities
Multivariate analysis. Cluster analysis. Longitudinal method
Philosophy. Theory. Classification. Methodology
Nur Saina, Haji Basri
Identifying types of facial skin / Nur Saina Haji Basri
title Identifying types of facial skin / Nur Saina Haji Basri
title_full Identifying types of facial skin / Nur Saina Haji Basri
title_fullStr Identifying types of facial skin / Nur Saina Haji Basri
title_full_unstemmed Identifying types of facial skin / Nur Saina Haji Basri
title_short Identifying types of facial skin / Nur Saina Haji Basri
title_sort identifying types of facial skin nur saina haji basri
topic Mathematical statistics. Probabilities
Multivariate analysis. Cluster analysis. Longitudinal method
Philosophy. Theory. Classification. Methodology
url https://ir.uitm.edu.my/id/eprint/33536/1/33536.pdf
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