Judul: Pengaruh Tingkat Penyangraian Terhadap Karakteristik Fisik Dan Kimia Kopi Robusta (Coffea Canephora. L).
Abstrak: Kopi robusta (Coffea canephora. L) mengandung senyawa aktif terutama berasal dari senyawa fenolik. Komponen aktif yang ditemukan dalam kopi memiliki aktivitas antioksidan yang kuat yang dapat mengurangi radikal bebas dalam tubuh. Senyawa fenolik serta karakteristik fisik kopi dipengaruhi oleh proses penyangraian. Karakteristik fisik (densitas kamba, kadar air, dan warna) dan karakteristik kimia (total fenolik dan kapasitas antioksidan) diuji untuk mengetahui pengaruh penyangraian terhadap karakteristik biji kopi. Densitas, kadar air, dan warna masing-masing dianalisis menggunakan metode gravimetri, oven pengering, dan chromameter. Sementara total fenolik dan kapasitas antioksidan (metode DPPH dan FRAP) dianalisis dengan menggunakan spektrofotometri UV-VIS. Proses penyangraian menurunkan kadar air, densitas kamba, warna, senyawa fenolik, dan kapasitas antioksidan (metode DPPH dan FRAP). Kadar air, densitas kamba, kecerahan warna, total fenol, aktivitas antioksidan (AA) DPPH (IC50) dan AA FRAP kopi beras masing-masing adalah 10.25%, 0.64 g/mL, 52.48, 13.18 g GAE/100 g, 2.49 mg/mL dan 35.26 g TEAC/100 g). Sementara itu, kadar air, densitas kamba, kecerahan warna, total fenol, aktivitas antioksidan (AA) DPPH (IC50) dan AA FRAP kopi sangrai pada tingkat penyangraian lanjut (dark roasted) adalah 0.86%, 0.26 g/mL, 17.87, 7.73 g GAE/100 g, 6.49 mg/mL dan 31.17 g TEAC/100 g.
Keyword: antioksidan, penyangraian, dan total fenol

Judul: Analisis Pengaruh Suhu Penyangraian Kopi terhadap Karakteristik Beberapa Jenis Kopi.
Abstrak: Karakteristik kopi sangat dipengaruhi oleh proses pengolahan kopi, salah satunya proses penyangraian. Penyangraian dengan suhu berbeda mampu memberikan karakteristik fisik, kimia, maupun sensori yang berbeda. Jenis penyangraian atau roasting dapat dibagi berdasarkan suhu yaitu light roasting (170-190 OC), medium roast (200-220 OC) dan dark roast (230 -240 OC). Penelitian terdahulu mengenai penyangraian kopi, umumnya hanya terbatas pada salah satu jenis kopi dan karakteristik tertentu. Pada penelitian ini, varietas kopi Nusantara yang akan diuji lebih bervariasi, meliputi dua jenis kopi Arabika (Arabika Gayo dan Mandheling dari Sumatera Utara) dan dua jenis kopi Robusta (Robusta Lampung dan Pupuan-Bali) dengan respon parameter yang lebih luas. Semakin tinggi derajat penyangraian, semakin besar pengurangan kadar air (pada kopi dark roasted hingga mencapai 1%), tingkat lightness dari warna biji kopi, densitas biji kopi, tingkat kekerasan biji, kadar karbohidrat, kadar kafein kopi jenis Arabika, kadar asam klorogenat, tingkat keasaman, dan keberagaman aroma kopi. Peningkatan total asam akan memuncak hingga lightmedium roasting sebelum asam terdegradasi pada dark roasting. Sebaliknya, proses roasting dapat meningkakan total padatan terlarut, kadar lemak, susut bobot, bodi kopi dan kepahitan kopi, tetapi tidak memberikan pengaruh yang signifikan terhadap kadar protein dan abu maupun kadar kafein jenis Robusta. Selain itu, citarasa sensori dapat berbeda antar satu jenis kopi dengan yang lain karena perbedaan tanah tempat kopi tersebut ditanam.
Keyword: kopi, penyangraian, suhu penyangraian, kopi sangrai, karakteristik kopi

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