Judul: Ketahan Pangan Rumah Tangga Petani Penghasil Beras Organik
Abstrak: Penelitian ini menggunakan desain Cross Sectional. Populasi dalam penelitian ini adalah rumah tangga peserta Program Pemberdayaan Petani Sehat yang diselenggarakan oleh Lembaga Pertanian Sehat bekerjasama dengan Gabungan Kelompok Tani Silih Asih di Desa Ciburuy, Kecamatan Cigombong, Kabupaten Bogor. Sampel dipilih secara acak dengan ukuran sebesar 61 rumah tangga. Pengumpulan data primer diperoleh melalui wawancara langsung dengan responden yang berpedoman pada kuesioner yang telah dipersiapkan sebelumnya dan telah diujicobakan. Data konsumsi pangan dilakukan dengan cara recall konsumsi pangan 1 X 24 jam, dan food frequency (seminggu, sebulan, dan setahun). Indikator yang digunakan untuk mengukur ketahanan pangan rumah tangga adalah tingkat kecukupan konsumsi energi (TKE). Suatu rumah tangga disebut tahan pangan jika tingkat kecukupan energi = 70%, dan jika < 70% disebut tidak tahan pangan. Untuk melihat hubungan antar peubah digunakan analisis korelasi dan untuk menentukan faktor-faktor determinan dari peubahpeubah bebas yang mempengaruhi ketahanan pangan digunakan analisis regresi lenear berganda.
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Judul: Ketahanan Pangan Rumah Tangga Petani Penghasil Beras Organik
Abstrak: Organic farming is very emphasized in using local input and decreasing external input. Therefore, farmers, who applied organic farming, were expected having a chance to increase their access of food. The study was designed to analyze level of household food security of farmers who yielding organic rice and identify landholding that supporting household food security. For that purposes, 61 samples of household were selected from farming households whose practicing organic farming in Ciburuy Village, Bogor Regency. The gathered data are including data on landholding, working capital, cooperation, education, purposes of practicing on organic farming, wasted management, knowledge of organic farming, organic rice productivity, income, food consumption and level of household food security. Food consumption is recalled in 1 X 24 hours and food frequency (weekly, monthly, and yearly) is used to obtain food consumption data. Household food security was determined based on level of energy consumption. Landholding that supporting household food security is determined based on rice consumption and household productivity. The analyses result of household food security showed that 85.2% of households were secure and 14.8% were insecure. Variables that significantly influenced household food security were income, knowledge of organic farming, organic rice productivity, purposes of practicing on organic farming, landholding, and waste management. Pearson correlation analyses showed that income, knowledge of organic farming, organic rice productivity, and landholding had significant effect toward household food security. Spearman correlation analyses showed that purposes of practicing on organic farming and waste management had significant association with household food security too. Based on daily average of energy sufficiency, land-man ratio should be 318 m2/person.
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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