IDENTIFIKASI CACAT KAYU SWIETENIA MAHAGONI MENGGUNAKAN EUCLIDEAN DISTANCE DENGAN PERBANDINGAN SEGMENTASI K-MEANS DAN THRESHOLDING

• SRI RAHAYU
• 14002296

---

ABSTRAK

ABSTRAK

 

 

Nama                            : Sri Rahayu

NIM                             : 14002296

Program Studi             : Ilmu Komputer

Jenjang                         : Strata Dua (S2)

Konsentrasi                  : Image Processing

Judul Tesis

:

Identifikasi Cacat Kayu Swietenia Mahagoni Menggunakan Euclidean Distance Dengan Perbandingan Segmentasi K-Means Dan Thresholding

 

Penghasilan terbesar dari negara-negara Asia Tenggara berasal dari kegiatan ekspor produksi kayu. Potensi ekspor kayu di Indonesia setiap tahunnya terus meningkat. Potensi yang melejit ini perlu terus ditingkatkan dengan menjaga kualitas agar kepercayaan dan kerjasama yang baik terus terjalin. Kualitas kayu berkaitan erat dengan cacat kayu, semakin cepat deteksi cacat kayu semakin cepat pula menentukan kualitas kayu. Di industri kayu yang masih manual juga rentan sekali terhadap kelelahan mata  manusia. Teknologi saat ini berkembang pesat untuk membantu kegiatan produktif manusia, image processing menjadi terobosan untuk dapat mendeteksi cacat kayu. Penelitian ini bertujuan untuk mendeteksi cacat kayu Swietenia Mahagoni dengan menggunakan metode eulidean distance dari hasil ekstraksi 6 fitur GLCM diantaranya metric, eccentricity, contras, correlation, energy, dan homogeneity yang sebelumnya dilakukan segmentasi dengan segmentasi terbaik hasil perbandingan segmentasi thresholding dan k-means dan berhasil mendapatkan rata-rata akurasi sebesar 95,33%. Dataset yang digunakan merupakan dataset primer dengan total 54 citra pada 3 jenis cacat kayu, yaitu cacat kayu kulit tumbuh pada bontos, cacat mata kayu busuk pada badan dan cacat mata kayu sehat pada badan.

 

Kata Kunci : Swietenia Mahagoni, Cacat Kayu, Euclidean Distance, GLCM, K-Means, Thresholding.

KATA KUNCI

Metode K-means

---

DAFTAR PUSTAKA

DAFTAR REFERENSI

 

 

[1]      M. L. Hadiwidjaja et al., “Developing Wood Identification System by Local Binary Pattern and Hough Transform Method Developing Wood Identification System by Local Binary Pattern and Hough Transform Method,” 2nd Int. Conf. Data Inf. Sci., 2019.

[2]      WITS, “Indonesia Wood Exports By Region 2018,” 2018.

[3]      BPS, “Ke Mana Tujuan Ekspor Kayu Lapis Indonesia??,” vol. 267, Jakarta, p. 2019, 31-Oct-2019.

[4]      Suhariyanto, Statistics Of Forestry Production. Indonesia: Badan Pusat Statistik, 2015.

[5]      J. G. M. da Silva, G. B. Vidaurre, D. Minini, R. F. Oliveira, S. M. G. Rocha, and  e F. G. Gonçalves, “Qualidade da madeira de mogno brasileiro plantado para a produção de serrados Wood quality of Brazilian mahogany planted for lumber production,” Qual. da madeira mogno Bras. plantado para a produção serrados Comer., pp. 1–12, 2019.

[6]      A. S. R. D. Lestari, Y. S. Hadi, D. Hermawan, and A. Santoso, “Glulam Properties of Fast-growing Species Using Mahogany Tannin Adhesive,” Peer Rev. Artic. Bioresour., vol. 10, no. 201 5, pp. 7419–7433, 2015.

[7]      R. Qayyum, K. Kamal, T. Zafar, and S. Mathavan, “Wood Defects Classification Using GLCM Based Features And PSO Trained Neural Network,” Int. Conf. Autom. Comput., pp. 3–7, 2016.

[8]      Gasim, A. Harjoko, K. B. Seminar, and S. Hartati, “Image Blocks Model for Improving Accuracy in Identification Systems of Wood Type,” Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 6, pp. 48–53, 2013.

[9]      R. N. N. Rahiddin, U. R. Hashim, N. H. Ismail, L. Salahuddin, N. H. Choon, and S. N. Zabri, “Classification of wood defect images using local binary pattern variants,” Int. J. Adv. Intell. Informatics, vol. 6, no. 1, pp. 36–45, 2020.

 

54

[10]    Z. Y. Xiang, Z. Y. Qin, L. Ying, J. L. Quan, and C. Z. Wei, “Identification of Wood Defects Based on LBP Features,” Proc. 35th Chinese Control Conf., pp. 4202–4205, 2016.

[11]    X. Yonghua and W. J. Cong, “Study on the identification of the wood surface defects based on texture features,” Opt. - Int. J. Light Electron Opt., 2015.

[12]    F. Perez-sanz, P. J. Navarro, and M. Egea-cortines, “Plant phenomics?: an overview of image acquisition technologies and image data analysis algorithms,” Gigascience, vol. 6, no. 11, pp. 1–18, 2017.

[13]    S. R. Sulistiyanti, F. A. Setyawan, and M. Komarudin, Pengolahan Citra Dasar dan Contoh Penerapannya, 1st ed. Yogyakarta: Teknosain, 2016.

[14]    N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image Segmentation using K -means Clustering Algorithm and Subtractive Clustering Algorithm,” Procedia - Procedia Comput. Sci., vol. 54, pp. 764–771, 2015.

[15]    F. G. Febrinanto, C. Dewi, and A. T. Wiratno, “Implementasi Algoritme K-Means Sebagai Metode Segmentasi Citra Dalam Identifikasi Penyakit Daun Jeruk,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5375–5383, 2018.

[16]    A. Zarkasi and H. Ubaya, “VISION SEBAGAI PENGOLAHAN CITRA API,” Konf. Nas. Teknol. Inf. Apl., vol. 4, pp. 39–44, 2016.

[17]    MB Herlambang, “Training dan test set,” www.megabagus.id, 2018. [Online]. Available: https://www.megabagus.id/training-set-test-set/. [Accessed: 24-Jun-2020].

[18]    E. R. Swedia and M. Cahyanti, ALGORITMA TRANSFORMASI RUANG WARNA. Depok, 2010.

[19]    G. P and V.Rajini, “YIQ Color Space based Satellite Image Segmentation using Modified FCM Clustering and Histogram Equalization,” Int. Conf. Adv. Electr. Eng., 2014.

[20]    R. Rulaningtyas, A. B. Suksmono, T. Mengko, and P. Saptawati, “Multi Patch Approach in K-Means Clustering Method for Color Image Segmentation in Pulmonary Tuberculosis Identification,” Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng., pp. 75–78, 2015.

[21]    K. Xiao et al., “Characterising the variations in ethnic skin colours: a new calibrated data base for human skin,” Ski. Res. Technol., no. 5, pp. 21–29, 2017.

[22]    A. Padmo and Murinto, “SEGMENTASI CITRA BATIK BERDASARKAN FITUR TEKSTUR MENGGUNAKAN METODE FILTER GABOR DAN K -MEANS,” J. Inform., vol. 10, no. 1, pp. 1173–1179, 2016.

[23]    M. Chandrakala and P. D. Devi, “Threshold Based Segmentation Using Block,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 4, no. 1, pp. 821–826, 2016.

[24]    H. Wang, J. F. Wellmann, Z. Li, X. Wang, and R. Y. Liang, “A Segmentation Approach for Stochastic Geological Modeling Using Hidden Markov Random Fields,” Int. Assoc. Math. Geosci. 2016, 2016.

[25]    X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu, and J. Paisley, “Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network,” IEEE Trans. Image Process., vol. 27, no. 5, pp. 1–14, 2018.

[26]    J. Li, J. M. Bioucas-dias, and A. Plaza, “Spectral – Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields,” EEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 809–823, 2015.

[27]    R. D. Atmaja, M. A. Murti, J. Halomoan, and F. Y. Suratman, “An Image Processing Method to Convert RGB Image into Binary,” Indones. J. Electr. Eng. Comput. Sci., vol. 3, no. 2, pp. 377–382, 2016.

[28]    M. B. A. Miah and M. A. Yousuf, “Detection of Lung Cancer from CT Image Using Image Processing and Neural Network,” Electr. Eng. Inf. Commun. Technol., no. May, pp. 21–23, 2015.

[29]    G. A. Ruz and P. A. Estévez, “Image segmentation using fuzzy min-max neural networks for wood defect detection,” Intell. Prod. Mach. Syst., no. C, 2015.

[30]    O. N. Al Sayaydeh, M. F. Mohammed, and C. P. Lim, “Survey of Fuzzy Min Max Neural Network for Pattern Classification Variants and Applications,” IEEE Trans. Fuzzy Syst., vol. PP, no. c, pp. 1–12, 2018.

[31]    M. Jaroš, P. Strakoš, T. Karásek, L. Ríha, M. Jarošová, and T. Kozubek, “Implementation of K-means segmentation algorithm on Intel Xeon Phi and GPU?: Application in medical imaging,” Adv. Eng. Softw., vol. 0, pp. 1–8, 2016.

[32]    A. Premana, R. M. H. Bhakti, and D. Prayogi, “SEGEMENTASI K-MEANS CLUSTERING PADA CITRA MENGGUNAKAN EKSTRAKSI FITUR WARNA DAN TEKSTUR,” Inf. Technol. J. UMUS, vol. 2, no. 1, pp. 89–97, 2020.

[33]    E. Maria, Yulianto, Y. P. Arinda, Jumiaty, and P. Nobel, “Segmentasi Citra Digital Bentuk Daun Pada Tanaman Di Politani Samarinda Menggunakan Metode Thresholding,” JURTI, vol. 2, no. 1, pp. 37–46, 2018.

[34]    A. Pamungkas, “Pemrograman Matlab Pengolahan Citra Digital , Pengolahan Video , Pengenalan Pola , dan Data Mining Thresholding,” pemrogramanmatlab.com, 2017. .

[35]    V. Wasule, “Classification of Brain MRI Using SVM and KNN Classifier,” Int. Conf. Sensing, Signal Process. Secur. Classif., pp. 218–223, 2017.

[36]    D. Hardiyanto and D. A. Sartika, “Identifikasi Konten Negatif pada Citra Digital Berbasis Tanda Vital Tubuh Menggunakan Ekstraksi Fitur GLCM dan Warna YCbCr,” Sist. Kendali-Tenaga-Elektronika-Telekomunikasi-Komputer, vol. 6, no. 1, pp. 120–131, 2017.

[37]    O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes Classification Using K-NN Based on GLCM and HSV Color Space,” Int. Conf. Innov. Creat. Inf. Technol., 2017.

[38]    E. W. Hidayat and A. Rahmatulloh, “Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants Identification of Bacterial Leaf Blight and Brown Spot Disease in Rice Plants with Image Processing Approach,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 2, pp. 59–67, 2020.

[39]    F. U. Karimah, Ernawati, and D. Andreswari, “RANCANG BANGUN APLIKASI PENCARIAN CITRA BATIK BESUREK BERBASIS TEKSTUR DENGAN METODE GRAY LEVEL CO- OCCURRENCE MATRIX DAN EUCLIDEAN DISTANCE,” J. Teknol. Inf., vol. 11, no. April, pp. 64–77, 2015.

[40]    N. H. Barnouti and W. E. Matti, “Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 5, pp. 371–377, 2016.

[41]    E. Utami, “Penggunaan Principal Component Analysis dan Euclidean Distance untuk Identifikasi Citra Tanda Tangan Use of Principal Component Analysis and Euclidean Distance to Identify Signature Image,” IPTEK-KOM, vol. 16, no. 1, pp. 1–16, 2016.

[42]    Saifudin and A. Fadlil, “SISTEM IDENTIFIKASI CITRA KAYU BERDASARKAN TEKSTUR MENGGUNAKAN GRAY LEVEL COOCURRENCE MATRIX ( GLCM ),” SINERGI, vol. 19, no. 3, pp. 181–186, 2015.

[43]    E. W. Hidayat and A. Rahmatulloh, “Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants Identification of Bacterial Leaf Blight and Brown Spot Disease in Rice Plants with Image Processing Approach,” no. February, 2020.

[44]    A. Bhardwaj and A. Tiwari, “Breast Cancer Diagnosis Using Genetically Optimized Neural Network Model,” Expert Syst. Appl., 2015.

[45]    A. Mardhotillah, A. Atfianto, and K. N. Rahmadhani, “Menghitung Jumlah Buah Cabe Berwarna Hijau Menggunakan Metode Transformasi Ruang Warna RGB,” e-Proceeding Eng., vol. 5, no. 2, pp. 3641–3648, 2018.

[46]    F. Yang and Y. Wang, “Study on Image Recognition and Classification of Wood Skin Defects Based on BOW Model,” Int. Conf. Mech. Electr. Electron. Eng. Sci. (MEEES 2018) Study, vol. 154, no. Meees, pp. 80–84, 2018.

[47]    K. Raval, R. Shukla, and A. K. Shah, “Color Image Segmentation using FCM Clustering Technique in RGB , L * a * b , HSV , YIQ Color spaces,” Eur. J. Adv. Eng. Technol., vol. 4, no. 3, pp. 194–200, 2017.

[48]    A. Khekade and K. Bhoyar, “Shadow detection based on RGB and YIQ Color models in Color aerial images,” Int. Conf. Futur. trend Comput. Anal. Knowl. Manag., no. Ablaze, pp. 144–147, 2015.

[49]    M. S. R. Naidu, P. R. Kumar, and K. Chiranjeevi, “Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation,” Alexandria Eng. J., 2017.

[50]    S. N and V. S, “I MAGE S EGMENTATION B Y U SING T HRESHOLDING,” Comput. Sci. Eng. An Int. J., vol. 6, no. 1, pp. 1–13, 2016.

[51]    F. T. Anggraeny, M. S. Munir, and U. W. Atmojo, “SEGMENTASI K-MEANS CLUSTERING PADA CITRA WARNA DAUN TUNGGAL MENGGUNAKAN MODEL WARNA L * a * b,” SCAN-Jurnal Teknol. Inf. dan Komun., vol. XIV, no. 2, pp. 38–44, 2019.

[52]    J. Kusanti and N. A. Haris, “Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut,” J. Pengemb. IT, vol. 03, no. 01, pp. 1–6, 2018.