Judul: Isolasi dan Identifikasi Bakteri Potensial Patogen pada Sampel Tanah Peternakan Kerbau (Sekolah Peternakan Rakyat) di Desa Tambang Ayam, Kecamatan Anyar, Kabupaten Serang
Abstrak: Banten merupakan salah satu provinsi yang memiliki jumlah populasi dan produksi ternak kerbau terbanyak di Indonesia. Peningkatan produktivitas ternak kerbau harus didukung dengan peternakan yang menunjang berbagai aktivitas ternak, terutama dari kondisi lingkungan. Penelitian ini bertujuan untuk mengembangkan sekolah peternakan rakyat melalui sektor kesehatan hewan dengan cara mengisolasi dan mengidentifikasi bakteri potensial patogen pada sampel tanah peternakan kerbau di Desa Tambang Ayam, Kecamatan Anyar, Kabupaten Serang yang dapat menimbulkan kerugian ekonomi bagi peternak rakyat. Sebanyak 11 sampel tanah diambil acak dengan metode komposit. Pengujian sampel dilakukan dengan menggunakan media API 20 E dan API 50 CHB untuk menentukan spesies bakteri. Data dianalisis secara deskriptif. Hasil penelitian menunjukkan bahwa spesies bakteri yang diperoleh terdiri dari 5 bakteri Gram negatif dan 4 bakteri Gram positif. Spesies bakteri tersebut adalah Pseudomonas fluorescens, Photobacterium damselae, Enterobacter cloacae, Pseudomonas aeruginosa, Vibrio parahaemolyticus sebagai bakteri Gram negatif. Sedangkan spesies bakteri Gram positif yang diperoleh adalah Bacillus cereus, Brevibacillus, Bacillus megaterium, dan Bacillus pumilus. Bakteri yang berhasil teridentifikasi berpotensial patogen dan dapat menimbulkan kerugian bagi ekonomi ternak adalah Bacillus cereus, Pseudomonas aeruginosa, dan Enterobacter cloacae.
Keyword: API, bakteri tanah, identifikasi, Sekolah Peternakan Rakyat

Judul: Isolasi dan Identifikasi Bakteri pada Sampel Tanah di Sekolah Peternakan Rakyat (SPR) Desa Cikolelet, Kecamatan Cinangka, Kabupaten Serang, Banten.
Abstrak: Penelitian ini bertujuan untuk mengisolasi dan mengidentifikasi bakteri Gram positif dan Gram negatif pada sampel tanah Sekolah Peternakan Rakyat (SPR) di Desa Cikolelet, Kecamatan Cinangka, Kabupaten Serang, Banten. Sebanyak 14 sampel tanah diambil menggunakan alat soil ring sample dengan teknik pengambilan sampel acak. Kegiatan isolasi dan identifikasi bakteri dilakukan dengan metode kultur, pewarnaan Gram, uji biokimia, dan uji API 20E. Bakteri Gram positif yang teridentifikasi pada sampel tanah di SPR Desa Cikolelet adalah Clostridium botulinum, Actinomyces naeslundii, Staphylococcus spp., dan Micrococcus luteus Biovar II. Adapun bakteri Gram negatif yang teridentifikasi pada sampel tanah di SPR Desa Cikolelet adalah Aeromonas hydrophila subsp. liquefaciens, Stenotrophomonas maltophilia, Enterobacter cloacae, Ochrobactrum anthropi, dan Pseudomonas aeruginosa. Bakteri Clostridium botulinum dan Enterobacter cloacae merupakan bakteri yang bersifat patogen pada kerbau, sedangkan bakteri Actinomyces naeslundii, Staphylococcus spp., Micrococcus luteus Biovar II, Aeromonas hydrophila subsp. liquefaciens, Stenotrophomonas maltophilia, Ochrobactrum anthropi, dan Pseudomonas aeruginosa merupakan bakteri patogen oportunistik yang dapat menyebabkan penyakit pada ternak ketika kondisi kekebalan menurun.
Keyword: API 20E, Gram positif bakteri, Gram negatif bakteri, identifikasi, isolasi, kerbau, Sekolah Peternakan Rakyat, tanah

Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm