Judul: Bioactivity of Fungus Comb Extracts from  Subterranean Termite’s Nests of Subfamily Macrotermitinae on Wood Stain Fungi
Abstrak: Indonesia merupakan salah satu wilayah sebaran utama spesies rayap dari subfamili Macrotermitinae (Isoptera: Termitidae). Salah satu keunikan spesies rayap dari subfamili ini adalah sumber makanannya berupa nodul jamur yang tumbuh pada fungus comb di dalam sarang rayap tersebut. Fungus comb tersebut hanya dapat ditumbuhi oleh genus jamur Termitomyces. Hal ini kemungkinan besar terkait dengan aktivitas senyawa kimia pada fungus comb. Penelitian ini bertujuan menentukan ekstrak fungus comb dari sarang rayap subfamili Macrotermitinae teraktif sebagai penghambat pertumbuhan jamur pewarna kayu Aspergillus foetidus. Ekstraksi fungus comb dilakukan secara bertingkat menggunakan n-heksana, etil asetat, metanol, dan air destilata. Masing-masing ekstrak dan larutan kontrol positif (metilen bistiosianat) ditentukan bioaktivitasnya terhadap A.foetidus berdasarkan metode persentase penghambatan pertumbuhan jamur tersebut pada media PDA (Potato Dextrose Agar). Hasil penelitian menunjukkan bahwa semua larutan ekstrak fungus comb memiliki aktivitas terhadap A.foetidus dengan daya hambat pertumbuhan berkisar antara 21,67% sampai 100%. Ekstrak etil asetat dengan konsentrasi 6% menunjukkan daya hambat pertumbuhan A.foetidus tertinggi., Indonesia is one of the main geographical distribution regions for termite species from the subfamily Macrotermitinae (Isoptera: Termitidae). One of the uniqueness of the termite species from this subfamily is that they developed a brain like structure inside their nest, known as fungus comb, as a substrate for growing Termitomyces fungus. On the fungus comb, Termitomyces fungus is the only genus of fungus that can grow. It is suspected that the chemical compounds of the fungus comb inhibited other fungus to be grown. This study aimed to determine the active extract of the fungus comb from the subfamily Macrotermitinae as an inhibitor for the wood staining fungi Aspergillus foetidus growth. Fungus comb extraction was carried out gradualy using n-hexane, ethyl acetate, methanol, and distilled water respectively. Each extract and positive control solution (methylene bistiocyanate) was determined for its bioactivity on A.foetidus based on the method of inhibiting the growth of the fungus on PDA (Potato Dextrose Agar) media. The results showed that all fungus comb extracts had activity in inhibiting A.foetidus growth with inhibition rate ranged from 21.67% to 100%. Ethyl acetate extract with a concentration of 6% showed the highest growth inhibition rate on A.foetidus
Keyword: Aspergillus foetidus, Bioaktivitas, Ekstrak, Fungus comb

Judul: Bioactivity of Fungus Comb Extracts on Wood Decay Fungi
Abstrak: Fungus comb adalah struktur khusus yang dibuat oleh koloni rayap tanah dari subfamili Macrotermitinae di dalam sarangnya sebagai substrat untuk menumbuhkan jamur Termitomyces. Struktur khusus tersebut diketahui dilaporkan mengandung senyawa aktif yang bersifat antimikroba, termasuk antijamur pewarna kayu. Namun demikian bioaktivitasnya sebagai antijamur pelapuk kayu belum pernah dilaporkan. Penelitian ini bertujuan menganalisis bioaktivitas ekstrak fungus comb dari sarang rayap tanah Macrotermes gilvus terhadap jamur pelapuk kayu Schizophyllum commune. Fungus comb diekstrak menggunakan metode ekstraksi maserasi bertingkat menggunakan pelarut n-heksana, etil asetat, metanol, dan air. Larutan ekstrak fungus comb hasil penelitian menunjukkan bahwa ekstrak fungus comb dapat menghambat pertumbuhan S. commune. Konsentrasi ekstrak n-heksana, etil asetat, metanol, dan air 2%, 4%, dan 6% menghasilkan nilai penghambatan yang berbeda terhadap pertumbuhan S. commune. Namun demikian larutan ekstrak etil asetat fungus comb memberikan hasil terbaik dibandingkan dengan ekstrak metanol, n-heksana, dan air., Fungus comb is a special structure created by subfamily Macrotermitinae colonies termite in their nests as a substrate for growing the fungus Termitomyces. This special structure is known to contain active compounds with antimicrobial properties, including antifungal for wood staining fungi. However, its bioactivity as an antifungal for wood decay fungi has never been reported. This study aimed to analyze the bioactivity of fungus comb extract from subterranean termites Macrotermes gilvus against the wood decay fungi Schizophyllum commune. Fungus comb was extracted using the multistage maceration extraction method using n hexane, ethyl acetate, methanol, and water. The fungus comb extract solution showed that the fungus comb could inhibiting the growth of the S. commune. Concentration of 2%, 4%, and 6% on the extracting material n-hexane, ethyl acetate, methanol, and water produced different inhibitory values on the growth of the fungus S. commune. The ethyl acetate extract gives the best results compared to methanol, n-hexane, and water.
Keyword: Bioactivity, extract, fungus comb, Schizophyllum commune

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