Judul: Indikator Karotenoid untuk Menentukan Masak Fisiologi Benih Cabai Rawit (Capsicum /rulescens L.) Varietas Sulawesi dan Rama
Abstrak: Penelitian ini dilaksanakan di kebun percobaan Leuwikopo, Laboratorium Ilmu dan Teknologi Benih dan Laboratorium RGCI (Research Group on Crop Improvement) Departemen .Agronomi dan Holtikultura, Fakultas Penanian. Institut Penanian Bogor. Pelaksanaan penelitian dilakukan pada bulan September 2006 - Juni 2007. Penelitian ini bertujuan untuk mengetahui kemungkinan total karotenoid benih dapat digunakan sebagai indikasi tingkat masak fisiologi pada benih cabai rawit dan hubungannya dengan tolak ukur tingkat masak fisiologi lainnya.
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Judul: Uji Daya Hasil Pendahuluan Cabai Rawit (Capsicum annuum L.) Generasi F5
Abstrak: Cabai merupakan tanaman hortikultura (sayuran) yang buahnya dimanfaatkan untuk keperluan aneka ragam pangan. Cabai biasanya digunakan untuk bumbu masakan dan berbagai bahan dasar pembuatan makanan pedas. Salah satu upaya menghasilkan varietas berdaya hasil tinggi adalah dengan menyilangkan antara tanaman unggul dengan tanaman unggul lainnya. Penelitian ini dilaksakan di Kebun Percobaan Leuwikopo, Institut Pertanian Bogor dan Laboratorium Genetika dan Pemuliaan Tanaman, Departemen Agronomi dan Hortikultura IPB. Penelitain dilaksakan mulai pada bulan Desember 2016 sampai bulan Juni 2017. Penelitian ini mengamati perbedaan fenotipe populasi antara tujuh tanaman yang diuji dengan lima tanaman pembanding. Rancangan percobaan yang digunakan dalam penelitian ini adalah Rancangan Kelompok Lengkap Teracak (RKLT) dan diuji lanjut menggunakan Dunnet. Bahan genetik yang diuji terdiri dari lima, yaitu F5145174-9-7-1, F5160291-3-1-1, F5145291-14-10-10, F5145293-19-8-3, F5160291-4-13-9, F5145291-14-9-3, F5160291-19-14-1. Varietas yang dipakai dalam penelitian ini Bara, Genie, Catas, IPB C291, dan Taring. Bahan genetik yang diuji terdapat perbedaan keragaan pada calyx margin, warna batang, dan bentuk daun. Hasil kuantitaif berpengaruh nyata terhadap sembilan parameter yang diuji. Uji lanjut berdasarkan Dunnet, genotipe F5145291-14-10-10, F5145293-19-8-3, dan F5145291-14-9-3 memiliki potensi yang lebih baik.
Keyword: fenotipe, populasi, potensi, sayuran, unggul

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