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FRACTAL DIMENSION AND LACUNARITY COMBINATION FOR PLANT LEAF CLASSIFICATION

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 9 No. 2 (2016) · pp. 96–105

DOI
10.21609/jiki.v9i2.385
ISSN
2502-9274, 2088-7051
Published
2016-06-25
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Plants play important roles for the existence of all beings in the world. High diversity of plant’s species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant’s leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy.

Keywords

Collaboration network of the authors

40 authors · 76 co-authorship links · 18 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Universitas Halu Oleo
  • Universitas Airlangga
  • University of Brawijaya
  • Hasanuddin University
  • Universitas Sembilanbelas November Kolaka
  • University of Indonesia
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Diana PurwitasariInstitut Teknologi Sepuluh Nopember11
Handayani TjandrasaInstitut Teknologi Sepuluh Nopember9
Anny YuniartiInstitut Teknologi Sepuluh Nopember5
Siti RochimahInstitut Teknologi Sepuluh Nopember4
Erika MaulidiyaInstitut Teknologi Sepuluh Nopember4
Jayanti Yusmah SariUniversitas Halu Oleo3
Yuna SugianelaInstitut Teknologi Sepuluh Nopember2
Imam KuswardayanInstitut Teknologi Sepuluh Nopember2
Hari GinardiInstitut Teknologi Sepuluh Nopember2
Isye ArieshantiInstitut Teknologi Sepuluh Nopember2
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember2
Syavira Tiara ZulkarnainInstitut Teknologi Sepuluh Nopember2
Narandha Arya RanggiantoInstitut Teknologi Sepuluh Nopember2
Sherly Rosa AnggraeniInstitut Teknologi Sepuluh Nopember2
Hilya TsaniyaInstitut Teknologi Sepuluh Nopember2
Hendra MesraHasanuddin University2
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember2
Eha Renwi AstutiUniversitas Airlangga2
Ramadhan Hardani PutraUniversitas Airlangga2
Sutardi SutardiUniversitas Halu Oleo2
Faida RoyaniAffiliation unknown2
Nurfitria NingsiUniversitas Sembilanbelas November Kolaka1
SarimuddinAffiliation unknown1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
M. Rahmat WidyantoUniversity of Indonesia1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Evan TanuwijayaInstitut Teknologi Sepuluh Nopember1
Bernardinus ArisandiAffiliation unknown1
Arya Yudhi WijayaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Ratih Kartika DewiUniversity of Brawijaya1
Lim Yohanes StefanusAffiliation unknown1
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiNorth Sumatra Islamic University1
Tri Anggraeny FettyAffiliation unknown1
Lalu Muhammad DzakiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">FRACTAL DIMENSION AND LACUNARITY COMBINATION FOR PLANT LEAF CLASSIFICATION</dc:title>
	<dc:creator>Muchtar, Mutmainnah</dc:creator>
	<dc:creator>Suciati, Nanik</dc:creator>
	<dc:creator>Fatichah, Chastine</dc:creator>
	<dc:subject xml:lang="en-US">leaf classification</dc:subject>
	<dc:subject xml:lang="en-US">fractal dimension</dc:subject>
	<dc:subject xml:lang="en-US">lacunarity</dc:subject>
	<dc:subject xml:lang="en-US">box counting</dc:subject>
	<dc:subject xml:lang="en-US">gliding box</dc:subject>
	<dc:description xml:lang="en-US">Plants play important roles for the existence of all beings in the world. High diversity of plant’s species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant’s leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2016-06-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/385</dc:identifier>
	<dc:identifier>10.21609/jiki.v9i2.385</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 9 No. 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 96-105</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v9i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/385/351</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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