Optimasi decision tree menggunakan particle swarm optimization untuk identifikasi penyakit mata berdasarkan analisis tekstur

Optimizing decision tree using particle swarm optimization to identify eye diseases based on texture analysis

*Toni Arifin scopus  -  Department of Informatics, Universitas BSI Bandung, Indonesia
Asti Herliana  -  Department of Informatics, Universitas BSI Bandung, Indonesia
Received: 10 Aug 2019; Revised: 8 Nov 2019; Accepted: 18 Nov 2019; Published: 31 Jan 2020.
DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.59-63 View
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Section: Original Research Articles
Language: ID
In Volume 8, Issue 1, Year 2020 (January 2020)
Statistics: 577 137
Abstract
The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.

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Keywords: optimization; classification; eye diseases, decision tree; particle swarm optimization; GLCM

Article Metrics:

  1. WHO, “Priority eye diseases,” 2019. [Online]. Available: https://www.who.int/blindness/causes/ priority/en/index1.html. [Accessed: 11 Aug 2019].
  2. S. Siswoyo, L. A. Susuma, and S. Rahayu, “Hubungan tingkat pengetahuan dengan upaya pencegahan penyakit glaukoma pada klien berisiko di wilayah kerja puskesmas Jenggawah kabupaten Jember,” Pustaka Kesehatan, vol. 6, no. 2, pp. 286-291, 2018.
  3. American Academy of Ophthalmology, “Eye disease,” 2019. [Online]. Available: http://www.aao-igh.com/. [Accessed: 11 Aug 2019].
  4. I. W. B. Sentana and A. E. Wardani, “Pengolahan citra untuk klasifikasi penyakit katarak,” Semnasteknomedia Online, vol. 3, no. 1, pp. 6–8, 2015.
  5. E. P. Purwandari, R. U. Hasibuan, and D. Andreswari, "Identifikasi jenis bambu berdasarkan tekstur daun dengan metode gray level co-occurrence matrix dan gray level run length matrix," Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 4, pp. 146-151, 2018. doi: 10.14710/jtsiskom.6.4.2018.146-151
  6. A. Kadir and A. Susanto, Teori dan aplikasi pengolahan citra digital. Yogyakarta: Andi, 2013.
  7. S. Anwar and U. Sudibyo, “Implementasi metode jaringan syaraf tiruan backpropagation untuk prediksi penyakit glaukoma di kota Pati,” Skripsi, Universitas Dian Nuswantoro, Indonesia, 2017.
  8. D. S. Tobias and A. R. Widiarti, “Deteksi glaukoma pada citra fundus retina dengan metode k-nearest neighbor,” in Seminar Nasional Ilmu Komputer (SNIK 2016), Semarang, Indonesia, Oct. 2016, pp. 92–99.
  9. R. Z. I. Ramli, R. D. Atmaja, and I. Widjayanto, “Deteksi dan klasifikasi stadium katarak senilis berdasarkan citra mata menggunakan metode support vector machine (SVM),” e-Proceeding of Engineering, vol. 5, no. 2, pp. 2023–2030, 2018.
  10. A. Halim, H. Hardy, and M. Mytosin, “Aplikasi image retrieval dengan histogram warna dan multi-scale GLCM,” Jurnal SIFO Mikroskil, vol. 16, no. 1, pp. 41-51, 2015.
  11. S. R. Listyanto, “Implementasi k-nearest neighbor untuk mengenali pola citra dalam mendeteksi penyakit kulit,” Skripsi, Universitas Dian Nuswantoro, Indonesia, 2018.
  12. S. Yastika, “Identifikasi penyakit anemia defisiensi besi berdasarkan kelainan sel darah merah menggunakan metode probabilistic neural network,” Skripsi, Universitas Dian Nuswantoro, Indonesia, 2018.
  13. N. Saragih, “Identifikasi penyakit hepatocellular carcinoma (HCC) pada citra ct-scan menggunakan probabilistic neural network,” Skripsi, Universitas Sumatera Utara, Indonesia, 2018.
  14. T. Arifin, D. Riana, and G. I. Hapsari, “Klasifikasi statistikal tekstur sel pap smear dengan decision tree,” Jurnal Informatika, vol. 1, no. 1, pp. 1-7, 2014. doi: 10.31311/ji.v1i1.180
  15. S. Supangat, A. R. Amna, and T. Rahmawati, “Implementasi decision tree C4.5 untuk menentukan status berat badan dan kebutuhan energi pada anak usia 7-12 tahun,” Teknika, vol. 7, no. 2, pp. 5-10, 2018. doi: 10.34148/teknika.v7i2.90
  16. T. Hidayatulloh, A. Herliana, and T. Arifin, “Klasifikasi Sel tunggal pap smear berdasarkan analisis fitur berbasis Naive Bayes classifier dan particle swarm optimization,” Swabumi, vol. 4, no. 2, pp. I86-193, 2016.
  17. S. A. Zega, “Penggunaan pohon keputusan untuk klasifikasi tingkat kualitas mahasiwa berdasarkan jalur masuk kuliah,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), Yogyakarta, Indonesia, Jun. 2014, pp. 7-13.
  18. A. Hermawan, A. R. Sukma, and R. Halfis, “Analisis algoritma klasifikasi C4.5 untuk memprediksi keberhasilan immunotherapy pada penyakit kutil,” Jurnal Teknik Komputer, vol. 5, no. 2, pp. 155-160, 2019. doi: 10.31294/jtk.v5i2.4851
  19. N. Yahya and A. Jananto, “Komparasi kinerja algoritma C4.5 dan Naïve Bayes untuk prediksi kegiatan penerimaan mahasiswa baru (studi kasus : Universitas Stikubank Semarang),” in Seminar Nasional Multi Disiplin 2019, Semarang, Indonesia, Jul. 2019, pp. 221-228.
  20. A. Herliana, T. Arifin, S. Susanti, and A. B. Hikmah, “Feature selection of diabetic retinopathy disease using particle swarm optimization and neural network,” in 6th International Conference on Cyber and IT Service Management, Parapat, Indonesia, Aug. 2018, pp. 1-4. doi: 10.1109/CITSM.2018.8674295
  21. T. Arifin and A. Herliana, “Optimasi metode klasifikasi dengan menggunakan particle swarm optimization untuk identifikasi penyakit diabetes retinopathy,” Khazanah Informatika, vol. 4, no. 2, pp. 77-81, 2018. doi: 10.23917/khif.v4i2.6825
  22. Center For Biometrics and Security Research, “CASIA iris image databases,” 2005. [Online]. Available: http://www.cbsr.ia.ac.cn/english/IrisDa tabase.asp. [Accessed: 10-Jan-2019].
  23. WebMD, “Eye health center,” 2019. [Online]. Available: https://www.webmd.com/eye-health/.
  24. R. C. Gonzalez, S. L. Eddins, and R. E. Woods, Digital image processing using Matlab. McGraw Hill Education, 2016.
  25. R. Scherer, Computer vision methods for fast image classification and retrieval (studies in computational intelligence book 821). Springer, 2019.
  26. T. Arifin, “Metode data mining untuk klasifikasi data sel nukleus dan sel radang berdasarkan analisa tekstur,” Jurnal Informatika, vol. 2, no. 2, pp. 425-433, 2015.
  27. T. Arifin, “Klasifikasi inti sel pap smear berdasarkan analisis tekstur menggunakan correlation-based feature selection berbasis algoritma C4.5,” Jurnal Informatika, vol. 1, no. 2, pp. 123-129, 2014.
  28. Y. Zhang, S. Wang, P. Phillips, and G. Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection,” Knowledge-Based System, vol. 64, pp. 22-31, 2014. doi: 10.1016/j.knosys.2014.03.015
  29. M. Hall, I. H. Witten, E. Frank, and C. J. Pal, Data mining practical machine learning tools and techniques. Morgan aufmann, 2016.

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