Judul: Household Resilience for Food Security during The Covid-19 Pandemic
Abstrak: Pandemi Covid-19 berdampak pada sektor pangan rumah tangga komunitas adat, terutama dalam hal akses pangan dan ketersediaan pangan. Tujuan penelitian ini adalah mengidentifikasi bagaimana resiliensi dalam ketahanan pangan pada komunitas adat pada masa pandemi Covid-19. Penelitian ini menggunakan metode survei melalui kuesioner dan didukung penjelasan kualitatif melalui wawancara mendalam dan observasi lapang. Teknik pemilihan responden dilakukan secara cluster random sampling kepada 60 rumah tangga pada komunitas adat di Kampung Adat Cireundeu dan informan ditentukan dengan menggunakan teknik purposive. Hasil penelitian ini menunjukkan bahwa rumah tangga pada komunitas adat tergolong resilien dalam hal pangan. Faktor-faktor yang memengaruhi resiliensi dalam ketahanan pangan rumah tangga adalah ada dan besarnya cadangan pangan yang disimpan oleh masing-masing rumah tangga. Cadangan pangan tersebut diperoleh dari hasil panen pada lahan pertanian yang terdapat di wilayah kampung adat dan diproduksi oleh masing-masing rumah tangga., The Covid-19 pandemic has had an impact on the food sector of indigenous peoples' households, especially in terms of food access and food availability. The purpose of this study is to identify resilience in food security in indigenous communities during the Covid-19 pandemic. This research used a survey method through a questionnaire and was supported by qualitative explanations through in- depth interviews and field observations. The respondent selection technique was carried out by cluster random sampling to 60 households in indigenous communities in the Cireundeu Traditional Village and informants were determined using purposive techniques. The results of this study show that households in indigenous communities are classified as resilient in terms of food. The factors that affect resilience in household food security are the presence and magnitude of food reserves stored by each household. These food reserves are obtained from crops on agricultural land found in traditional village areas and produced by each household.
Keyword: covid-19 pandemic, food security, indigenous communities

Judul: Resiliensi Komunitas Miskin di Pedesaan Menghadapi Pandemi Covid-19
Abstrak: Pandemi Covid-19 yang terjadi sejak awal tahun 2020 sampai saat ini memberikan dampak buruk bagi kesehatan, sosial, dan ekonomi. Menghadapi gangguan akibat bencana ini komunitas harus mampu beradaptasi agar dapat bertahan hidup, terutama komunitas miskin yang merupakan kelompok rentan. Komunitas miskin merupakan komunitas yang tidak berdaya serta memiliki keterbatasan ekonomi. Ancaman besar akibat pandemi ini ialah akan meningkatkan kemiskinan akibat guncangan ekonomi dan pembatasan aktivitas. Tujuan penelitian ini akan mengkaji ketahanan komunitas miskin di pedesaan menghadapi pandemi yang ditinjau melalui kapasitas adaptasi dan tindakan kolektif komunitas. Metode yang digunakan adalah survei menggunakan instrumen kuesioner, observasi, dan wawancara mendalam. Pemilihan responden menggunakan teknik sample random sampling dan didapatkan sample sebanyak 40 responden. Hasil penelitian menunjukkan bahwa ketahanan komunitas miskin di pedesaan tidak terbangun karena komunitas tidak mampu beradaptasi dalam menghadapi pandemi dan tidak adanya upaya kolektif yang dilakukan untuk mengatasi permasalahan bersama selama pandemi. Komunitas cenderung mengandalkan bantuan dan dukungan eksternal dari pihak luar komunitas untuk bertahan menghadapi pandemi Covid-19., The Covid-19 pandemic that has occurred since the beginning of 2020 until now has had a negative impact on health, social and economy. Facing the disruption caused by this disaster, the community must be able to adapt in order to survive, especially the poor who are a vulnerable group. The poor community is a community that is powerless and has economic limitations. The big threat due to this pandemic is to increase poverty due to economic shocks and restrictions on activities. The purpose of this research is to examine the resilience of rural poor communities to face pandemics in terms of adaptive capacity and community collective action. The method used is a survey using a questionnaire instrument, observation, and indepth interviews. Selection of respondents using a random sampling technique and obtained a sample of 40 respondents. The results showed that the resilience of poor rural communities was not developed because the community was unable to adapt to the pandemic and there was no collective effort to overcome common problems during the pandemic. Communities tend to rely on external assistance and support from outside the community to survive the Covid-19 pandemic
Keyword: adaptation capacity, collective action, community resilience

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