Judul: Pemanfaatan Potensi Manggis Merah (Garcinia Forbesii) Sebagai Pengawet Alami Dan Minuman Fungsional
Abstrak: Manggis merah (Garcinia forbesii) merupakan tanaman langka yang masih satu famili dengan manggis (Garcinia mangostana L.), yang memiliki warna merah dan rasa asam. Penelitian ini bertujuan untuk mengetahui aktivitas antimikroba ekstra buah manggis merah terhadap mikroba patogen dan perusak makanan, yaitu Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococus aureus, Bacillus subtilis dan Bacillus cereus, serta pengembangan minuman kaya antioksidan bercitarasa rempah. Pengujian aktivitas antimikroba dilakukan dengan metode difusi sumur terhadap ekstrak kulit dan daging buah manggis merah yang diperoleh melalui ekstraksi tunggal menggunakan pelarut air dan etanol, dan ekstraksi bertingkat menggunakan pelarut heksanaa, etil asetat dan metanol. Semua jenis ekstrak kulit dan daging buah manggis merah yang diuji memiliki aktivitas antimikroba. Ekstrak heksanaa bagian kulit dan daging buah memiliki daya penghambatan terkecil dan daya penghambatan terbesar ditunjukkan ekstrak etil asetat dari kulit buah manggis merah (1.88±0.082 cm) dan ekstrak etanol (2.00±0.082) terhadap bakteri Bacillus cereus. Selanjutnya pada ekstrak etanol dan etil asetat bagian kulit buah manggis dilakukan uji MIC untuk mengetahui konsentrasi minimum yang masih memiliki kemampuan menghambat pertumbuhan mikroba. Nilai MIC yang diperoleh dari ekstrak etil asetat kulit buah manggis merah adalah 0.05 mg/mL pada semua mikroba kecuali pada S. Typhimurium sebesar 0.03 mg/mL, sedangkan pada ekstrak etanol kulit buah manggis merah nilai MIC yang diperoleh sebesar 0.05 mg/mL untuk S. aureus, B. subtilis dan B. cereus. Nilai MIC ekstrak etanol kulit buah manggis merah pada E coli sebesar 0.3 mg/mL dan 0.4 mg/mL untuk S. Typhimurium dan P. aeruginosa. Kulit buah manggis merah memiliki kapasitas antioksidan yang tinggi yaitu sebesar 2215.6±1.06 AAE (μg/mL) dan pengembangannya sebagai minuman fungsional menunjukkan bahwa campuran ekstrak kulit manggis dan jeruk nipis pada rasio 5:1 (v/v) merupakan formula dasar terpilih dengan nilai kapasitas antioksidan 821.9±0.71 AAE (μg/mL). Selanjutnya pengembangan citarasa rempah pada formula dasar terpilih menunjukkan formula citarasa terpilih adalah campuran ekstrak jahe dan cengkeh dengan rasio 9:2:1 (v/v/v) yang memiliki kapasitas antioksidan 2340.4 AAE (μg/mL) dan formula dasar dan ekstrak kayu manis dengan rasio 5:3 (v/v) yang memiliki kapasitas antioksidan sebesar 1781.3 AAE (μg/mL).
Keyword: manggis merah, antimikroba, MIC, antioksidan, ekstrak

Judul: Kajian Pemanfaatan dan Pengembangan Potensi Ekstrak Manggis Merah (Garcinia forbesii) sebagai Minuman Fungsional Kaya Antioksidan dan Kestabilannya
Abstrak: Red Mangosteen (Garcinia forbesii) is a fruit that comes from South Kalimantan and rarely used by people. Red mangosteen fruit has white flesh with sweet flavor and fresh red rind with strong sour taste. Red mangosteen rind has a potency to be processed into functional beverage. This study is aimed to determine the functional beverage formulas made from red mangosteen rind extract (M) and emprit ginger extract (J) with the addition of pekak extract (P) and cinnamon extract (K) and then analyzed the characterization of physicochemical properties, functional and determination of its stability at high temperatures. Raw Material M obtained in the form of extracts with 1.4 TPT oBrix while J , P and K obtained by water extraction with water and spices ratio of 1:10 for J and 1:5 for P and K. Formula drink based on M and J was made in three concentration ratio, which is 5:1 (MJ 1), 5:2 (MJ 2), and 5:3 (MJ 3) (v/v). MJ 1 formula is selected as the basic formula based on hedonic rating test and antioxidant capacity. MJ 1 formula is given each addition P extracts and K extract which is made in three different concentrations, are 5%, 10%, and 15%. Formula MJP 5% and MJK 5% is chosen as the selected formula to determine its stability. Formula MJP 5% has the antioxidant capacity 6,46 mg AEAC/mL and total phenol content 20,38 mg GAE/mL, while MJP 5% has 7,95 mg AEAC/mL antioxidant capacity and total phenol content 21,17 mg GAE/mL. Determination of stability MJP 5% and MJK 5% to 85% usable quality was conducted by using the Arrhenius method with antioxidant capacity and total phenol content as parameters. Both formulas packaged in vials dark bottles of 10 ml volume without heating treatment processes, such as pasteurization and sterilization and stored in an incubator at temperature 35°C, 45°C and 55°C. Stability of MJK 5% stored at 35°C, 45°C and 55°C are 8 days, 5 days and 4 days based on the antioxidant capacity parameter and 7 days, 6 days, and 5 days based on total phenol parameters, while the stability of MJP 5% is 9 days, 6 days, and 5 days based on antioxidant capacity parameter and 7 days, 6 days, and 5 days based on total phenol parameter. The observation results showed that the formula mixture of red mangosteen rind extract and ginger extract by the addition of pekak extract and cinnamon extract could potentially be developed as a functional drink from the utilization of rare fruit cultivation, red mangosteen fruit.
Keyword: Arrhenius, cinnamon, pekak, emprit ginger, red mangosteen rind, functional drink

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