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Jurnal SINTA 2

ABCD FEATURE EXTRACTION OF IMAGE DERMATOSCOPIC BASED ON MORPHOLOGY ANALYSIS FOR MELANOMA SKIN CANCER DIAGNOSIS

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 3 No. 2 (2012) · pp. 82–90

DOI
10.21609/jiki.v3i2.145
ISSN
2502-9274, 2088-7051
Published
2012-05-29
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

This research present asymmetry, border irregularity, color variation, and diameter (ABCD) feature extraction of image dermatoscopic for melanoma skin cancer diagnosis. ABCD feature is the important information based on morphology analysis of image dermatoscopic lesion. ABCD feature is used to calculate Total Dermatoscopic Value (TDV) for melanoma skin cancer diagnosis. Asymmetry feature consist information of asymmetry and lengthening index of the lesion. Border irregularity feature consist information of compactness index, fractal dimension, edge abruptness, and pigmentation transition from the lesion. Color homogeneity feature consist information of color homogeneity and the correlation between photometry and geometry of the lesion. Diameter extraction is diameter of the lesion. There are three diagnosis that is used on this research i.e. melanoma, suspicious, and benign skin lesion. The experiment uses 30 samples of image dermatoscopic lesion that is suspicious melanoma skin cancer. Based on the experiment, the accuracy of the system is 85% that there are four false diagnoses of 30 samples. Penelitian ini menyajikan ekstraksi fitur citra dermatoskopik untuk diagnosis kanker kulit melanoma berdasarkan asymmetry, border irregularity, color variation, dan diameter (ABCD). Fitur ABCD adalah informasi yang penting berdasarkan analisis morfologi lesi citra dermatoskopik. Fitur tersebut digunakan dalam perhitungan Total Dermatoscopic Value (TDV) untuk diagnosis kanker kulit melanoma. Fitur asymmetry terdiri dari informasi asimetri dan indeks perpanjangan luka. Fitur border irregularity terdiri dari informasi indeks compactness, dimensi fraktal, edge abruptness, dan transisi pigmentasi dari lesi. Warna fitur homogenitas terdiri dari informasi homogenitas warna dan korelasi antara fotometri dan geometri lesi. Ekstraksi diameter adalah diameter lesi. Ada tiga diagnosa yang digunakan pada penelitian ini yaitu melanoma, diduga melanoma, dan benign skin lesion. Percobaan ini menggunakan 30 sampel dari lesi citra dermatoskopik kanker kulit melanoma yang mencurigakan. Berdasarkan percobaan, akurasi dari sistem ini adalah 85% dan terdapat empat diagnosa palsu dari 30 sampel.

Keywords

Collaboration network of the authors

40 authors · 71 co-authorship links · 15 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
  • University of Indonesia
  • University of Brawijaya
  • Universitas Airlangga
  • Hasanuddin University
  • North Sumatra Islamic University
  • Universitas Muhammadiyah Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Nanik SuciatiInstitut Teknologi Sepuluh Nopember9
Diana PurwitasariInstitut Teknologi Sepuluh Nopember8
Handayani TjandrasaInstitut Teknologi Sepuluh Nopember7
Siti RochimahInstitut Teknologi Sepuluh Nopember4
Anny YuniartiInstitut Teknologi Sepuluh Nopember3
Nurtami SoedarsonoUniversity of Indonesia3
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember3
Hari GinardiInstitut Teknologi Sepuluh Nopember2
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember2
Hilya TsaniyaInstitut Teknologi Sepuluh Nopember2
Reggio N. HartonoUniversity of Indonesia2
Hendra MesraHasanuddin University2
Eha Renwi AstutiUniversitas Airlangga2
Ramadhan Hardani PutraUniversitas Airlangga2
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Evan TanuwijayaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Ratih Kartika DewiUniversity of Brawijaya1
Shinta PuspasariAffiliation unknown1
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiNorth Sumatra Islamic University1
Faida RoyaniAffiliation unknown1
Dewi RosidaAffiliation unknown1
Margo PudjiantaraInstitut Teknologi Sepuluh Nopember1
Khakim GhozaliInstitut Teknologi Sepuluh Nopember1
Tri Agung Wahyu HandrianAffiliation unknown1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Nur HayatinUniversitas Muhammadiyah Malang1
Mustika MentariInstitut Teknologi Sepuluh Nopember1
Rizal Setya PerdanaUniversity of Brawijaya1
Fahrur RoziInstitut Teknologi Sepuluh Nopember1
Ahmad Hayam BrilianAffiliation unknown1
Aditya BagusmulyaInstitut Teknologi Sepuluh Nopember1
Mirza Galih KurniawanInstitut Teknologi Sepuluh Nopember1
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember1
Mamluatul Hani’ahAffiliation 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 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">ABCD FEATURE EXTRACTION OF IMAGE DERMATOSCOPIC BASED ON MORPHOLOGY ANALYSIS FOR MELANOMA SKIN CANCER DIAGNOSIS</dc:title>
	<dc:creator>Amaliah, Bilqis</dc:creator>
	<dc:creator>Fatichah, Chastine</dc:creator>
	<dc:creator>Widyanto, M. Rahmat</dc:creator>
	<dc:subject xml:lang="en-US">asimetri</dc:subject>
	<dc:subject xml:lang="en-US">asymmetry</dc:subject>
	<dc:subject xml:lang="en-US">border irregularity</dc:subject>
	<dc:subject xml:lang="en-US">color variation</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">melanoma</dc:subject>
	<dc:subject xml:lang="en-US">ekstraksi fitur</dc:subject>
	<dc:subject xml:lang="en-US">variasi warna</dc:subject>
	<dc:description xml:lang="en-US">This research present asymmetry, border irregularity, color variation, and diameter (ABCD) feature extraction of image dermatoscopic for melanoma skin cancer diagnosis. ABCD feature is the important information based on morphology analysis of image dermatoscopic lesion. ABCD feature is used to calculate Total Dermatoscopic Value (TDV) for melanoma skin cancer diagnosis. Asymmetry feature consist information of asymmetry and lengthening index of the lesion. Border irregularity feature consist information of compactness index, fractal dimension, edge abruptness, and pigmentation transition from the lesion. Color homogeneity feature consist information of color homogeneity and the correlation between photometry and geometry of the lesion. Diameter extraction is diameter of the lesion. There are three diagnosis that is used on this research i.e. melanoma, suspicious, and benign skin lesion. The experiment uses 30 samples of image dermatoscopic lesion that is suspicious melanoma skin cancer. Based on the experiment, the accuracy of the system is 85% that there are four false diagnoses of 30 samples.

Penelitian  ini menyajikan ekstraksi fitur citra dermatoskopik untuk diagnosis kanker kulit melanoma berdasarkan asymmetry, border irregularity, color variation, dan diameter (ABCD). Fitur ABCD adalah informasi yang penting berdasarkan analisis morfologi lesi citra dermatoskopik. Fitur tersebut digunakan dalam perhitungan Total Dermatoscopic Value (TDV) untuk diagnosis kanker kulit melanoma. Fitur asymmetry terdiri dari informasi asimetri dan indeks perpanjangan luka. Fitur border irregularity terdiri dari informasi indeks compactness, dimensi fraktal, edge abruptness, dan transisi pigmentasi dari lesi. Warna fitur homogenitas terdiri dari informasi homogenitas warna dan korelasi antara fotometri dan geometri lesi. Ekstraksi diameter adalah diameter lesi. Ada tiga diagnosa yang digunakan pada penelitian ini yaitu melanoma, diduga melanoma, dan benign skin lesion. Percobaan ini menggunakan 30 sampel dari lesi citra dermatoskopik kanker kulit melanoma yang mencurigakan. Berdasarkan percobaan, akurasi dari sistem ini adalah 85% dan terdapat empat diagnosa palsu dari 30 sampel.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2012-05-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/145</dc:identifier>
	<dc:identifier>10.21609/jiki.v3i2.145</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 3 No. 2 (2010): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 82-90</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v3i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/145/263</dc:relation>
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