Judul: Pertumbuhan Jamur Kuping (Auricularia auriculae) dan Jamur Tiram (Plerotus ostreatus) dalam Rumah Tanaman dengan Suhu Terkendali
Abstrak: Rumah tanaman yang dirancnag denan sistem pengontrolan suhu secara otomatik merupakan suatu penerapan teknologi modern dibidang pertanian. Salah satu kondisi pertanian yang pertumbuhan dan perkembangannya dipengaruhi oleh suhu udara adalah jamur. Saat ini komoditas jamur sangant digemari dan banyak dibudidayakan karena kandungan nilai gizinya yang tinggi. Jenis jamur yang sudah banyak dibudidayakan di Indonesia adalah Jamur Kuping dan Jamur Tiram.
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Judul: Distribusi Suhu dan Kelembaban dalam Ruang Tumbuh Jamur Terkendali
Abstrak: Jamur merupakan salah satu bahan pangan yang memiliki kandungan gizi tinggi dan banyak terdapat di alam. Jamur menjadi salah satu komoditas ekspor yang tengah dikembangkan sehingga belum banyak produsen jamur yang ada di Indonesia. Proses budidaya jamur berkembang sejalan dengan proses perkembangan teknologi budidaya yang meningkat. Dalam budidaya jamur pada prinsipnya adalah mengusahakan kondisi lingkungan yang sesuai dengan habitat tumbuhnya di alam. Lokasi yang ideal untuk budidaya jamur adalah pada ketinggian lebih dari 800 m dp\., dengan kelembaban udara (RR) berkisar antara 60-90%. Untuk budidaya jamur di dataran rendah dapat dilakukan dengan sistem pengkondisian ruang budidaya. Salah satu cara adalah dengan melakukan pengkondisian suhu dan RH di dalam ruang tumbuh. Penelitian ini bertujuan untuk mengetahui distribusi suhu dan kelembaban ruang tumbuh untuk budidaya jamur dengan pengendalian suhu menggunakan kendali fUzzy, dan menguji pengaruh pengkondisian ruang tumbuh terhadap pertumbuhan jamur.
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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