Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet
Rhizome is modification of stem that grows below the surface of the soil and produce new bud and roots from its segments. Besides being used as spices, rhizome also used by people as ingredients of traditional medicine to treat various diseases. This proves that rhizome has many benefits. However, t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Udayana
2020-02-01
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| Series: | Journal of Electrical, Electronics and Informatics |
| Online Access: | https://ojs.unud.ac.id/index.php/JEEI/article/view/56217 |
| _version_ | 1818864506837663744 |
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| author | Krisna Hany Indrani Duman Care Khrisne I Made Arsa Suyadnya |
| author_facet | Krisna Hany Indrani Duman Care Khrisne I Made Arsa Suyadnya |
| author_sort | Krisna Hany Indrani |
| collection | DOAJ |
| description | Rhizome is modification of stem that grows below the surface of the soil and produce new bud and roots from its segments. Besides being used as spices, rhizome also used by people as ingredients of traditional medicine to treat various diseases. This proves that rhizome has many benefits. However, the ability to recognize types of rhizome can only be done by certain people because rhizome has variety of types, aromas, and different colors. This study was designed to build an Android based application to recognize the types of rhizome, so that people can recognize types of rhizome without having special knowledge. The application was built using Convolutional Neural Network (CNN) methods with SqueezeNet architecture model. This study used 9 class of rhizome with Zingiberaceae Family, namely Bangle, Jahe, Kunyit Kuning, Kencur, Lengkuas, Temu Kunci, Temu Ireng, Temu Mangga, and Temulawak. Testing is carried out to know the performance of application such as accuracy level of application in recognize types of rhizome. Based on the results of testing with 54 rhizomes sample images, the application is capable of recognizing rhizomes types by obtaining a top-1 accuracy value of 41% and top-5 accuracy value of 81%. |
| first_indexed | 2024-12-19T10:32:45Z |
| format | Article |
| id | doaj.art-9ff01541e9cb4f48a0123bf540a4d8b2 |
| institution | Directory Open Access Journal |
| issn | 2549-8304 2622-0393 |
| language | English |
| last_indexed | 2024-12-19T10:32:45Z |
| publishDate | 2020-02-01 |
| publisher | Universitas Udayana |
| record_format | Article |
| series | Journal of Electrical, Electronics and Informatics |
| spelling | doaj.art-9ff01541e9cb4f48a0123bf540a4d8b22022-12-21T20:25:42ZengUniversitas UdayanaJournal of Electrical, Electronics and Informatics2549-83042622-03932020-02-0141101410.24843/JEEI.2020.v04.i01.p0256217Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNetKrisna Hany IndraniDuman Care KhrisneI Made Arsa SuyadnyaRhizome is modification of stem that grows below the surface of the soil and produce new bud and roots from its segments. Besides being used as spices, rhizome also used by people as ingredients of traditional medicine to treat various diseases. This proves that rhizome has many benefits. However, the ability to recognize types of rhizome can only be done by certain people because rhizome has variety of types, aromas, and different colors. This study was designed to build an Android based application to recognize the types of rhizome, so that people can recognize types of rhizome without having special knowledge. The application was built using Convolutional Neural Network (CNN) methods with SqueezeNet architecture model. This study used 9 class of rhizome with Zingiberaceae Family, namely Bangle, Jahe, Kunyit Kuning, Kencur, Lengkuas, Temu Kunci, Temu Ireng, Temu Mangga, and Temulawak. Testing is carried out to know the performance of application such as accuracy level of application in recognize types of rhizome. Based on the results of testing with 54 rhizomes sample images, the application is capable of recognizing rhizomes types by obtaining a top-1 accuracy value of 41% and top-5 accuracy value of 81%.https://ojs.unud.ac.id/index.php/JEEI/article/view/56217 |
| spellingShingle | Krisna Hany Indrani Duman Care Khrisne I Made Arsa Suyadnya Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet Journal of Electrical, Electronics and Informatics |
| title | Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet |
| title_full | Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet |
| title_fullStr | Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet |
| title_full_unstemmed | Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet |
| title_short | Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet |
| title_sort | android based application for rhizome medicinal plant recognition using squeezenet |
| url | https://ojs.unud.ac.id/index.php/JEEI/article/view/56217 |
| work_keys_str_mv | AT krisnahanyindrani androidbasedapplicationforrhizomemedicinalplantrecognitionusingsqueezenet AT dumancarekhrisne androidbasedapplicationforrhizomemedicinalplantrecognitionusingsqueezenet AT imadearsasuyadnya androidbasedapplicationforrhizomemedicinalplantrecognitionusingsqueezenet |