Judul: Potensi Nanopartikel Ekstrak Daun Kersen (Muntingia calabura) sebagai Imunomodulator pada Puyuh (Coturnix coturnix Japonica) yang diberi Deksametason
Abstrak: Tujuan penelitian ini mengkaji karakteristik dan potensi nanopartikel daun kersen sebagai imunomodulator pada puyuh yang mengalami stres oksidatif akibat pemberian Deksametason. Pembuatan ekstrak daun kersen menggunakan metode maserasi dan nanopartikel daun kersen menggunakan metode gelasi ionik dengan penyalut kitosan. Penelitian menggunakan Rancangan Acak Lengkap (RAL) dengan menggunakan puyuh berusia 8 minggu. Penelitian ini terdiri dari 5 kelompok dan masing-masing kelompok terdapat 4 ekor puyuh jantan. Perlakuan yang diberikan A (Kontrol imunosupresan : Deksametason 2.5 mg/Kg BB), C (Deksametason 2.5 mg/Kg BB + Stimuno 0.75 mg/Kg BB), AE (Deksametason 2.5 mg/Kg BB + Nanopartikel ekstrak daun kersen 0.15 mg/Kg BB), BE (Deksametason 2.5 mg/Kg BB + Nanopartikel ekstrak daun kersen 0.075 mg/Kg BB) dan CE (Deksametason 2.5 mg/Kg BB + Ekstrak daun kersen 0.75 mg/Kg BB). Spesifikasi nanopartikel daun kersen menggunakan uji PSA (Particle Size Analyzer). Parameter profil darah yang diamati yaitu jumlah leukosit, differensial leukosit, rasio stres, dan titer antibodi. Nanopartikel ekstrak daun kersen memiliki ukuran partikel 469.16 nm dan indeks polidispersitas sebesar 0.073. Hasil penelitian menunjukan bahwa pemberian nanopartikel ekstrak daun kersen memberikan pengaruh yang signifikan (P<0.05) terhadap peningkatan jumlah leukosit, limfosit, titer antibodi dan penurunan rasio stres pada puyuh setelah induksi dengan Deksametason. Kesimpulan dari penelitian ini nanopartikel ekstrak daun kersen memiliki potensi yang besar sebagai imunomodulator pada puyuh.
Keyword: daun kersen, imunomodulator, nanopartikel, puyuh

Judul: Potential of Suji Leaves (Dracaena angustifolia  Roxb) Nanoencapsulation as Immunomodulators in Due to Methylprednisolone Consumption
Abstrak: Puyuh merupakan unggas yang mudah stres, ditandai dengan mening kat nya kadar kortikosteron sehingga dapat menekan sistem imun. Penelitian ini bertujuan mempelajari potensi nanoenkapsulasi daun suji sebagai imunomodu lator pada puyuh yang mengalami stres oksidatif yang diberi metilprednisolon. Nanoenkapsulasi merupakan suatu teknologi penghantaran obat dengan cara membungkus ekstrak daun suji dengan kitosan dan membuat ukuran menjadi nanopartikel. Pembuatan ekstrak daun suji menggunakan metode maserasi dan nanoenkapsulasi daun suji menggunakan metode gelasi ionik. Penelitian ini menggunakan 24 ekor puyuh betina yang dibagi kedalam enam kelompok, yai tu: 1) KN, kontrol negatif; 2) KP, kontrol positif diberikan imunomodulator komersial; 3)P1, nanoenkapsulasi daun suji dosis 0.32 mg/Kg BB; 4)P2, nanoenkapsulasi daun suji dosis 0.29 mg/Kg BB; 5)P1T, nanoenkapsulasi daun suji dosis 0.32 mg/Kg BB + tween 3% ; 6)P2T, nanoenkapsulasi daun suji dosis 0.29 mg/Kg BB + tween 0.3%. Setiap kelompok diberikan metilprednisolon dosis 0.70 mg/Kg BB, sel darah merah domba (SDMD), dan vaksin aktif ND La Sota. Parameter yang diamati yaitu: jumlah leukosit, diferensial leukosit, rasio heterofil/limfosit (H/L), dan titer antibodi. Hasil penelitian menunjukkan bahwa pemberian nanoenkapsulasi daun suji memberikan pengaruh signifikan (P<0.05) terhadap peningkatan jumlah leukosit, limfosit, titer antibodi dan penurunan rasio H/L. Nanoenkapsulasi daun suji memiliki potensi yang besar sebagai imunomodulator pada puyuh, Quail is a bird that is easily stressed, characterized by increase of cor ticosterone levels so that it can suppress the immune system. The research aimed to study the potency nanoencapsulation of suji leaves as immunomodula tors in quail experiencing oxidative stress due to methylprednisolone consump tion. Nanoencapsulation is a drug delivery technology of packaging suji leaf extract with chitosan and making the size into nanoparticles. Preparation of suji leaves extract was done by using the maceration method and nanoencapsulation of suji leaves using the ionic gelation method. The research used 24 female quail divided into six groups:1) KN, negative control; 2) KP, positive control given commercial immunomodulators; 3) P1, suji leaves nanoencapsulation dose of 0.32 mg/Kg BW; 4) P2, suji leaves nanoencapsulation dose of 0.29 mg/Kg BW; 5) P1T, suji leaves nanoencapsulation dose of 0.32 mg/Kg BW + 3% tween; 6) P2T, suji leaves nanoencapsulation with dose of 0.29 mg/Kg BW+ 0.3% tween. Each group was given methylprednisolone dose of 0.70 mg/Kg BW, sheep red blood cell (SRBC), and ND La Sota Active Vaccine. Pa rameters observed were the number of leukocytes, the leukocyte differential, the heterophile/lymphocyte ratio (H/L), and the antibody titer. The results showed that the consumption of suji leaves nanoencapsulation had a significant effect (P<0.05) to increase the number of leukocytes, lymphocytes, antibody titers, and decreased H/L ratio. Suji leaves nanoencapsulation has potential as an immunomodulator in quail.
Keyword: Daun suji, Imunomodulator, Nanoenkapsulasi, Puyuh

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