Judul: Karakterisasi Sifat Fisiko Kimia dan Analisa Profil Deskriptif Flavor Minyak Biji Adas (Foeniculum vulgare Mill)
Abstrak: Tanaman adas merupakan salah satu tanaman obat dan penghasil minyak atsiri yang belum banyak mendapat perhatian. Manfaat dan khasiat tanaman ini telah lama digunakan secara tradisional sebagai obat dan pengharum masakan karena aromanya yang sangat harum. Pada penelitian ini digunakan biji adas yang telah dikeringkan yang diperoleh dari Kebun Percobaan Balai Penelitian Tanaman Rempah dan Obat yang terdapat di Gunung Putri, Cipanas.
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Judul: Karakteristik Sifat Fisiko Kimia dan Analisa Profil Deskriptif Flavor Minyak Biji Adas (Foeniculum vulgare Mill)
Abstrak: Tariarnari adas merupakan salah satu tariaman obat dan penghasil rriiilyak alsiri yang belum banyak meridapat perhatian. Manfaat dari kliasiat tarlamati irri lelah lama digunakatt secara tradisional sebagai obat dar~ penyharum masaka11k are~iaa )-oniatiyay arig saltgat liar-um. Pada penelitian ini diyunakan biji adas yang telah dikeringlca~t yaiiy diperoleli dari I(ebu11 Percobaati Balai Perielitian Tanarnan Rempah dari Obal yari(4 teidapat dr Gunurlg Pulri, Gipaiias. Pada tahap awal dilakukan periyulirigan minyak biji adas uritilk rnerIyetahui rendemen yariy diliasilka~i. Kerriudiari mitiyak yarig diliasilka~: tersebut dianalisa sifat fisiko kimianya untuk rnengetahui kemur~liari dari r;;uturiya uiltuk seiarjutl1ya diideilttfikasi dan dideskripsikan ltornpolier!- Itompotieti penyusun minyak biji adas. Berdasarkan hasil aiialisa sifat fisik dari kimia, didapat baliv/a minyak biji adas yalig dillasilkart derlgati metode petiyulingan uap selama 3 jam iiiernilik~k ualilas atau iriutu yarig cukiip baik seliitigga miriyak tersebut dapal diailalisa Lebil.1 la~~jigltu tla mengidentifikasi dan mendeskripsikari komponei! ar'uma i~iiriyaici- ~ijai das. Dari kromatogram krornatografi gas (GC) dapat diidetitifikasi 14 ir~acani koinporieri penyusvn mi!!yak adas; yaitu g~loiigari ii~oiio'rerperl (9 !:.ompo!!eri), ;)lieiiol (2 komponeii) dari golorigan keton (3 komporien). Yang tert~iasuk goiongall phenol adalah anelo! dan iso arletol yalrg berkontribusi paling besar yaitu 58.52%; galongan monoterpen terdiri dari cu-pinen (3.0%), [Lpi!ien (1.29%), (3-myrcene (0.89%), camphetie (0.33%), (7,-limonene (3 59+,!/: piieilandrene (0.48%), p-cymene (0.22%), cr.-terpinolene (0.16°h), 4-carena (!.30%); sedangkari golu~tgan kelon yarig berliasil diidentifikasi adalah dcarnphore (D.35%), fenchorie (26.7%) dari 3.tliujene (0.38%). Deiigaii menggunakan kromatografi gas- oliaktometri (GC-0), aroma kornpoi~en-kornponeri tersebut dapat dideskripsikan. Komponen-komponeri yarig ber1;asrl dideskripsikar~ aromariya adalah u.-pinen ( P I I ) ~ , ~voo(Yy), pmyrcene (niarigga muda), linio~ieri (citr~ls dari sweet), terpinolene (minyak ianaiii, cyrnerie (s:weef. irility, rumput), arietllol (sweet, iiangat, biji adas kuat. rneiiyeriarigltanj, phellandrene (herba), carnphore (pedas, hangat), fenchnne ibau obat, palill) Dari iiasil deskripsi tersebut, tarnpak baliwa walaupi~n aiietol barkontribusi paling besar sebagai komponen pen)fusurl rninyak aaas, i~arnui~a rcma khas adas dibentuk dan dikarakterisasi ole11 sejumlali kotnpoii~n-Iiompcl~peeli~\y usunl>ya.
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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