Identifikasi Cacat Kayu Swietenia Mahagoni Menggunakan Euclidean Distance dengan Perbandingan Segmentasi K-Means dan Thresholding

Identifikasi Cacat Kayu Swietenia Mahagoni Menggunakan Euclidean Distance dengan Perbandingan Segmentasi K-Means dan Thresholding

• By Rahayu Sri;
• Tesis
• 509

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.

• Katakunci Swietenia Mahagoni, Cacat Kayu, Euclidean Distance, GLCM, KMeans, Thresholding.
• URL http://
• Penerbit ,

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