Judul: Karakterisasi aksesi Meniran (Phyllanthus sp.) koleksi Pusat Studi Biofarmaka IPB
Abstrak: Meniran (Phyllanthus niruri L. dan Phyllanthus urinaria L.) dikenal sebagai gulma dengan daerah okupasi yang luas. Meniran mengandung bahan-bahan aktif yang memiliki khasiat pengobatan diantaranya sebagai antibakteri, hepatoprotektif, dan antidiuretik. Penelitian ini dilakukan untuk mengarakterisasi 14 aksesi meniran hasil eksplorasi yang telah dikonservasi di kebun Pusat Studi Biofarmaka IPB. Karakterisasi dilakukan berdasarkan karakter morfologi, agronomi, dan anatomi. Pelaksanaan penelitian dimulai dari penyemaian, pindah tanam (5 MST), pemeliharaan, dan pengamatan (sejak pindah tanam). Data kuantitatif hasil karakterisasi diuji dengan nilai t-student, selanjutnya data kuantitatif dan kualitatif diuji dengan analisis gerombol untuk memperoleh informasi tingkat kemiripan antar-aksesi dan jumlah gerombol yang dibentuk. Hasil uji-t menunjukkan bahwa faktor aksesi memiliki pengaruh nyata terhadap seluruh karakter agronomi dan anatomi yang diamati. Dendogram hasil analisis gerombol menunjukkan pada tingkat kemiripan 61%, aksesi-aksesi meniran terbagi menjadi 4 gerombol. Grup A terdiri dari aksesi Sawahan 1, Kandang Jati 3, Sumedang 3, dan Balittro. Grup B terdiri dari aksesi Sumedang 4, Bontang, Jember, Cikabayan 1, dan PSB 1. Grup C terdiri dari aksesi Madiun, Pati, Ciampea 2, dan Sawahan 2. Grup D hanya diisi oleh aksesi PSB 2.
Keyword: accession, dendogram, characterizationconservation, Phyllanthus sp

Judul: Karakterisasi Aksesi Meniran (Phyllanthus sp. L.) Koleksi Pusat Studi Biofarmaka IPB.
Abstrak: Ketersediaan bibit unggul menjadi salah satu kendala dalam budidaya tanaman obat meniran, sehingga perlu dilakukan tahap pemuliaan tanaman. Penelitian ini bertujuan untuk mempelajari keragaan beberapa aksesi meniran serta hubungan antara karakter agronomi dengan produksi biomassa meniran Percobaan dilakukan di Kebun Pusat Studi Biofarmaka IPB, Bogor, pada bulan Februari-Juni 2016. Evaluasi karakter melibatkan 19 aksesi meniran koleksi Pusat Studi Biofarmaka (PSB) IPB. Hasil analisis komponen utama terhadap 19 aksesi meniran, berdasarkan 18 peubah pengamatan memperlihatkan bahwa keragaman sebesar 87,9% dapat dijelaskan oleh lima komponen utama. Berdasarkan analisis komponen utama diperoleh diagram pencar (komponen utama I-II) yang menunjukkan terdapat tiga gerombol. Berdasarkan hasil analisis gerombol, semua aksesi membentuk tiga gerombol pada koefisien kemiripan 42,47%. Hasil uji nilai tengah dari karakter agronomi dan anatomi kelompok aksesi meniran hijau dan merah tidak berbeda nyata. Biomassa tanaman meniran berkorelasi positif dengan karakter tinggi tanaman, diameter batang, jumlah cabang, dan bobot basah tanaman meniran. Hasil analisis HPLC, kandungan filantin terdeteksi pada aksesi Bontang (2,619 mg g-1) dan Pati (0,001 mg g-1), akan tetapi tidak terdeteksi pada aksesi Sumedang 2, Sumedang 4 dan Ciampea 1. Kandungan hipofilantin terdeteksi pada aksesi Bontang (6,018 mg g-1), Sumedang 2(0,115 mg g-1), Ciampea 1 (0,013 mg g-1), Pati (0,006 mg g-1), akan tetapi tidak terdeteksi pada aksesi Sumedang 4.
Keyword: antosianin, biomassa, korelasi, analisis komponen utama

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