Judul: Kajian proses pembuatan bubuk kunyit (Curcuma domestica Val.) dan analisa finansial
Abstrak: Indonesia kaya akan berbagai sumber daya alam, diantaranya tanaman obat. Salah satu tanaman obat yang telah banyak dimanfaatkan dalam kehidupan sehari-hari adalah kunyit (Curcuma domestica Val.). Kunyit juga bermanfaat sebagai bumbu masak, bahan kosmetik, serta pewarna makanan maupun tekstil. Penelitian ini bertujuan untuk mendapatkan pengirisan yang optimal, proses pengeringan (suhu dan waktu) yang optimal serta mendapatkan informasi kelayakan ekonomis. Pada penelitian ini, kunyit dibagi atas dua macam perlakuan yaitu rimpang kunyit di iris setebal 1-2 mm serta kombinasi pengirisan dan perajangan. Kemudian dikeringkan menggunakan sistem pengeringan tipe rak dengan suhu 60°C. Rimpang kunyit yang diiris dan dirajang (kombinasi), dikeringkan selama 8 jam (A,B₁), 10 jam (A,B2), dan 12 jam (A,B3). Sedangkan rimpang kunyit yang diiris setebal 1-2 mm dikeringkan dengan lama pengeringan yang berbeda yaitu 8 jam (A2B1), 10 jam (A2B2), dan 12 jam (A2B3). Setelah dikeringkan kemudian kunyit dihancurkan menggunakan grinder (80 mesh) menjadi bubuk kunyit. Penelitian pendahuluan menggunakan rimpang kunyit segar memperoleh hasil analisa kadar air 89,0%, kadar abu 1,78%, kadar protein 1,00%, kadar lemak 0,46%, kadar serat kasar 0,44%, kadar karbohidrat 7,32% dan kadar kurkumin 2,58%. ...
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Judul: Blanching Treatment and Drying Temperature to Maintain  Quality of Turmeric (Curcuma domestica Val.) as Traditional Herbal Medicine
Abstrak: Kunyit (Curcuma domestica Val.) merupakan salah satu komoditas sebagai bahan baku jamu tradisional yang sering dikonsumsi oleh masyarakat Indonesia karena mengandung kurkumin untuk melawan radikal bebas dalam tubuh. Penelitian ini bertujuan mengkaji pengaruh blanching dan suhu pengeringan terhadap mutu kunyit. Perlakuan yang dilakukan adalah blanching pada suhu 90 ℃ selama 10 menit dan suhu pengeringan yang digunakan adalah 50, 60, 70 ℃. Selama penelitian, dilakukan analisis parameter terhadap laju pengeringan, kadar air, kadar abu, warna dan kadar kurkuminoid. Rancangan percobaan yang digunakan adalah Rancangan Faktorial yang disusun secara acak lengkap RAL 2 faktor dan RAL 1 faktor untuk kurkuminoid. Hasil penelitian menunjukan bahwa blanching 90 ℃ meningkatkan laju pengeringan, kadar abu serta kurkuminoid dan menurunkan kadar air akhir serta warna. Perbedaan suhu pengeringan meningkatkan laju pengeringan dan menurunkan kadar air. Perlakuan blanching dan suhu pengeringan 60 ℃ merupakan proses yang menghasilkan mutu kunyit terbaik dengan kadar air akhir 4,12±0,66 %bk/menit, kadar abu total 6,58±0,5869%, bisdesmethoxycurcumin 1,73±0,13%, desmethoxycurcumin 1,35±0,1%, kurkumin 3,84±0,36%, serta kurkuminoid 6,92±0,13%. Berdasarkan SNI 7953-2004 kunyit yang dihasilkan masuk ke dalam Mutu I.
Keyword: Curcuma domestica Val, kunyit, kurkumin, Steam blanching

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