Judul: Klasifikasi Citra Menggunakan Metode Minor Component Analysis pada Sistem Temu Kembali Citra
Abstrak: In content-based image retrieval (CBIR) system, retrieving process is done by comparing a query image to all images from image database. This process is not effective because spends much times besides the retrieved images are not always match with the query image particularly for large databases. To solve this problem, image classification is proposed. In this research, minor component analysis (MCA) is used for images classification. For each image, a feature vector describing color, shape and texture. MCA vector will be formed as a representative pattern for each images class. In classification process, image database divided for image training and testing. Train data used to build classification model while test data used for test the accuracy of classification model. From this research it can be concluded that usage of MCA can improve the accuracy about 33.20%. This improvement shows that MCA can be applied in CBIR system, especially for large images database.
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Judul: Optimisasi Query Citra dengan Relevance Feedback dan Support Vector Machine
Abstrak: Temu kembali citra dilakukan berdasarkan isi citra (CBIR). masing-masing citra memiliki warna, bentuk dan tekstur yang beragam sehingga perlu digunakan teknik klasifikasi agar proses pencarian dapat bekerja optimal. Relevance Feedback (RF) digunakan untuk lebih memaksimalkan hasil temu kembali citra. Penelitian ini menitikberatkan pada temu kembali citra berdasarkan ciri warna. Citra query disegmentasi menggunakan Expectation Maximization kemudian diekstraksi dengan Fuzzy Color Histogram (FCH). Kelas dari citra query diperoleh dari model klasifikasi yang dibentuk menggunakan Support Vector Machine (SVM). RF akan melakukan update pada FCH query sesuai masukan dari pengguna. Penelitian ini mengukur peningkatan yang terjadi pada temu kembali citra dengan SVM dan RF dibandingkan dengan SVM tanpa RF. Evaluasi hasil temu kembali menggunakan rataan precision untuk tiap tingkatan recall. Berdasarkan penelitian ini, hasil temu kembali citra menggunakan SVM tanpa RF memiliki rataan precision 0.74, sedangkan hasil temu kembali ctra menggunakan SVM dan RF memiliki rataan precision 0.82.
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Judul: Studi Seroprevalensi Newcastle Disease pada Unggas Peliharaan Masyarakat di Kecamatan Lore Utara Kabupaten Poso.
Abstrak: This research aims to determine the seroprevalence of Newcastle Disease (ND) on native birds in North Lore Poso District. The research used serum samples from unvaccinated native birds. A total of 45 samples; 22 from domestic chicken and 23 from Muscovy ducks; were taken from Sedoa, Wuasa, and Napu village. Newcastle Disease was determined by Enzyme-linked Immunosorbent Assay (ELISA) method. The seroprevalence of ND in domestic chicken and muscovy ducks were 40.9% and 69.56%, respectively. It was equal with 9 domestic chicken and 13 muscovy ducks that were positive antibody ND. Based on the results, we can conclude that ND virus was present on those three village in North Lore. The area was determined as ND-endemic.
Keyword: ELISA, Newcastle Disease, Poso, Seroprevalence