Judul: Cemaran Bakteri Dan Koliform Pada Layar Telepon Genggam Mahasiswa Program Sarjana Fakultas Kedokteran Hewan Institut Pertanian Bogor.
Abstrak: Penelitian ini bertujuan untuk menguji tingkat cemaran dan mengidentifikasi faktor risiko yang berpengaruh terhadap jumlah bakteri dan koliform pada layar telepon genggam mahasiswa Program Sarjana Fakultas Kedokteran Hewan Institut Pertanian Bogor. Pengambilan sampel dilakukan dengan metode acak sederhana dengan jumlah sampel sebesar 224 orang. Penelitian ini merupakan kajian cross-sectional dan data diperoleh melalui uji swab terhadap jumlah bakteri dan koliform pada permukaan telepon genggam dan wawancara responden (mahasiswa) menggunakan kuesioner. Hasil penelitian menunjukkan bahwa rata-rata jumlah cemaran bakteri dan koliform berdasarkan layar telepon genggam pada semua sampel secara berurutan adalah 876 cfu/cm2 (minimum 5 cfu/cm2; maksimum 50 000 cfu/cm2) dan 27 cfu/cm2 (minimum 0 cfu/cm2; maksimum 1 800 cfu/cm2). Rata-rata jumlah cemaran bakteri berdasarkan layar telepon genggam pada responden laki-laki nyata lebih tinggi (p<0.05) dibandingkan dengan responden perempuan. Tipe layar nontouchscreen nyata lebih tinggi (p<0.05) dibandingkan dengan tipe layar touchscreen dalam hal rata-rata jumlah cemaran bakteri. Frekuensi mencuci muka 2 kali sehari (OR = 2.17) menjadi faktor risiko yang memengaruhi cemaran bakteri. Lebih lanjut, faktor risiko yang memengaruhi cemaran koliform adalah jambang pada laki-laki (OR = 2.99), frekuensi mencuci tangan 2 kali sehari (OR = 3.09), tipe telepon genggam non-touchscreen (OR = 2.11), dan bahan mencuci tangan dengan air dan sabun biasa (OR = 3.26).
Keyword: cemaran bakteri, koliform, mahasiswa, swab, telepon genggam

Judul: Total Mikrob dan Koliform pada Kran Air Kamar Mandi, Wastafel, dan Musholla di Fakultas Kedokteran Hewan Institut Pertanian Bogor.
Abstrak: Cemaran mikrob pada kran air berasal dari udara, paparan kulit tangan, dan sisa air. Selain itu terdapat beberapa faktor yang dapat meningkatkan tingkat cemaran mikrob. Penelitian ini bertujuan mengidentifikasi total mikrob dan koliform pada kran air kamar mandi, wastafel, dan musholla di Fakultas Kedokteran Hewan Institut Pertanian Bogor. Digunakan 126 sampel swab kran air yang terdiri dari 63 sampel swab kran air sebelum pemakaian dan 63 sampel swab kran air sesudah pemakaian. Dilakukan pemeriksaan sampel terhadap total plate counts dan koliform menggunakan metode hitung cawan. Hasil penelitian menunjukkan bahwa rataan total mikrob dan koliform pada kran air kamar mandi, wastafel, dan musholla sesudah pemakaian 535 943.40 ± 1 607 830.19 cfu dan 1 394.91 ± 577.80 cfu memiliki jumlah lebih tinggi dibandingkan dengan sebelum pemakaian 345 724.48 ± 254 931.27 cfu dan 1 210.06 ± 597.09 cfu, tetapi tidak ada perbedaan yang nyata (p>0.05).
Keyword: koliform, swab kran air, total mikrob

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