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Journal : Proceedings of KNASTIK

SEGMENTASI CITRA MENGGUNAKAN CLUSTERING DENGAN PENDETEKSI SADDLE POINT Saikhu, Ahmad; Soelaiman, Rully; Hambali, Imam
Proceedings of KNASTIK 2009
Publisher : Duta Wacana Christian University

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Abstract

Segmentasi citra adalah proses membagi citra ke dalam region-region yang terpisah, di mana setiap region adalahhomogen dan mengacu pada sebuah kriteria keseragaman yang jelas. Segmentasi yang dilakukan terhadap citra harus tepatagar informasi yang terkandung di dalamnya dapat diterjemahkan dengan baik.Penelitian ini menggunakan algoritma meanshift untuk segmentasi. Mean shift merupakan prosedur nonparametric sederhana untuk mengestimasi kerapatan gradient.Metode ini mempunyai parameter untuk mengontrol resolusi hasil segmentasi. Untuk mendeteksi boundary klasterdigunakan algoritma saddle point.Berdasarkan hasil uji coba menunjukkan waktu proses dan jumlah klaster dalamsegmentasi tergantung pada nilai parameter bandwidth domain spasial hs, bandwidth domain range hr, dan klaster terkecil Myang dimasukkan. Semakin besar nilai hs maka waktu segmentasi semakin lama. Semakin besar nilai hs,hr, dan M makajumlah klaster semakin sedikit. Boundary yang dihasilkan saddle point menjadi lebih halus.
DISCRIMINANT ANALYSIS IMPLEMENTATION BASED ON VARIABLE PREDICTIVE MODELS FOR SIMILARITY PATTERN CLASSIFICATION Saikhu, Ahmad; Eka Putra, Deneng
Proceedings of KNASTIK 2012
Publisher : Duta Wacana Christian University

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Abstract

At present, there are many pattern classification methods that can be used such asLDA, kNN, Bayesian networks, CART, ANN and SVM. However, many classificationmethods mentioned above causes some issues. The problem are the large computationalcost, and weakness of methods mentioned above to classify the class because it is basedsolely on inter-class boundary (decision boundaries).As an alternative method otherthan the methods have already been exist, the relationship between features (interrelation)in a class can be used to classify a sample of a particular class. Based onthese ideas variable predictive model method based class discrimination (VPMCD) isproposed by (Raghuraj &Lakshminarayanan, 2008) as a new classification approach tothe problem of large data and overlapping which cannot be easily solved by the otherclassification methods.The testings are done using six well studied data sets (Diabetic,Hear, Iris, Wine, Digit, Letter). The results are equations wihich have capability toclasify new sample.