SISTEM PENGENALAN OTOMATIS DIAMETER CITRA MANTOUX UNTUK DETEKSI DINI PENYAKIT TBC KELENJAR
Abstract
The Mantoux test is a test performed to detect tuberculosis, a test performed by injecting protein into the skin tissue of the left hand, this test
is mostly done because it feels more accurate the results, the way the Mantoux test works is to measure the diameter of the protein-given area, if the diameter is less than 5 mm, the patient is not diagnosed with tuberculosis, but if the diameter of the area given by the protein exceeds 5 mm then the patient is diagnosed with tuberculosis. To help simplify the calculation, the area of the Mantoux test needs to be image processing. In this study image processing uses K-Means clustering method with edge detection, then the calculation of diameter after edge detection. And the end result is the image of the Mantoux test area can easily be calculated in diameter, without having to make measurements using a manual ruler.
References
Agustina, S., Yhudo, D., Santoso, H., Marnasusanto, N., Tirtana, A., & Khusnu, F. (2012). CLUSTERING KUALITAS BERAS BERDASARKAN CIRI FISIK MENGGUNAKAN METODE K-MEANS Algoritma. Clustering K-Means, 1–7.
Arifin, T., Riana, D., & Hapsari, G. I. (2014). Klasifikasi Statistikal Tekstur Sel Pap Smear Dengan Decesion Tree. Jurnal Informatika, (1), 1–7.
Arsy, L., Nurhayati, O. D., & Martono, K. T. (2016). Aplikasi Pengolahan Citra Digital Meat Detection Dengan Metode Segmentasi K-Mean Clustering Berbasis OpenCV Dan Eclipse. Jurnal Teknologi Dan Sistem Komputer, 4(2), 322–332. https://doi.org/10.14710/jtsiskom.4.2.2016.322-332
Aryo, H., & Chastine, F. (2017). Segmentasi dan pemisahan sel darah putih bersentuhan menggunakan k-means dan hierarchical clustering analysis pada citra leukemia myeloid akut, 15, 140–151.
Atina. (2017). Segmentasi Citra Paru Menggunakan Metode k-Means Clustering. Segmentasi Citra Paru Menggunakan Metode K-Means Clustering, 3(2), 57–65.
Azhar, R., Arifin, A. Z., Khotimah, W. N., Informatika, J. T., & Informasi, F. T. (2016). Integrasi Density-Based Clustering Dan Hmrf-Em Pada, 6, 28–37.
End, U. T. O., Global, A. N. U., Global, R. T. O. A., & The, T. (2018). United Nations High-Level Meeting on Tuberculosis Un General Assembly High-Level Meeting on Tb, (September).
Gosno, E. B., Arieshanti, I., Soelaiman, R., Pendahuluan, I., Clustering, P., & Clustering, A. A. K. (2013). Implementasi KD-Tree K-Means Clustering untuk Klasterisasi Dokumen, 2(2).
Junianto, E., & Riana, D. (2017). Penerapan PSO Untuk Seleksi Fitur Pada Klasifikasi Dokumen Berita Menggunakan NBC. Jurnal Informatika, 4(1), 38–45.
Kemenkes, R. (2011). Pedoman nasional pengendalian tuberkulosis. Jurnal Kesehatan Masyarakat, 2011. https://doi.org/614.542 Ind p
Kumaseh, M. R., Latumakulita, L., & Nainggolan, N. (2013). Segmentasi citra digital ikan menggunakan metode Tresholding. Jurnal Ilmiah Sains, 13 No.(1), 6.
Kusumanto, R. D., & Tompunu, A. N. (2011). Pengolahan Citra Digital Untuk Mendeteksi Obyek Menggunakan Pengolahan Warna Model Normalisasi RGB. Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2011, 2011(Semantik), 1–7.
Mulyati, S., Amini, S., & Juliasari, N. (2014). Perancangan Data Warehouse Untuk Pengukuran Kinerja Pengajaran Dosen, 6(1), 1–90.
Nafi’iyah, N. (2015). Algoritma Kohonen dalam Mengubah Citra Graylevel Menjadi Citra Biner. Jurnal Ilmiah Teknologi Informasi Asia, 9(2), 49–55.
Nugroho, S. Q., & Pramunendar, R. A. (2015). Pengelompokan Kayu Kelapa Menggunakan Algoritma K-Means Berdasarkan Tekstur Citra Kayu Kelapa Dua Dimensi ( 2D ), 1–5.
Nur Ridha Apriyanti, dkk. (2015). Algortima K-Means Clustering Dalam Pengolahan Citra Digital Landsat. Ilmu Komputer, 02(Clustering K-Measn), 1–13
Rahman, H. (2016). Deteksi Jalan Dari Citra Satelit Resolusi Tinggi, 7(1), 1–6.
Riana, D. (2013). Pilar Nusa Mandiri Vol . IX No . 2 September 2013 ANALISA FITUR TEKSTUR NUKLEUS DAN DETEKSI SITOPLASMA Pilar Nusa Mandiri, IX(2), 102–106.
Rulaningtyas, R., Suksmono, A. B., Mengko, T. L. R., Saptawati, G. A. P., Teknik, S., & Bandung, I. T. (2015). Segmentasi Citra Berwarna dengan Menggunakan Metode Clustering Berbasis Patch untuk Identifikasi Mycobacterium Tuberculosis.
Rusjayanthi, D. (2013). Identifikasi Biometrika Telapak Tangan Menggunakan Metode Pola Busur Terlokalisasi, Block Standar Deviasi, dan K-Means Clustering, 4(2), 265–276.
Septian, W., Riana, D., Prayogo, M. J., Pusat, J., Pusat, J., & Pusat, J. (2016). Deteksi Diameter Tumor Pada Kulit. Informatika, 3(September), 314–323.
Slogotskaya, L., Bogorodskaya, E., Ivanova, D., & Sevostyanova, T. (2018). tuberculosis children and adolescents in Moscow, 2013–2016.
Sucipto, D. B., & Riana, D. (2013). Aplikasi Diagnosa Potensi Glaukoma Melalui Citra Iris Mata Dengan Jaringan Saraf Tiruan Metode Propagasi Balik, 1(3), 19–27.
Tomasoa, L. E., Yulianto, S., Prasetyo, J., Informasi, F. T., Studi, P., Informatika, T., … Sungai, A. (2018). Analisis Index Vegetasi Pesisir Pantai Aceh Pasca Tsunami Menggunakan Citra Satelit Landsat 7 Dan Landsat 8 Dengan Metode Clustering Algoritma K-Means.
Umar, R., Riadi, I., Studi, P., Informatika, T., Dahlan, U. A., Studi, P., … Cluster, K. (2018). Sistem Identifikasi Keaslian Uang Kertas Rupiah Menggunakan Metode K-Means Clustering, 17(2), 179–185.
Widyastuti, N., & Hamzah, A. (2007). PENGGUNAAN ALGORITMA GENETIKA DALAM PENINGKATAN KINERJA FUZZY CLUSTERING UNTUK ( Application of Genetic Algorithm to Enhance the Performance of Clustering. Berkala MIPA, 17(2), 1–14.
Wijayanto, H. (2015). Klasifikasi Batik Menggunakan Metode K-Nearest Neighbour Berdasarkan Gray Level Co-Occurrence Matrices ( Glcm ). Klasifikasi Batik Menggunakan Metode K-Nearest Neighbour Berdasarkan Gray Level Co-Occurrence Matrices ( Glcm ), (5).
Wiradharma, kusuma, ananda, P., Purwanto, Y., & purboyo, waluyo, tito. (2015). Analysis Of Traffic Anomaly Detection System Using Isodata Clustering Algorithm (Self-Organizing Data Analysis Technique) With Euclidean Distance Putu, 2(2), 1542–1549.
Yunus, M. (2012). Perbandingan Metode-metode Edge Detection untuk Proses Segmentasi Citra Digital. Jurnal Teknologi Informasi, 3(2), 146–160.
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.
- The copyright in each article belongs to the author.
- Authors retain all their rights to published works, not limited to the rights set out on this page.
- The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
- Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
- The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
- If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.