Handwriting Character Recognition using Vector Quantization Technique
This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recogn...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Negeri Malang
2019-12-01
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| Series: | Knowledge Engineering and Data Science |
| Online Access: | http://journal2.um.ac.id/index.php/keds/article/view/9869 |
| _version_ | 1811274266087260160 |
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| author | Haviluddin Haviluddin Rayner Alfred Ni’mah Moham Herman Santoso Pakpahan Islamiyah Islamiyah Hario Jati Setyadi |
| author_facet | Haviluddin Haviluddin Rayner Alfred Ni’mah Moham Herman Santoso Pakpahan Islamiyah Islamiyah Hario Jati Setyadi |
| author_sort | Haviluddin Haviluddin |
| collection | DOAJ |
| description | This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results. |
| first_indexed | 2024-04-12T23:15:53Z |
| format | Article |
| id | doaj.art-33ea466fe87249f1a5595d56fe048746 |
| institution | Directory Open Access Journal |
| issn | 2597-4602 2597-4637 |
| language | English |
| last_indexed | 2024-04-12T23:15:53Z |
| publishDate | 2019-12-01 |
| publisher | Universitas Negeri Malang |
| record_format | Article |
| series | Knowledge Engineering and Data Science |
| spelling | doaj.art-33ea466fe87249f1a5595d56fe0487462022-12-22T03:12:41ZengUniversitas Negeri MalangKnowledge Engineering and Data Science2597-46022597-46372019-12-0122828910.17977/um018v2i22019p82-894529Handwriting Character Recognition using Vector Quantization TechniqueHaviluddin Haviluddin0Rayner AlfredNi’mah MohamHerman Santoso PakpahanIslamiyah IslamiyahHario Jati Setyadi(SCOPUS ID: 56596793000, Universitas Mulawarman)This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.http://journal2.um.ac.id/index.php/keds/article/view/9869 |
| spellingShingle | Haviluddin Haviluddin Rayner Alfred Ni’mah Moham Herman Santoso Pakpahan Islamiyah Islamiyah Hario Jati Setyadi Handwriting Character Recognition using Vector Quantization Technique Knowledge Engineering and Data Science |
| title | Handwriting Character Recognition using Vector Quantization Technique |
| title_full | Handwriting Character Recognition using Vector Quantization Technique |
| title_fullStr | Handwriting Character Recognition using Vector Quantization Technique |
| title_full_unstemmed | Handwriting Character Recognition using Vector Quantization Technique |
| title_short | Handwriting Character Recognition using Vector Quantization Technique |
| title_sort | handwriting character recognition using vector quantization technique |
| url | http://journal2.um.ac.id/index.php/keds/article/view/9869 |
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