Judul: Karakteristik Riwayat Kehamilan Ibu dan Pola Asuh Makan pada Anak Batita Stunting dan Normal di Desa Sindangkerta Kabupaten Bandung Barat
Abstrak: Penelitian ini bertujuan menganalisis riwayat kehamilan ibu dan pola asuh makan batita di Desa Sindangkerta, Kabupaten Bandung Barat. Desain penelitian adalah deskriptif cross-sectional. Data yang digunakan dalam penelitian ini adalah data sekunder sebagai bagian dari data penelitian BKKBN dan tim yang berjudul “Studi Karakteristik Keluarga, Pola Asuh, dan Ketahanan Keluarga terhadap Kejadian Stunting” pada September 2018. Analisis dan interpretasi data dilaksanakan Oktober-Desember 2018 di Institut Pertanian Bogor. Sebanyak 51 batita stunting dan 45 batita normal berusia 6-36 bulan dipilih sebagai subyek penelitian menggunakan metode purposive sampling. Data yang dikumpulkan meliputi karakteristik subyek, karakteristik keluarga, riwayat kehamilan, dan pola asuh makan. Hasil penelitian menunjukkan tidak terdapat perbedaan yang nyata pada karakteristik subyek, katakteristik keluarga, dan riwayat kehamilan ibu antara batita stunting dan batita normal (p>0.05). Namun, rata-rata panjang lahir batita stunting kurang dari batas normal (47.37 cm) dibandingkan batita normal (48.20 cm). Pola asuh makan antara batita stunting dan batita normal berbeda nyata (p<0.05). Pola asuh makan yang berhubungan secara signifikan dengan kejadian stunting adalah riwayat pemberian ASI eksklusif (p<0.05), yaitu sebagian besar batita normal (73.3%) diberi ASI eksklusif sedangkan batita stunting tidak diberi ASI eksklusif (54.9%)
Keyword: batita, pola asuh makan, riwayat kehamilan, stunting

Judul: Hubungan Pola Asuh dan Pengetahuan Ibu dengan Kejadian Stunting Bayi Usia 6-23 Bulan di Desa Parappe Sulawesi Barat
Abstrak: Masalah stunting pada balita masih cukup serius, angka nasional menunjukkan prevalensi stunting sebesar 37.2% (Riskesdas 2013), yang berarti terjadi peningkatan dibandingkan tahun 2010 (35.6%) dan tahun 2007 (36.8%). Sulawesi Barat menduduki peringkat kedua prevalensi Stunting dengan kategori pendek dan sangat pendek setelah Aceh (37.9%) pada anak umur 0-23 bulan sebesar 37.1% angka ini diatas angka nasional sebesar 29.9% (Riskesdas 2018). Penelitian ini bertujuan untuk menganalisis hubungan pola asuh dan pengetahuan Ibu dengan kejadian stunting bayi usia 6-23 bulan di Desa Parappe Sulawesi Barat. Penelitian ini menggunakan desain cross sectional dengan subjek sebesar 77 bayi. Pengumpulan data pola asuh dan pengetahuan melalui wawancara menggunakan kuesioner. Status gizi subjek ditentukan berdasarkan indeks PB/U. Hasil penelitian menunjukkan terdapat hubungan yang signifikan (p<0.05) antara pendapatan keluarga dengan kejadian stunting. Besar keluarga, berat badan lahir, pendidikan, pengetahuan, pola asuh, dan pemberian ASI tidak berhubungan dengan kejadian stunting.
Keyword: kejadian stunting, pengetahuan, pola asuh

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