Judul: Pengaruh penurunan kadar air dengan menggunakan "Dehumidifier" terhadap mutu madu yang dihasilkan
Abstrak: Kadar air merupakan salah satu faktor yang sangat menentukan kua- litas madu karena mempunyai pengaruh terhadap masa simpannya. Jika madu memiliki kadar air yang tinggi maka akan sangat mudah mengalami fermentasi. Kelembaban udara relatif yang tinggi di Indonesia menyebab- kan madu-madu yang dihasilkan juga memiliki kadar air yang tinggi. Untuk itu perlu dilakukan upaya penurunan kadar air. Proses pemanasan memang dapat menurunkan kadar air madu, na- mun proses ini sangat tidak dianjurkan karena akan menurunkan mutu madu akibat kenaikan kandungan HMF, kerusakan enzim diastase dan juga hilangnya aroma madu. Penggunaan vacuum dehidrator dapat memperke- cil kenaikan HMF namun hasilnya masih belum memuaskan karena tetap terjadi kehilangan komponen aroma yang menyebabkan aroma madu ber- bada dengan aslinya. Penelitian ini bertujuan mempelajari penurunan kadar air madu de- ngan menggunakan "dehumidifier" serta melihat pengaruh penggunaan "dehumidifier" terhadap kualitas madu...
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Judul: Pengaruh Metode Penurunan Kadar Air, Suhu dan  Lama Penyimpanan terhadap Kualitas Madu Randu
Abstrak: Madu Indonesia pada umumnya mengandung kadar air yang tinggi sehingga rentan terhadap fermentasi. Salah satu cara pencegahan fermentasi adalah menurunkan kadar air madu menjadi sekitar 17- 18%. Pusat Perlebahan Nasional (Pusbahnas) Parungpanjang menurunkan kadar air madu melalui pemanasan tidak langsung (suhu sekitar 57 "C) dengan alat dehidrator vakum (metode dehidrasi) dan melalui penguapan dengan alat dehumidifier (metode dkhumidifikasi). Penelitian ini membandingkan kualitas madu yang telah mengalami pikes dehidrasi dengan yang mengalami proses dehumidifikasi. Selain itu, penelitian ini juga bertujuan mencari kondisi penyimpanan yang paling cocok bagi madu yang berbeda metode penurunan kadar airnya, karena kondisi penyimpanan mempengaruhi kualitas madu. Masyarakat Indonesia biasanya menyimpan madu di ruangan terbuka atau di refrigerator selama lebih dari enam bulan.
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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