Judul: Analisis Risiko Usaha Sayuran Organik di FAM Organic Tenjolaya Bogor dengan Pendekatan Enterprise Risk Management
Abstrak: Meningkatnya tren gaya hidup sehat pada masyarakat dunia berdampak pada perkembangan produksi dan perdagangan produk organik di pasar internasional dalam dekade terakhir. Meskipun demikian, pertanian organik yang bersifat labor intensive memiliki kemungkinan risiko yang lebih tinggi dari pertanian konvensional. Risiko tersebut dihadapi pula oleh FAM Organic Tenjolaya selaku usaha budidaya sayuran organik di Indonesia. Tujuan dari penelitian ini adalah 1) mengidentifikasi jenis risiko yang dihadapi FAM Organic Tenjolaya, 2) mengukur dan memetakan risiko usaha FAM Organic Tenjolaya, 3) merumuskan strategi mitigasi risiko yang efektif dalam mengelola risiko di FAM Organic Tenjolaya. Metode yang digunakan pada penelitian ini adalah pendekatan Enterprise Risk Management (ERM). Hasilnya 1) risiko yang ditemukan pada alur bisnis FAM Organic Tenjolaya berhubungan dengan bidang strategic, operating, dan reporting, 2) ukuran risiko tercermin dari peta risiko berdasarkan peluang dan dampaknya, 3) strategi mitigasi diprioritaskan berdasarkan ukuran risiko dari risiko tertinggi hingga terendah.
Keyword: ERM, mitigasi, organik, risiko

Judul: Strategi Pengembangan Usaha Sayuran Organik di FAM Organic Tenjolaya Kabupaten Bogor Jawa Barat
Abstrak: FAM Organic Tenjolaya merupakan salah satu perusahaan yang membudidayakan sayuran organik. Seiring dengan berkembangnya pesaing dalam pertanian organik dan meningkatnya permintaan produk organik, FAM Organic Tenjolaya mengharapkan keberlanjutan usahanya di masa yang akan datang. Tujuan dari penelitian ini adalah menganalisis faktor eksternal dan internal serta merumuskan strategi pengembangan usaha sayuran organik. Metode yang digunakan dalam penelitian ini adalah Matrix SWOT dan pendekatan Arsitektur Strategi. Hasil dari penelitian ini menunjukkan bahwa ada tujuh alternatif strategi, yaitu (1) Meningkatkan produksi yang diiringi dengan pengembangan pasar ke arah yang lebih luas, (2) Meningkatkan kualitas SDM dan memperbaiki sistem perencanaan, pengorganisasian, serta administrasi, (3) mengoptimalkan kegiatan promosi, (4) Melakukan penelitian dan pengembangan dalam upaya menciptakan inovasi produk, (5) Meningkatkan kualitas dan kuantitas produk sayuran organik, (6) Menciptakan produk olahan berbahan baku sayuran organik, dan (7) Mengelola sumberdaya internal yang sudah dimiliki dengan baik untuk mempertahankan usaha. Kemudian strategi tersebut dipetakan dalam rentang empat periode ke dalam arsitektur strategi.
Keyword: arsitektur strategi, sayuran organik, matriks SWOT

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