Judul: Studi Pengendalian Kualitas Gondorukem di PGT. Paninggaran-KPH Pekalongan Timur Perum Perhutani Unit I Jawa Tengah
Abstrak: Gondorukem merupakan salah satu hasil hutan non kayu yang diperoleh melalui proses pengolahan getah menjadi gondorukem rata-rata 63% dan luas areal hutan tanaman pinus yang telah berumur 11 tahun seluas 468.800 ha (Mukerji, 1989), maka potensi gondorukem di seluruh Indonesia diperkirakan dapat mencapai 180.882 ton per tahun.
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Judul: Analisis biaya produksi pengolahan getah pinus menjadi gondorukem dan terpentin di Pabrik gondorukem dan terpentin Sindangwangi, Nagreg, KPH Bandung Utara Perum Perhutani Unit III Jawa Barat
Abstrak: Hutan merupakan salah satu sumberdaya yang penting bagi bangsa Indonesia, karena mempunyai manfaat cukup besar bagi pembangunan dewasa ini. Didalam pemanfaatannya tumpuan utama. hasil hutan berupa kayu merupakan Sedangkan di dalam pemanfaatan hasil hutan non kayu masih belum dimanfaatkan secara optimal, padahal potensi hasil hutan non kayu tersebut mempunyai potensi yang cukup besar. Salah satu hasil hutan non kayu adalah getah pinus, yang berasal dari tegakan Pinus merkusii. Pemanfaatan getah Pinus merkusii sebagai bahan baku industri mempunyai prospek yang cerah serta peranannya semakin penting di dalam menunjang pendapatan Perum Perhutani. Industri gondorukem dan terpentin pada dewasa baik tingkat efisiensi maupun produktivitasnya masih dapat ditingkatkan. Tujuan penelitian ini adalah untuk mengetahui proses produksi pengolahan getah pinus menjadi gondorukem dalam kaitannya dengan faktor-faktor yang mempengaruhi biaya produksi produktivitas tiap-tiap proses produksi, besar biaya produksi serta tingkat break even point (BEP) di PGT (Pabrik Gondorukem dan Terpentin) Sindangwangi, 'Nagreg, KPH Bandung Utara Pe- rum Perhutani Unit III Jawa Barat. Produktivitas diketahui melalui pengamatan langsung terhadap unit-unit kegiatan proses produksi gon- dorukem. Selanjutnya nilai produktivitas ini dibandingkan untuk mengetahui keseimbangan tiap mesin dengan menggunakan ujit student. ...
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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