Judul: Clustering Fragmen Metagenome Menggunakan Som Dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix (Glcm) Pada Variasi Panjang Fragmen
Abstrak: Penelitian di bidang bioinformatika berkembang semakin pesat, terutama penelitian mengenai metagenome. Namun, pada penelitian metagenome masih terdapat kelemahan, yaitu proses sequencing secara langsung akan memungkinkan bercampurnya fragmen sehingga dapat mempengaruhi perakitan fragmen. Oleh sebab itu, penelitian ini menggunakan teknik clustering untuk mengantisipasi kesalahan perakitan tersebut. Data yang digunakan dalam penelitian ini adalah data metagenome yang diunduh dari situs National Center for Biotechnology Information (NCBI) sebanyak 100 organisme dari 10 genus. Terdapat 4 variasi panjang fragmen yang digunakan, yaitu 200 bp, 1 Kbp, 3 Kbp, dan 10 Kbp. Clustering pada fragmen dilakukan dengan menggunakan metode Self Organizing Maps (SOM) dan pada tahap ekstraksi fitur menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Proses clustering yang telah dilakukan mencapai akurasi 92% - 93.6%. Nilai spesifisitas berada pada rentang 85.9% - 87.4%, sedangkan nilai sensitivitas berada pada rentang 61% - 65%. Dari penelitian yang telah dilakukan, tidak terlihat adanya pengaruh panjang fragmen terhadap hasil akurasi clustering.
Keyword: clustering, fragmen, GLCM, metagenome, SOM

Judul: Klasifikasi Fragmen Metagenome Menggunakan KNN dan PNN dengan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) pada Variasi Panjang Fragmen
Abstrak: Bioinformatics is a field of study which is developing rapidly in Indonesia. The main focus of this study is to classify metagenome fragment into some taxonomies using GLCM that has 13 features. The training data used in the classification process are 50 organisms with 5-fold cross validation. The DNA combination sequences inside a fragment can be seen as a 1xN image, where N is the fragment’s length in two-dimension’s matrix. The result from those features will be classified using PNN and KNN. The research shows that the accuracy percentage with all lengths variety, including 200 bp, 1 Kbp, 3 Kbp, and 10 Kbp, are 100%. It can be stated that the variety fragment’s length does not affect the accuracy. In addition, it can be concluded that GLCM feature extraction method can be prospectively implemented for classifying metagenome fragment.
Keyword: Bogor Agricultural University (IPB), PNN, KNN, GLCM, Fragment

Judul: Manajemen Pemeliharaan Kuda (Equus caballus) Untuk Upacara Kenegaraan Dan Sarana Kesenjataan Di Detasemen Kavaleri Berkuda (DenKavkud) TNI-AD Parongpong
Abstrak: Cavalry Horse Detachement (Denkavkud) Indonesian National Army-Cavalary Parongpong is one of the Indonesian military units that utilize the horse and train it as a means of purpose of formal state ceremony and armed forces. Skills possessed by the horse cavalry was the result of care management and specialized training provided by the trainer. Aspects of care management are breeding, feeding, and management. Horses that are kept in the Denkavkud, Parongpong are stallions, mares, foals, and military horses. Total populations of horses at Denkavkud are 183 horses. Besides commited horse training (remonte), Denkavkud also did breeding program to produce horses that can be trained and used as a military horse. There are some issues contained in Denkavkud, such as some of the nutrients needed by the horses not being met from feed given. One of these is crude protein and lysine. Lactation mares was crude protein and lysine deficiency. The number of death in 2010 is higher than previous years. Based on the number of horses that died from January until June 2010 is 11 horses. The average mortality was caused by colic. Colic is caused by excessive eating, excessive drinking during hot, moldy food, and even by investment roundworms. Animal welfare in Denkavkud can be enhanced by improvement of feed given and improving the health of livestock through improved sanitation.
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