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: Diversifikasi sayuran organik sebagai alternatif mengatasi risiko usaha pada The Pinewood Organic Farm di Kabupaten Bogor
Abstrak: Risk is the possibility of adversity or loss, and refers to uncertainty that matters. Consequently, risk management involves choosing among alternative to reduce the effect of risk. One of indicator to know the existence of production risk is the fluctuation in productivity of brokoli, tomato, bayam hijau, and carrot. This study analyzes the risk of commodity production in the four selected in the Pinewood Organic Farm with a diversified approach to analyze the risk of production of more than one commodity. Fluctuations in production will lead to revenues received also fluctuates, will be calculated risks and confectionary risk through the implementation of risk management strategies. Risk analysis is a comprehensive step in the risk assessment process. This assessment begins with the calculation of odds, the value of expected return, until the value of the risk. The analysis tools is the variance (the variance), standard deviation (standard deviation), and the coefficient of variation (coefficient of variation). Measures of deviation is also aided by another measure, namely the calculation of the odds and expected return. The results of the risk analysis calculations show that the specialization of business spinach organic green vegetables have the highest risk level of the four commodities. Based on the comparison of the risk on the four organic vegetables broccoli, tomatoes, green spinach, and carrots are done Pinewood Organic Farm concluded that diversification can reduce risk. However, by diversifying no means eliminate the risk or create a risk to zero. Diversified production activities in the Pinewood Organic Farm has been effectively implemented by the company in an effort to deal with the risk. The strategy needs to be done by the company is to improve the management of the company, to cooperate or partner with other farmers and suppliers of organic vegetables.
Keyword: 

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