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Classification of Cassava (Manihot sp.) Leaf Variants Using Transfer Learning

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 11 No. 2 (2023) · hlm. 442–452

DOI
10.52549/ijeei.v11i2.4685
ISSN
2089-3272, 2089-3272
Terbit
2023-06-15
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

There are several types of cassava leaves with different characteristics, tastes, and nutritional content. Some people use cassava leaves as a vegetable ingredient for daily consumption as a source of fiber and minerals. However, people often have difficulty identifying the different types of cassava leaves, including cassava leaf variants that are locally referred to as gajah, karet, and mentega. This study aims to use transfer learning to identify the variant of cassava leaves. The Inception v3 architecture was selected to build the classification model. To demonstrate the superiority of transfer learning, the Inception v3 architecture was run with two different weights. The first weight was randomly initialized, while the second weight was taken from pre-trained weights from ImageNet. The experimental results show that the classification accuracy rate using the pre-trained weights reached 95.76%. This indicates that the classification model used in this study is promising and can be used for practical purposes in everyday life.

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6 penulis · 15 tautan kepenulisan bersama · 5 lintas organisasi

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  • International Islamic University Malaysia
  • Telkom University
  • Organisasi berbeda
  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Teddy Surya GunawanInternational Islamic University Malaysia1
Mira KartiwiInternational Islamic University Malaysia1
Siti Hajar YusoffInternational Islamic University Malaysia1
Fatin Najihah Muhamad ZamriInternational Islamic University Malaysia1
Yasir Mohd MustafahInternational Islamic University Malaysia1

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Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Classification of Cassava (Manihot sp.) Leaf Variants Using Transfer Learning</dc:title>
	<dc:creator xml:lang="en">Pratondo, Agus</dc:creator>
	<dc:subject xml:lang="en">cassava leaves gajah karet metega inception v3 transfer learning</dc:subject>
	<dc:description xml:lang="en">There are several types of cassava leaves with different characteristics, tastes, and nutritional content. Some people use cassava leaves as a vegetable ingredient for daily consumption as a source of fiber and minerals. However, people often have difficulty identifying the different types of cassava leaves, including cassava leaf variants that are locally referred to as gajah, karet, and mentega. This study aims to use transfer learning to identify the variant of cassava leaves. The Inception v3 architecture was selected to build the classification model. To demonstrate the superiority of transfer learning, the Inception v3 architecture was run with two different weights. The first weight was randomly initialized, while the second weight was taken from pre-trained weights from ImageNet. The experimental results show that the classification accuracy rate using the pre-trained weights reached 95.76%. This indicates that the classification model used in this study is promising and can be used for practical purposes in everyday life.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2023-06-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/4685</dc:identifier>
	<dc:identifier>10.52549/ijeei.v11i2.4685</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 11 No. 2: June 2023; 442-452</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/4685/834</dc:relation>
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