Judul: Characteristics of Beef Rendang with Addition of (Zingiber officinale var. Rubrum) During Storage at Room Temperature
Abstrak: Rendang adalah makanan khas Minangkabau yang dibuat selama 4-6 jam dan disimpan untuk waktu yang lama. Selama penyimpanan rendang dapat mengalami ketengikan sehingga penambahan jahe merah selama pembuatan rendang diharapkan mampu meningkatkan antioksidan untuk mencegah ketengikan serta meningkatkan umur simpan rendang. Penelitian ini bertujuan untuk menganalisis karakteristik fisikokimia, mikrobiologi, organoleptik dan aktivitas antioksidan rendang daging dengan penambahan jahe merah sebanyak 0%, 15%, dan 30%. Rendang yang diuji disimpan di dalam stoples penyimpanan yang tertutup rapat dan dibiarkan pada suhu ruangan. Hasil penelitian menunjukkan bahwa penambahan jahe merah berpengaruh nyata (p<0,05) terhadap nilai pH, aktivitas air, kadar air, kadar protein, nilai TBARS, total plate count (TPC), aktivitas antioksidan dan kapasitas antioksidan, namun tidak berpengaruh terhadap kadar abu, kadar lemak, dan keempukan. Penambahan jahe merah pada pembuatan rendang daging nyata meningkatkan (p<0,05) nilai pH, kadar air, nilai aw, kadar protein, dan aktivitas antioksidan serta mampu menurunkan peningkatan nilai TBARS dan menghambat pertumbuhan mikrobiologi selama penyimpanan. Karakteristik kadar abu, kadar lemak dan keempukan tidak menunjukkan perbedaan nyata dengan rendang daging tanpa penambahan jahe merah secara statistika. Dari semua sampel yang telah diuji, pemberian jahe merah sebanyak 30% memberikan hasil terbaik. Secara organoleptik, penambahan jahe merah tidak memberi pengaruh terhadap kesukaan panelis terhadap rendang yang dihasilkan. Dapat disimpulkan penambahan jahe merah pada pembuatan rendang daging memberikan efek positif terhadap karakteristik fisikokimia, meningkatkan aktivitas antioksidan, serta menghambat pertumbuhan mikrobiologi.
Keyword: antioxidant, microbiology, organoleptic, red ginger, rendang

Judul: Characteristics of Beef Dendeng Added by Fresh Red Ginger (Zingiber officinale var. Rubrum) as A Seasoning
Abstrak: Dendeng merupakan produk olahan daging yang dapat mengalami ketengikan akibat adanya reaksi oksidasi lipida. Penambahan jahe merah segar sebagai bumbu dapat menghambat pertumbuhan bakteri, meningkatkan aktivitas antioksidan dan berpotensi meningkatkan keempukan. Penelitian ini bertujuan menguji karakteristik dendeng sapi yang diberi penambahan jahe merah segar sebagai bumbu. Penelitian ini menggunakan perlakuan penambahan jahe merah segar dengan konsentrasi sebesar 0%, 12,5% dan 25% dari berat daging. Daging sapi diiris dengan ketebalan 5 mm dan dimarinasi dengan bumbu selama 12 jam. Daging yang telah dimarinasi, dikeringkan dalam oven pada suhu 60 °C selama 3 jam dan dilanjutkan dengan menggunakan suhu 70 °C selama 5 jam. Hasil penelitian menunjukkan bahwa penambahan jahe merah segar pada dendeng mampu meningkatka pH, aw, kadar air, aktivitas penghambatan DPPH, dan kapasitas antioksidan, serta menurunkan tingkat kekerasan, kadar MDA dan TPC. Bakteri Salmonella sp., Escherichia coli dan Staphylococcus aureus tidak terdeteksi keberadaanya pada semua perlakuan dan lama penyimpanan. Berdasarkan keempukan yang dihasilkan, penambahandan jahe merah segar sebesar 12,5% dan 25% pada dendeng sapi matang lebih disukai panelis. Dendeng sapi dengan penambahan jahe merah segar sebesar 25% menunjukkan hasil terbaik untuk pengujian sifat fisikokimia, organoleptik, mikrobiologi, dan aktivitas antioksidan., Dendeng is a processed meat product that can have rancidity due to lipid oxidation and microbiology activity. Adding fresh red ginger as a spices can influencing taste, inhibit the growth of bacteria, increasing antioxidant activity, and potentially increasing tenderness. This study aims to examine the characteristics of beef jerky added by fresh red ginger (Zingiber officinale var. Rubrum) as a seasoning in marination step. This study used the addition of fresh red ginger with proportion 0%, 12,5% and 25% from meet weight. Beef was sliced with a thickness of 5 mm then was marinated with spices for 12 hours. Marinated beef was dried using oven at 60 °C for 3 hours and was continued by using temperature of 70 °C for 5 hours. The results showed that the addition of fresh red ginger to beef jerky was able to increase pH, aw, moisture content, DPPH inhibitory activity, and antioxidant capacity, as well as reduce hardness level, MDA and TPC numbers. Salmonella sp., Escherichia coli and Staphylococcus aureus bacteria were not detected in all treatments and storage time. Based on the tenderness result, the addition of fresh red ginger by 12.5% and 25% in cooked beef jerky was preferred by the panelists. Beef jerky with the addition of 25% fresh red ginger showed the best results for testing the physicochemical, organoleptic, microbiological, and antioxidant activity properties.
Keyword: antioxidant, beef jerky, fresh red ginger, microbiology, sensory

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