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Abstract
ABSTRACT
Iridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512.
Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).
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References
- REFERENSI
- Trihono, “Riset Kesehatan Dasar”, Badan Penelitian dan Pengembangan Kesehatan Kementerian Kesehatan RI, Jakarta, 2013.
- Y-W Lim, S-I Park, Y-J Park, Y-B Park, “A Review of Iridology”, The Journal of The Society of Korean Medicine Diagnostics, Volume 17, No.1, halaman 1-16, 2013.
- R. Passarella, Erwin, M. Fachrurrozi, Sutarno, “Development of Iridology Basis data for Colon Disorder Identufication Using Image Processing”, Indian Journal of Bioinformatics and Biotechnology, Vol 2(6), halaman 100-103, 2013.
- B. Setiawan, “Identifikasi Iris Mata Menggunakan Metode Hidden Markov Model”, Skripsi, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Indonesia, Depok, 2009.
- Sherif E. Hussein, Osama A. Hassan, Malcolm H. Granat, “Assesment of the Potential Iridology for Diagnosing Kidney Disease Using Wavelet Analysis and Neural Networks”, Biomedical Signal Processing and Control 8, halaman 534-541, Elsevier Ltd., 2013.
- Rita M. Holl, “Iridology: Another Look”, Alternative Health Practitioner, Vol. 5, No. 1, Springer Publishing Company, 1999.
- B. Jensen, “What is Iridology”, Ben Jensen Enterprise Publishers, California, 1982.
- _______, “Iridology Chart”, Maikong Industry, http://www.iriscope.org/iridology-chart/iridology-chart-2, diakses Oktober 2019.
- Basuki P. Purnomo, “Dasar-dasar Urologi”, Edisi Ketiga, Sagung Seto, Jakarta, 2014.
- R. C. Gonzalez, R.E. Woods, “Digital Image Processing Second Edition”, Prentice Hall, New Jersey, 2002.
- T. Sutoyo, E. Mulyanto, V. Suhartono, O. D. Nurhayati, “Teori Pengolahan Citra Digital”, Penerbit ANDI, Yogyakarta, 2009.
- A. P. Lestari, “Rancang Bangun Pengenalan Penyakit Darah Menggunakan Metode Hidden Markov Model”, Tesis, Universitas Indonesia, Depok, 2008.
- H. Miar-Naimi, P. Davari, “A New Fast and Efficient HMM-Bassed Face Recognition System Using a 7-State HMM Along With SVD Coefficien”, Iranian Journal of Electrical and Electronic Engineering, Vol 4, Nos. 1 & 2, halaman 46-57, 2008.
References
REFERENSI
Trihono, “Riset Kesehatan Dasar”, Badan Penelitian dan Pengembangan Kesehatan Kementerian Kesehatan RI, Jakarta, 2013.
Y-W Lim, S-I Park, Y-J Park, Y-B Park, “A Review of Iridology”, The Journal of The Society of Korean Medicine Diagnostics, Volume 17, No.1, halaman 1-16, 2013.
R. Passarella, Erwin, M. Fachrurrozi, Sutarno, “Development of Iridology Basis data for Colon Disorder Identufication Using Image Processing”, Indian Journal of Bioinformatics and Biotechnology, Vol 2(6), halaman 100-103, 2013.
B. Setiawan, “Identifikasi Iris Mata Menggunakan Metode Hidden Markov Model”, Skripsi, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Indonesia, Depok, 2009.
Sherif E. Hussein, Osama A. Hassan, Malcolm H. Granat, “Assesment of the Potential Iridology for Diagnosing Kidney Disease Using Wavelet Analysis and Neural Networks”, Biomedical Signal Processing and Control 8, halaman 534-541, Elsevier Ltd., 2013.
Rita M. Holl, “Iridology: Another Look”, Alternative Health Practitioner, Vol. 5, No. 1, Springer Publishing Company, 1999.
B. Jensen, “What is Iridology”, Ben Jensen Enterprise Publishers, California, 1982.
_______, “Iridology Chart”, Maikong Industry, http://www.iriscope.org/iridology-chart/iridology-chart-2, diakses Oktober 2019.
Basuki P. Purnomo, “Dasar-dasar Urologi”, Edisi Ketiga, Sagung Seto, Jakarta, 2014.
R. C. Gonzalez, R.E. Woods, “Digital Image Processing Second Edition”, Prentice Hall, New Jersey, 2002.
T. Sutoyo, E. Mulyanto, V. Suhartono, O. D. Nurhayati, “Teori Pengolahan Citra Digital”, Penerbit ANDI, Yogyakarta, 2009.
A. P. Lestari, “Rancang Bangun Pengenalan Penyakit Darah Menggunakan Metode Hidden Markov Model”, Tesis, Universitas Indonesia, Depok, 2008.
H. Miar-Naimi, P. Davari, “A New Fast and Efficient HMM-Bassed Face Recognition System Using a 7-State HMM Along With SVD Coefficien”, Iranian Journal of Electrical and Electronic Engineering, Vol 4, Nos. 1 & 2, halaman 46-57, 2008.