Judul: Pemodelan Biplot pada Klasifikasi Data Metagenom dengan Kmers sebagai Ekstraksi Ciri dan LVQ sebagai Classifier
Abstrak: The reading of the genome one organism that it had become is used for the majority of the scientists, now the scientists turn to recitation metagenom , that is the reading of a sample of the genome taken some of the neighborhood. But in reading the fragment metagenom can happen the mixture of fragments of organisms A with B organism caused the same set of overlap between the two. This can be corrected with an binning process, with the purpose to classify fragments into different taxonomy levels. This can be corrected with an binning process, with the purpose to classify fragments into different taxonomic levels. Accuracy results obtained using methods lvq ranges 78.10 % to 90.90 %.Accuracy is 90.90 %, most high namely on trial with those organisms that have long been known and not use 10000 fragments bp biplot.Accuracy results obtained without using biplot larger than the value of accuracy that uses biplot because biplot done reduction finite-dimensional ± 80 % of features.
Keyword: Bogor Agricultural University (IPB), LVQ, biplot, k-mers, metagenom

Judul: Pemodelan Biplot pada Klasifikasi Fragmen Metagenom dengan K-Mers Sebagai Ekstraksi Ciri Dan Probabilistic Neural Network Sebagai Classifier
Abstrak: Metagenomics is the study of the genetic material taken directly from the organism's source environment by performing deoxyribonucleic acid sequencing procedure. Sequencing was performed on a set of genomes that have been mixed with a variety of other organisms in the environment. This led to the identification of the organism is required to prevent fragment assembly error between organisms. This study objectives to classify metagenome fragments at the genus level with Probabilistic Neural Network algorithms and k-mers as the features extraction. Biplot modelling is used to reduce features of the k-mers results that have dimensions large enough. This study uses 3 genuses of microorganisms which are divided into 2 dataset groups, namely known organisms and unknown organisms. The length of fragments used for each dataset is 500 bp, 1 kbp, 5 kbp, and 10 kbp. From the results of this study, the best accuracy for the known organisms was 98.10% and 92.80% for unknown organisms. Biplot modelling managed to reduce 68.75% fragment features with classification efficiency of 12%.
Keyword: Bogor Agricultural University (IPB), PNN, biplot, k-mers, DNA, metagenome

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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