Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurence Matrix (GLCM)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 8 No. 1 (2014) · pp. 59–68

DOI
10.22146/ijccs.3496
ISSN
2460-7258, 1978-1520
Published
2014-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakKanker payudara adalah penyakit yang paling umum dideritaoleh wanitapadabanyak negara. Pemeriksaan kanker payudara dapat dilakukan dengan menggunakan mamografi.Padapenelitianini, pendekatan yang diusulkan bertujuanuntuk mengklasifikasi mammogram berdasarkan tiga kelas yaitukelas normal, tumor jinak, dan tumor ganas. Sistem yang diusulkan terdiri dari empat langkah utamayaitu preprosesing, segmentasi, ekstraksi fitur dan klasifikasi. Padatahappreprosesingakandilakukangrayscale, interpolasi, amoeba mean filter dan segmentasi. Ekstraksi ciri menggunakan Gray Level Cooccurence Matrix (GLCM) danakan dihitung ciri-ciristatistikpada 4 arah (d=1 dan d=2), GLCM 8 arah(d=1) dan GLCM 16 arah (d=2).Fitur yang digunakanada 5 yaitukontras, energi, entropi, korelasi dan homogenitas. Langkah terakhir adalah klasifikasi menggunakan Backpropagation. Beberapa parameter penting divariasikan dalam proses ini seperti learning rate dan jumlah node dalam lapisan tersembunyi. Hasil penelitian menunjukkan bahwa fitur ekstraksi GLCM 4 arah(denganjarak d=1memiliki akurasi terbaik dalammengklasifikasimammogram yaitusebesar 81,1% dankhususpadaarah akurasi klasifikasidiperolehsebesar 100%. AbstractBreast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps: preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2), GLCM 8 angles and GLCM 16 angles. The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in hidden layer. The research result suggest that extraction feature in 4 angles ( and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in accuracy is 100%.

Keywords

Collaboration network of the authors

40 authors · 52 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.

  • Universitas Gadjah Mada
  • Universitas Stikubank
  • University of Indonesia
  • State University of Semarang
  • Universitas Pendidikan Ganesha
  • State University of Medan
  • Duta Wacana Christian University
  • 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
Edi WinarkoUniversitas Gadjah Mada3
Retantyo WardoyoUniversitas Gadjah Mada2
Sri T. HartatiUniversitas Gadjah Mada2
Edy WinarnoUniversitas Stikubank2
Aniati Murni ArymurthyUniversity of Indonesia2
Kridalaksana Harsa AwangAffiliation unknown1
Zaenal AbidinState University of Semarang1
Yustina RetnoAffiliation unknown1
Abriyono AbriyonoAffiliation unknown1
I Gede Aris GunadiUniversitas Pendidikan Ganesha1
Rocky Yefrenes DillakUniversitas Gadjah Mada1
Ikhwan RusliantoUniversitas Gadjah Mada1
Rahmi HidayatiUniversitas Gadjah Mada1
Salman AliajiUniversitas Gadjah Mada1
Mahmuddin YunusAffiliation unknown1
Hermawan SyahputraState University of Medan1
Sri SuwarnoDuta Wacana Christian University1
Rajif Agung YunmarSumatera Institute of Technology1
Nurmiyati TamatjitaUniversitas Gadjah Mada1
La Ode Hasnuddin S. SagalaUniversitas Gadjah Mada1
Nicodemus Mardanus SetiohardjoPoliteknik Negeri Kupang1
I Putu Adi PratamaUniversitas Gadjah Mada1
Herman HermanUniversitas Gadjah Mada1
Dian Hafidh ZulfikarUniversitas Gadjah Mada1
Made Leo RadhityaInstitut Bisnis dan Teknologi Indonesia1
Kharis SyabanUniversitas Gadjah Mada1
Pawit RiantoAffiliation unknown1
Adri PriadanaUniversitas Gadjah Mada1
Ade NurhopipahUniversitas Amikom Purwokerto1
Aina MusdholifahUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Angga Maulana PurbaUniversitas Gadjah Mada1
Mohammad Edi WibowoAffiliation unknown1
Wahyono WahyonoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Anny Kartika SariUniversitas Gadjah Mada1
Alfian Abdul JalidUniversitas Gadjah Mada1
Nicolaus Euclides Wahyu NugrohoUniversitas Gadjah Mada1

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (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-US">Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurence Matrix (GLCM)</dc:title>
	<dc:creator>Listia, Refta</dc:creator>
	<dc:creator>Harjoko, Agus</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">mammogram, GLCM, Backpropagation</dc:subject>
	<dc:description xml:lang="en-US">AbstrakKanker payudara adalah penyakit yang paling umum dideritaoleh wanitapadabanyak negara. Pemeriksaan kanker payudara dapat dilakukan dengan menggunakan mamografi.Padapenelitianini, pendekatan yang diusulkan bertujuanuntuk mengklasifikasi mammogram berdasarkan tiga kelas yaitukelas normal, tumor jinak, dan tumor ganas. Sistem yang diusulkan terdiri dari empat langkah utamayaitu preprosesing, segmentasi, ekstraksi fitur dan klasifikasi. Padatahappreprosesingakandilakukangrayscale, interpolasi, amoeba mean filter dan segmentasi. Ekstraksi ciri menggunakan Gray Level Cooccurence Matrix (GLCM) danakan dihitung ciri-ciristatistikpada 4 arah (d=1 dan d=2) , GLCM 8 arah(d=1) dan GLCM 16 arah (d=2).Fitur yang digunakanada 5 yaitukontras, energi, entropi, korelasi dan homogenitas. Langkah terakhir adalah klasifikasi menggunakan Backpropagation. Beberapa parameter penting divariasikan dalam proses ini seperti learning rate dan jumlah node dalam lapisan tersembunyi. Hasil penelitian menunjukkan bahwa fitur ekstraksi GLCM 4 arah(denganjarak d=1memiliki akurasi terbaik dalammengklasifikasimammogram yaitusebesar 81,1% dankhususpadaarah akurasi klasifikasidiperolehsebesar 100%.  AbstractBreast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps : preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2),  GLCM 8 angles and GLCM 16 angles.  The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in  hidden layer. The research result suggest that extraction feature in 4 angles ( and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in accuracy is 100%.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2014-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/3496</dc:identifier>
	<dc:identifier>10.22146/ijccs.3496</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 8, No 1 (2014): January; 59-68</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/3496/3022</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2014 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>