Judul: Karakteristik Morfologi Usus Ayam Cemani (Gallus gallus domesticus)
Abstrak: Ayam cemani (Gallus gallus domesticus) menjadi ayam lokal yang memiliki nilai jual tinggi karena keunikan serta kepercayaan magisnya oleh masyarakat. Penelitian ini bertujuan mengetahui karakteristik struktur makroskopis dan mikroskopis usus ayam cemani. Sejumlah tiga ekor ayam cemani betina diambil ususnya kemudian dilakukan pewarnaan HE, AB pH 2,5, dan PAS. Hasil penelitian menunjukkan bahwa panjang usus ayam cemani adalah 96,73 ± 6,82 cm dan secara makroskopis usus halus ayam cemani tidak menyeluruh berwarna hitam sedangkan pada usus besar seluruhnya berwarna hitam. Mikroanatomi usus ayam cemani tidak berbeda jauh dengan ayam pada umumnya. Pigmen melanin terdistribusi di lamina propria dari mukosa usus besar, tunika submukosa usus besar, tunika mukularis ileum dan usus besar, serta di seluruh serosa usus. Selain itu, pigmen melanin juga ditemukan di sekitar pembuluh darah dan bursa Fabricius. Pewarnaan karbohidrat asam dan netral menunjukkan hasil positif pada sel goblet di lapisan epitel mukosa dan sel kelenjar Lieberkuhn.
Keyword: cemani chicken, intestine, melanin pigment

Judul: Karakteristik Morfologi Lidah dan Esofagus Ayam Cemani (Gallus gallus domesticus)
Abstrak: Ayam cemani merupakan salah satu ayam lokal yang mempunyai karakteristik seluruh tubuhnya berwarna hitam. Informasi mengenai sistem pencernaan ayam cemani masih sangat terbatas. Penelitian ini bertujuan untuk mempelajari morfologi lidah dan esofagus secara makroskopis dan mikroskopis. Penelitian ini menggunakan tiga lidah dan esofagus dari ayam cemani betina berusia tiga bulan. Penelitian makroskopis meliputi pengamatan morfologi dan pengukuran sampel, sedangkan penelitian mikroskopis dilakukan untuk mengamati struktur umum serta identifikasi distribusi karbohidrat kelenjar. Lidah berwarna dominan hitam dan esofagus berwarna cokelat kehitaman dengan berat rata-rata sebesar 2,0 ± 0,0 g dan 7,0 ± 1,6 g. Lidah berbentuk segitiga yang meruncing ke arah rostral. Esofagus terdiri atas esofagus servikalis dan esofagus torakalis. Kelenjar lingualis ditemukan tipe mukus. Esofagus servikalis memiliki epitel mukosa lebih tebal dan kelenjar lebih banyak dibanding esofagus torakalis. Kelenjar esofagus tidak ditemukan pada tembolok. Pigmen melanin banyak ditemukan di lamina propria organ. Selain itu, pigmen melanin juga ditemukan di lapisan muskularis organ dan tunika adventisia. Jaringan limfatik ditemukan di kelenjar esofagus dan tonsil esofageal di peralihan esofagus torakalis dan proventrikulus. Hasil pewarnaan karbohidrat positif pada kelenjar lingualis dan kelenjar esofagus serta hasil negatif pada tembolok.
Keyword: cemani chicken, esophagus, melanin pigment, tongue

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