Jurnal SINTA 2
Optimasi Model Segmentasi Citra Metode Fuzzy Divergence Pada Citra Luka Kronis Menggunakan Algoritma Genetika
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 3 No. 1 (2016) · pp. 66–74
- ISSN
- 2528-6579, 2355-7699
- Published
- 2016-03-17
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- en
Abstract
AbstrakLuka kronis merupakan masalah yang masih terbilang berat dalam penanganan, memerlukan ketekunan, biaya mahal, tenaga terlatih dan terampil. Proses pengkajian luka masih dilakukan secara manual, membutuhkan waktu yang cukup lama dan menghasilkan hasil yang lebih subyektif. Dengan adanya permasalahan tersebut, maka dibutuhkan sistem yang dapat membantu pengkajian luka dengan pendekatan citra digital atau dikenal dengan istilah digital planimetry. Fokus permasalahan yang diselesaikan hanya sebatas pada penggolongan komposisi jaringan luka dengan pendekatan segmentasi citra. Pada task segmentasi citra, algoritma yang digunakan yaitu fuzzy divergence yang dioptimasi menggunakan algoritma genetika untuk pemilihan nilai threshold optimal. Pada algoritma genetika, representasi kromosom berupa real-coded, proses reproduksi meliputi operasi extended intermediate crossover dan random mutation, serta metode seleksi elit dengan penambahan mekanisme random injection. Metode yang diusulkan dapat digunakan untuk mengoptimasi model segmentasi citra multilevel thresholding dengan meminimalkan nilai fuzzy divergence dengan parameter algoritma genetika; meliputi ukuran populasi sebesar 60, kombinasi ukuran cr dan mr secara berturut-turut 0.6 dan 0.4, dan ukuran generasi sebesar 100. Kemudian, berdasarkan evaluasi hasil segmentasi citra menggunakan Standar Deviasi (SD), distribusi Gamma menghasilkan hasil segmentasi yang lebih baik.Kata kunci: luka kronis, digital planimetry, segmentasi citra, fuzzy divergence, algoritma genetikaAbstractChronic wounds are a problem that is still difficult in wound management, require persistence, high cost for treatment, and trained-skilled personnel. In wound management, the assessment process are still performed manually, however it’s very time-consuming and produce more subjective outcomes. Given these problems, there is a need for a system that helps wound assessment with the approach in measuring wound size using digital images, known as digital planimetry. In this work, the focus only on wound tissue classification using image segmentation. In image segmentation, the algorithm used is fuzzy divergence that optimized by using genetic algorithm for selecting optimal threshold. For genetic algorithm, the representation of chromosomes is real-coded, then reproduction process using the extended intermediate crossover and random mutation, and elitism selection with the addition of random injection mechanism. The proposed method can use to optimize image segmentation multilevel thresholding by minimizing the value of fuzzy divergence with genetic algorithm parameters which includes the size of the population is 60, the combination of size Cr and Mr respectively 0.6 and 0.4, and the size of generation is 100. Then, based on the evaluation result of image segmentation using Standard Deviation (SD), found that Gamma distribution leads better segmentation as compared to others.Keywords: chronic wounds, digital planimetry, image segmentation, fuzzy divergence, genetic algorithm
Collaboration network of the authors
40 authors · 63 co-authorship links · 4 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.
- University of Brawijaya
- Institut Teknologi Sepuluh Nopember
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Yusuf Priyo Anggodo | University of Brawijaya | 3 |
| Asyrofa Rahmi | University of Brawijaya | 2 |
| Syaiful Anam | University of Brawijaya | 2 |
| Adyan Nur Alfiyatin | University of Brawijaya | 2 |
| Putra Pandu Adikara | University of Brawijaya | 2 |
| Firnanda Al Islama Achyunda Putra | University of Brawijaya | 1 |
| Fitri Utaminingrum | University of Brawijaya | 1 |
| Tri Hadiah Muliawati | Institut Teknologi Sepuluh Nopember | 1 |
| Reddy Alexandro | Institut Teknologi Sepuluh Nopember | 1 |
| Ida Wahyuni | University of Brawijaya | 1 |
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Chastine Fatichah | Institut Teknologi Sepuluh Nopember | 1 |
| Diana Purwitasari | Institut Teknologi Sepuluh Nopember | 1 |
| Satrio Agung Wicaksono | University of Brawijaya | 1 |
| Rochmat Setiyawan | University of Brawijaya | 1 |
| Budi Darma Setiyawan | University of Brawijaya | 1 |
| Ari Hernawan | University of Brawijaya | 1 |
| Ervin Yohannes | University of Brawijaya | 1 |
| Hilman Nuril Hadi | University of Brawijaya | 1 |
| Ria Febriyana | University of Brawijaya | 1 |
| Vitara Nindya Putri Hasan | University of Brawijaya | 1 |
| Mohammad Zoqi Sarwani | University of Brawijaya | 1 |
| Luthfi Hidayat | University of Brawijaya | 1 |
| Rifki Setya Armanda | University of Brawijaya | 1 |
| Garsinia Ely Riani | University of Brawijaya | 1 |
| Fita Lathifatul Mu’asyaroh | Affiliation unknown | 1 |
| Agus Wahyu Widodo | University of Brawijaya | 1 |
| Diva Kurnianingtyas | University of Brawijaya | 1 |
| Muchammad Chandra Cahyo Utomo | University of Brawijaya | 1 |
| Candra Fajri Ananda | University of Brawijaya | 1 |
| Farhanna Mar’i | University of Brawijaya | 1 |
| Cleoputri Yusainy | University of Brawijaya | 1 |
| Indriati . | Affiliation unknown | 1 |
| Anshar Zulhilmi | University of Brawijaya | 1 |
| Muhammad Fathur Rahman Khairul | University of Brawijaya | 1 |
| Zahra Asma Annisa | University of Brawijaya | 1 |
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.