Judul: Reformulasi Minuman Fungsional Berbasis Kumis Kucing yang Diperkaya dengan Emulsi Minyak Buah Merah (Pandanus Conoideus Lam).
Abstrak: Minuman fungsional berbasis kumis kucing mengalami penurunan aktivitas antioksidan serta degradasi warna selama penyimpanan. Penambahan emulsi minyak buah merah dilaporkan dapat mengatasi masalah tersebut, namun belum diperoleh jumlah penambahan emulsi yang tepat. Jumlah penambahan emulsi berkorelasi negatif dengan penerimaan sensori terhadap minuman, namun berkorelasi positif pada peningkatan kapasitas antioksidan. Penelitian ini bertujuan untuk memformulasi ulang minuman fungsional kumis kucing yang diperkaya emulsi minyak buah merah agar dapat lebih diterima secara sensori dan memiliki aktivitas antioksidan yang memadai. Minyak buah merah yang digunakan adalah minyak terpilih pada hasil penelitian sebelumnya, yaitu yang berasal dari Jayapura. Emulsi dibuat dengan metode yang telah dilaporkan terdahulu, menggunakan minyak buah merah X% dan Y% carboxymethyl cellulose (CMC). Minuman fungsional dibuat dengan metode yang telah dilaporkan terdahulu yaitu mencampurkan ekstrak simplisia kumis kucing, secang, jahe dan temulawak dengan ekstrak jeruk nipis dan purut serta bahan tambahan pangan sesuai formula yang sudah ditetapkan. Optimasi formula dilakukan menggunakan Response Surface Methodology (RSM) dengan program Design Expert®7.0.0. Sebanyak delapan perlakuan dihasilkan dari program dan dianalisis total komponen fenolik, lightness, oHue, viskositas, dan hedonik (warna, rasa, keseluruhan). Hasil optimasi menunjukkan formula optimum terpilih adalah dengan penambahan emulsi sejumlah Z% (b/v). Formula terpilih memiliki nilai desirability 0.756. Dari hasil verifikasi, diperoleh minuman dengan total fenolik 367.43±1.95 mg GAE/L, tingkat kesukaan terhadap keseluruhan minuman adalah agak suka (nilai hedonik 4.6 dari 7.0) dan tidak terdapat perbedaan nyata dengan minuman tanpa penambahan emulsi pada taraf signifikansi 5%. Panelis lebih menyukai warna minuman formula terpilih (skala hedonik 5.0 dari 7.0) dibandingkan warna minuman tanpa emulsi (skala hedonik 4.0 dari 7.0).
Keyword: Emulsi, kumis kucing, minuman fungsional, minyak buah merah, response surface methodology

Judul: Optimasi Formula  Minuman Fungsional Berbasis Kumis Kucing Menggunakan Ekstrak Hasil  Sterilisasi Non-Termal Teknologi Membran
Abstrak: Minuman fungsional berbasis kumis kucing sudah dikembangkan sejak 2007, namun dalam perjalannya produk ini menghadapi permasalahan terkait tingginya cemaran mikroba, sehingga pengolahannya bergantung pada proses termal dan penggunaan BTP untuk menjaga umur simpan. Penelitian ini bertujuan mengoptimasi formula baru menggunakan ekstrak yang telah melalui membran mikrofiltrasi menggunakan metode D-optimal mixture design pada Response Surface Methodology (RSM) dengan mempertimbangkan sensori hedonik dan aktivitas antioksidannya. Sebanyak 19 rancangan formula dihasilkan oleh software Design Expert 13.0Ⓡ sebagai data keluaran dari data masukan berupa, nilai batas bawah-atas dari ekstrak kumis kucing (EKK: a-d mL), ekstrak kayu secang (EKS: b-e mL), dan ekstrak jahe gajah (EJG: c-f mL). Hasil penelitian menunjukkan formula optimum memiliki nilai desirability sebesar 0,839 (83,9%). Komposisi formula optimum terdiri atas x mL EKK, y mL EKS, dan z mL EJG. Formula optimum tersebut memperoleh nilai aktivitas antioksidan sebesar 352,59±0,42 µM TE/mL, nilai ALT sebesar 1,7 × 101 CFU/mL, dan skor uji hedonik rating untuk atribut aroma (6,5±1,3), warna (6,7±1,3); rasa (7,0±1,1); dan overall (6,8±1,1). Formula optimum memperbaiki penerimaan sensori dan mempertahankan mutu mikrobiologis, walau sedikit menurunkan aktivitas antioksidan., A functional drink based on Java Tea has been in development since 2007, but along the way, this product has faced problems related to high levels of microorganism, so its processing relies on thermal processes and the use of food additives (FA) to maintain shelf life. This research aims to optimize a new formula using extracts that have passed through microfiltration using the D-optimal mixture design in Response Surface Methodology (RSM) by considering the hedonic sensory and antioxidant activity. A total of 19 formula designs were produced by Design Expert 13.0Ⓡ software as output data from input data in the form of lower-upper limit values of java tea extract (EKK: a-d mL), sappan wood extract (EKS: b-e mL), and ginger extract (EJG: c-f mL). The results of the study showed that the optimum formula has a desirability value of 0,839 (83,9%). The optimum formula composition consists of x mL EKK, y mL EKS, and z mL EJG. The optimum formula obtained values of antioxidant activity value at 352,59±0,42 µM TE/mL, ALT value at 1,7 × 101 CFU/mL, and hedonic rating test scores for aroma (6,5±1,3), color (6,7±1,3); taste (7,0±1,1), and overall (6,8±1,1). The optimum formula improves sensory acceptance and maintains microbiological quality, despite a slight reduction in antioxidant activity.
Keyword: Optimasi formula, Response Surface Methodology (RSM), Design expert, Minuman Fungsional Berbasis Kumis Kucing, Antioksidan DPPH, Rating hedonik, Ranking hedonik, Membran mikrofiltrasi, Mikrofiltrasi

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