Judul: Perbandingan Kinerja Pembobotan Ciri pada Temu Kembali Citra Menggunakan Bayesian Network dan Algoritme Genetika
Abstrak: This research proposes performance comparison on image retrieval using bayesian network and genetic algorithm, which combines color, shape and texture information. Histogram-162 is used for color feature, edge direction histogram for shape feature and co-occurrence matrix for texture feature. Combining multiple features in image retrieval process can be implemented using feature weight assignment. Bayesian network and genetic algorithm is used to find the optimal weight. This research used 1050 images with various classes i.e car, lion, sunset, texture, bear, elephant, arrow, landscape, reptile and aircraft. Experiment results shows that genetic algorithm has better precision than bayesian network on recall between 0.0 and 0.3.
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Judul: Algoritme Genetika untuk Optimasi Pembobotan Fitur pada Temu Kembali Citra
Abstrak: Content based image retrieval is developed to retrieve image based on color, shape, and texture feature. One of the problems to be solved in content based image retrieval is how to combine these features to get the optimum retrieval result. Combining multiple features in image retrieval process can be implemented using feature weight assignment. One of feature weight assignment methods is each feature weight is assigned by hand. However this approach is subjective and lack of theory foundation. In this research an automatic feature weight assignment approach based on genetic algorithm is proposed. Genetic algorithm is used for optimizing feature weight in order to get the optimum retrieval result. Image features is used in this research are color, shape, and texture feature. In genetic algorithm, a chromosom represented by features weight. These chromosomes get into genetic operator process such as selection, crossover, and mutation until maximum generation is reached. Result of genetic algorithm process get an optimum feature weight. It used for image retrieval process. The retrieval evaluation used recall precision graphic. The experiment showed feature weight assignment based on genetic algorithm can improve average precision in image retrieval.
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Judul: Karakteristik Piezoelektrik Film LaxSr₁-xTiO3 di atas Substrat TCO
Abstrak: Studi material perovskite SrTiO3 sudah banyak dilakukan dan menunjukkan sifat feroelektrik, piezoelektrik, kuantum paraelektrik dan superkonduktor yang baik. Dengan penambahan dopant La2O3 diduga menambah performa sifat piezo dan karakteristik lainnya. Karakteristik film LaxSr₁-xTiO3 (LST) dengan variasi x= 0, 0.02, 0.04, 0.06 terbentuk pada suhu annealing 550 oC di atas substrat TCO melalui metode (Chemical Solution Deposition) CSD dan spin coating dengan 700 rpm pada kondisi suhu ruang dan tekanan atmosfer standar. Hasil menunjukkan, semakin banyak pendadah La maka nilai ketebalan film, ukuran rerata kristalit, parameter kisi, transmitansi, nilai konduktivitas (maksimum 589.7 S/cm pada sampel x=0.04 di frekuensi 10 Hz), nilai konstanta tegangan (c) dan muatan (d) piezo semakin meningkat. Dinamika bentuk grafik nilai tegangan (V) dan konstanta regangan mekanik (s) nampak mirip. Nilai V mencapai 0.127 V untuk x=0.06 (beban 50 g). Nilai maksimum c, d, s masing-masing mencapai 20.07 Vm/N, 1.14x10-8 C/N, 13642.71. Nilai band gap untuk x=0, 0.02, 0.04, 0.06 masing-masing 3.21 eV, 3.1 eV, 2.6 eV, 2.4 eV. La2O3 menambah performa sifat piezo, sehingga LST berpotensi sebagai sensor tekanan.
Keyword: CSD method, La2O3, LaxSr1-xTiO3, piezoelectric, TCO