Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 9 No. 2 (2015) · pp. 187–196

DOI
10.22146/ijccs.7547
ISSN
2460-7258, 1978-1520
Published
2015-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakPenggunaan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam proses identifikasi salah satu gangguan neurologis pada bagian kepala yang dikenal dalam istilah kedokteran stroke ischemic dari hasil ct scan kepala dengan tujuan untuk mengidentifikasi lokasi yang terkena stroke ischemik. Langkah-langkah yang dilakukan dalam proses identifikasi antara lain ekstraksi citra hasil ct scan kepala dengan menggunakan histogram. Citra hasil proses histogram ditingkatkan intensitas hasil citranya dengan menggunakan threshold otsu sehingga didapatkan hasil pixel yang diberi nilai 1 berkaitan dengan obyek sedangkan pixel yang diberi nilai 0 berkaitan dengan background. Hasil pengukuran digunakan untuk proses clustering image, untuk proses cluster image digunakan fuzzy c-mean (FCM). Hasil clustering merupakan deretan pusat cluster, hasil data digunakan untuk membangun fuzzy inference system (FIS). Sistem inferensi fuzzy yang diterapkan adalah inferensi fuzzy model Takagi-Sugeno-Kang. Dalam penelitian ini ANFIS digunakan untuk mengoptimalkan hasil penentuan lokasi penyumbatan stroke ischemic. Digunakan recursive least square estimator (RLSE) untuk pembelajaran. Hasil RMSE yang didapat pada proses pelatihan sebesar 0.0432053, sedangkan pada proses pengujian dihasilkan tingkat akurasi sebesar 98,66% Kata kunci—stroke ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE Abstract The use of Adaptive Neuro Fuzzy Inference System (ANFIS) methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM) clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS). Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE) for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66% Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

Collaboration network of the authors

31 authors · 42 co-authorship links · 7 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas 'Aisyiyah Surakarta
  • IPB University
  • Universitas Lamappapoleonro
  • Diponegoro University
  • Universitas Amikom Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada3
Aina MusdholifahUniversitas Gadjah Mada2
Sri NurdiatiIPB University1
Jati Sasongko WibowoAffiliation unknown1
Aridhanyati ArifinAffiliation unknown1
Jamila JamilaAffiliation unknown1
Rio KurniawanUniversitas Gadjah Mada1
Rita WiryasaputraUniversitas Gadjah Mada1
Sri Fitri WahyuniAffiliation unknown1
Sherly JayantiAffiliation unknown1
Salahuddin SalahuddinUniversitas Gadjah Mada1
Tedy RismawanUniversitas Gadjah Mada1
Muhammad Lhsan SaritaAffiliation unknown1
Rusdi EfendiAffiliation unknown1
Rukli RukliUniversitas Lamappapoleonro1
Khadijah KhadijahDiponegoro University1
Hence Beedwel LumentutAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Eka WahyudiAffiliation unknown1
Ari Kusuma WardanaUniversitas Gadjah Mada1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Achmad Siddik FathoniUniversitas Gadjah Mada1
Agus SihabuddinUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Azhari SubanarAffiliation unknown1
Agus HarjokoUniversitas Gadjah Mada1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Subanar SubanarUniversitas Gadjah Mada1
Azhari AzhariAffiliation 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://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">Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)</dc:title>
	<dc:creator>Kusanti, Jani</dc:creator>
	<dc:creator>Hartati, Sri</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">AbstrakPenggunaan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam proses identifikasi salah satu gangguan neurologis pada bagian kepala yang dikenal dalam istilah kedokteran stroke ischemic dari hasil ct scan kepala dengan tujuan untuk mengidentifikasi lokasi  yang terkena stroke ischemik. Langkah-langkah yang dilakukan dalam proses identifikasi antara lain ekstraksi citra hasil ct scan kepala dengan menggunakan histogram. Citra hasil proses histogram ditingkatkan intensitas hasil citranya dengan menggunakan threshold otsu sehingga didapatkan hasil pixel yang diberi nilai 1 berkaitan dengan obyek sedangkan pixel yang diberi nilai 0 berkaitan dengan background. Hasil pengukuran digunakan untuk proses clustering image, untuk proses cluster image digunakan fuzzy c-mean (FCM). Hasil clustering merupakan deretan pusat cluster, hasil  data digunakan untuk membangun fuzzy inference system (FIS). Sistem inferensi fuzzy yang diterapkan adalah inferensi fuzzy model Takagi-Sugeno-Kang. Dalam penelitian ini ANFIS digunakan untuk mengoptimalkan hasil penentuan lokasi penyumbatan stroke ischemic. Digunakan recursive least square estimator (RLSE) untuk pembelajaran. Hasil RMSE yang didapat pada proses pelatihan sebesar 0.0432053, sedangkan pada proses pengujian dihasilkan tingkat akurasi sebesar 98,66% Kata kunci—stroke ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE  Abstract            The use of Adaptive Neuro Fuzzy Inference System (ANFIS) methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM) clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS). Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE) for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66% Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2015-07-31</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/7547</dc:identifier>
	<dc:identifier>10.22146/ijccs.7547</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 9, No 2 (2015): July; 187-196</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/7547/5870</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2015 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>