Judul: Pengujian Ketahanan Kayu Jabon Terimpregnasi Metil Metakrilat dan Termitisida Fipronil Melalui Uji Kubur (Graveyard Test).
Abstrak: Kayu jabon (Anthocephalus cadamba Miq.) adalah kayu cepat tumbuh yang kelas awet kayunya rendah (kelas V). Tujuan penelitian ini adalah untuk menganalisis pengaruh impregnasi kayu jabon menggunakan bahan metil metakrilat (MMA) dan termitisida komersial (fipronil) terhadap kerusakan akibat organisme perusak kayu melalui uji kubur. Proses impregnasi terhadap contoh uji dilakukan dengan polimerisasi pemanasan. Hasil penelitian menunjukkan bahwa kadar air kayu meningkat setelah tiga bulan uji kubur, yaitu kayu kontrol dari 10,96 %, menjadi 38,34 %, kayu MMA dari 8,32 % menjadi 29,05 %, dan kayu fipronil dari 11,31 % menjadi 28,34 %. Nilai rata-rata polymer loading kayu MMA sebesar 24,91 % dan retensi kayu fipronil 5,98 kg/m3. Perlakuan impregnasi yang diberikan pada kayu jabon berpengaruh nyata (p < 0,05) meningkatkan nilai ketahanan kayu berdasarkan nilai ketahanan terhadap rayap tanah, perubahan warna dan kehilangan berat. Klasifikasi ketahanan kayu terhadap rayap tanah meningkat, yaitu dari 7,33 (kontrol) menjadi 8,67 (MMA) dan 10 (Fipronil). Kerusakan visual warna menurun dari 86,67 (kontrol) menjadi 76,67 (MMA) dan 40,00 (fipronil). Kehilangan berat menurun dari 19,74 % (kontrol) menjadi 16,04 % (MMA) dan 13,26 % (fipronil).
Keyword: jabon, impregnasi, metil metakrilat, fipronil, uji kubur

Judul: Sifat Fisis dan Mekanis Kayu Jabon (Anthocephalus cadamba) Hasil Furfurilasi.
Abstrak: Kayu jabon merupakan salah satu jenis kayu cepat tumbuh yang mengandung banyak kayu juvenile sehingga sifat fisis dan mekanis lebih rendah dibandingkan kayu dewasa. Tujuan penelitian ini adalah untuk menganalisis sifat fisis dan mekanis kayu jabon yang diimpregnasi furfuril alkohol. Proses impregnasi menggunakan metode vakum tekan. Hasil impregnasi FA kemudian dibandingkan dengan kayu jabon tanpa perlakuan impregnasi (kontrol). Rata-rata nilai weight percent gain (WPG) FA 99,37%. Rata-rata nilai kadar air, kerapatan, daya serap air, pengembangan volume, penyusutan, modulus elastisitas, modulus patah, kekerasan radial, kekerasan tangensial dan kekerasan ujung kayu jabon hasil impregnasi secara berturut-turut sebesar 2,15%, 0,64 g/cm3, 6,15%, 0,83%, 0,84%, 54.486 kg/cm2, 318 kg/cm2, 265 kg/cm2, 264 kg/cm2 dan 182 kg/cm2. Sementara itu, nilai rata-rata kadar air, kerapatan, daya serap air, pengembangan volume, penyusutan, modulus elastisitas, modulus patah, kekerasan radial, kekerasan tangensial dan kekerasan ujung kayu jabon kontrol secara berturut-turut sebesar 12,61%, 0,34 g/cm3, 69,9%, 6,9%, 2,87%, 52.924 kg/cm2, 493 kg/cm2, 127 kg/cm2, 134 kg/cm2 dan 258 kg/cm2. Kayu jabon hasil impregnasi FA memiliki stabilitas dimensi tinggi karena anti swelling efficiency (ASE) 88,81%. Berdasarkan analisis ANOVA, kayu jabon hasil furfurilasi tidak berpengaruh nyata pada pengujian modulus elastisitas dan kekerasan ujung.
Keyword: jabon, fisis, mekanis, impregnasi, jabon hasil furfurilasi

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