Judul: Contribution of Processed Potato Consumption to the Intake of Carbohydrates and Fats and the Association with Body Mass Index
Abstrak: Peningkatan penduduk menimbulkan permasalahan sensitif terutama tentang pangan. Permasalahan ini dapat dihindari jika masyarakat berusaha untuk membiasakan mengonsumsi makanan pokok non-beras yang berpotensial sebagai pangan lokal. Kentang merupakan umbi yang banyak dikonsumsi oleh masyarakat di dunia sebagai makanan pokok dan sumber karbohidrat. Saat ini, Penyakit Tidak Menular (PTM) menjadi masalah kesehatan yang diperhatikan, baik nasional maupun global. Penyebab risiko PTM salah satunya karena banyak mengonsumsi lemak yang terdapat pada produk gorengan. Data primer penelitian ini berasal dari survei online dan in-depth interview menggunakan perhitungan full population dan eater only. Survei berupa survei konsumsi pangan keluarga sedangkan wawancara mendalam dilakukan secara perseorangan. Penelitian ini menggunakan metode survei semi quantitatif food frequency questionnaire (SQ-FFQ) dan selanjutnya diuji korelasi chi-square dengan ketepatan 95% dan ɑ 0,05. Rata-rata konsumsi nasional kentang segar sebesar 7,65 – 36,50 kg/kapita/tahun. Hasil ini lebih tinggi dibandingkan dengan data Susenas yaitu 2,547 kg/kapita/tahun. Konsumsi kentang responden dipengaruhi secara signifikan oleh lokasi tempat tinggal. Kontribusi karbohidrat dari penelitian ini sebesar 1,17% - 8,65% dari 325 g/kapita/hari. Kontribusi asupan lemak dari penelitian ini sebesar 0,98% - 12,57% dari 67 g/kapita/hari. IMT responden penelitian ini termasuk dalam kategori berat normal. Asupan lemak dan IMT memberikan efek jangka panjang terhadap penyakit tidak menular.
Keyword: potato, body mass index, carbohydrate contribution, fat contribution, local food

Judul: Consumption Patterns of Processed Cassava, Influencing Factors, and Its Relation to Body Mass Index in Indonesia
Abstrak: Diversifikasi pangan sumber karbohidrat di Indonesia masih rendah. Oleh karena itu, pemerintah mengeluarkan roadmap program diversifikasi pangan sumber karbohidrat yang terdiri dari enam komoditas yang salah satunya adalah singkong. Penelitian ini bertujuan memetakan dan mendeskripsikan pola konsumsi olahan singkong, menganalisis besaran konsumsi dan kontribusinya terhadap asupan karbohidrat dan lemak, serta mengidentifikasi faktor-faktor yang memengaruhinya. Metode pengukuran konsumsi olahan singkong menggunakan semi food frequency questionnaire yang ditanyakan melalui survei dan in-depth interview secara daring. Responden penelitian ini terdiri dari 847 responden keluarga pada survei dan 123 responden individu pada in-depth interview. Tahapan penelitian terdiri dari penyusunan kuesioner survei dan in-depth interview, uji coba kuesioner, pelaksanaan survei dan in-depth interview, serta pengolahan dan analisis data. Konsumsi olahan singkong di Indonesia sebesar 11,85 g singkong segar/kapita/hari dengan kontribusi asupan karbohidrat sebesar 1,36% dan lemak sebesar 0,87% dari angka kecukupan gizi orang dewasa. Singkong lebih banyak dikonsumsi sebagai pangan selingan daripada sebagai pangan pokok. Singkong rebus, singkong goreng, dan keripik singkong merupakan olahan singkong yang paling banyak dikonsumsi. Konsumsi olahan singkong dipengaruhi oleh faktor usia sehingga cenderung dikonsumsi oleh segmentasi usia tertentu yaitu paruh baya hingga lansia. Sedangkan nilai indeks massa tubuh tidak dipengaruhi oleh konsumsi olahan singkong., Food diversification of carbohydrate sources in Indonesia is still low. Therefore, the government issued a roadmap for a food diversification program from carbohydrate sources consisting of six commodities and one of them is cassava. This study aims to map and describe the consumption pattern of processed cassava, analyze the amount of consumption and its contribution for carbohydrate and fat intake, and identify the factors that influence it. The measuring method for consumption of processed cassava uses a semi-food frequency questionnaire which is asked through online surveys and in-depth interviews. This study consists of 847 family respondents in survey and 123 individual respondents in-depth interviews. This research consists of prepare survey questionnaires and in-depth interviews, tests for questionnaires, surveys and in-depth interviews, data processing and analysis. Total consumption of processed cassava in Indonesia is 11,85 g of fresh cassava/capita/day with 1.36% contribution of carbohydrate and 0,87% fat intake of the adult nutritional adequacy rate. Cassava is consumed more as a snack than as a staple food. Boiled cassava, fried cassava, and cassava chips are the most widely consumed processed cassava. Consumption of processed cassava is influenced by age, so it tends to be consumed by a certain age segment from middle age to the elderly. Meanwhile, the body mass index value was not influenced by the consumption of the processed cassava.
Keyword: body mass index, carbohydrate, consumption pattern, cassava

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