Judul: Analisis KonsumsiPanganPendudukProvinsi DKIJakarta
Abstrak: This study was aimed to assess food consumption of people in DKI Jakarta. The design of this study was cross sectional study. The study which is conduted from analyzing food consumption data of Basic Health Research 2010 collected by a 24-hour recall method. Subject obtained from1626 households with 4964 household members.The result showed that energy consumption average of households stood at 1500 ± 711.48 kkal/cap/day and protein consumption average of households stoodat 50.82 ± 27.1 g/cap/day.Almost all (53.1%) energyadequacy level (TKE) households stood atextreemly leveldeficit category,proteinadequacylevel (TKP) households stood at normal category (47.2%) and desirable dietary pattern (PPH) stood at76.6. Spearman test resultshowed there wasno significant correlation(P>0.05) between maternal education level, number of household memberand food expenditure to householdsTKE and TKP.
Keyword: Bogor Agricultural University (IPB), protein adequacy level (TKP), energy adequacy level (TKE), household, food consumption

Judul: Analysis of The Situation and Determinant Factors  Affecting Food Consumption among Population in DKI Jakarta Province
Abstrak: Penelitian ini bertujuan untuk untuk menganalisis situasi konsumsi pangan penduduk serta faktor determinan wilayah terkait di Provinsi DKI Jakarta. Penelitian ini menggunakan data sekunder dari BPS dan Badan Ketahanan Pangan yang diolah dan dianalisis secara kuantitatif dengan teknik deskriptif. Hasil penelitian ini menunjukkan bahwa rata-rata peningkatan jumlah penduduk sebesar 84.591 jiwa tiap tahunnya. Rata-rata peningkatan RLS sebesar 0,09 tahun dan rata rata peningkatan pengeluaran pangan sebesar Rp 52.267/kap/bulan tiap tahunnya. Rata-rata persentase pengeluaran pangan sebesar 38,65%. Tingkat kecukupan energi (TKE) di Provinsi DKI Jakarta tahun 2013-2020 terendah sebesar 86,2% dan tertinggi sebesar 106,2%. Tingkat kecukupan protein (TKP) terendah sebesar 102,1% dan tertinggi sebesar 126,8%. Skor PPH belum ada yang mencapai skor PPH maksimal tetapi sudah tergolong cukup baik yaitu terendah sebesar 81,7 dan tertinggi sebesar 89. Forecasting jumlah penduduk tahun 2021-2023 menggunakan metode deret geometri. Forecasting RLS, pengeluaran pangan, TKE, TKP, dan skor PPH menggunakan metode moving average. Hasil uji regresi menunjukkan bahwa jumlah penduduk dan pengeluaran pangan tidak berpengaruh secara signifikan terhadap TKE, TKP, dan skor PPH konsumsi pangan (p>0,01). RLS berpengaruh secara signifikan terhadap TKE (p=0,0948) dan skor PPH (p=0,0775), tetapi tidak berpengaruh secara signifikan terhadap kuantitas TKP konsumsi pangan (p>0,01)., This research aims to analyze the population's food consumption situation and related regional determinant factors in DKI Jakarta Province. A secondary data from BPS and Food Security Agency which was processed and analyzed quantitatively using descriptive techniques. The results of this research showed that the average population increase is 84591 people each year. The average increase in RLS is 0.09 years and the average increase in food expenditure is IDR 52267,00/cap/month each year. The energy adequacy level (TKE) was the lowest at 86.2% and the highest at 106.2%. The lowest level of protein adequacy (TKP) was 102.1% and the highest was 126.8%. No PPH score has yet reached the maximum PPH score but it is quite good, the lowest is 81.7 and the highest is 89. Forecasting total population in 2021-2023 uses the geometric series method. Forecasting RLS, food expenditure, TKE, TKP, and PPH scores using the moving average method. The results of the regression test showed that total population and food expenditure did not significantly affect TKE, TKP, and PPH score of food consumption (p>0.01). RLS did significantly affect TKE (p=0.0948) and PPH score (p=0.0775), but did not significantly affect TKP food consumption (p>0.01).
Keyword: economic and sociodemographic factors, energy and protein adequacy  levels, food consumption, pph score

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