Judul: Determinants of Capital Structure in Food  and Beverage Companies Listed on the IDX
Abstrak: Industri makanan dan minuman merupakan industri yang memiliki kontribusi yang besar terhadap PDB dan salah satu sub sektor penyerapan tenaga kerja terbesar di Indonesia. Namun kondisi tersebut tidak diikuti oleh semakin meningkatnya struktur modal perusahaan. Struktur modal yang optimal adalah yang meminimalisasi biaya modal perusahaan dan dengan demikian dapat memaksimalkan nilai perusahaan (Horne dan Wachowicz 2008). Objek penelitian ini adalah sub sektor industri makan dan minuman. Data yang digunakan merupakan data sekunder yang diperoleh dari laporan keuangan perusahaan selama periode 2015-2019 serta studi literatur. Metode yang digunakan dalam penelitian adalah analisis regresi data panel dengan profitabilitas, struktur aset, pertumbuhan perusahaan, risiko bisnis sebagai variabel independen serta struktur modal sebagai variabel dependen. Hasil penelitian menunjukkan bahwa struktur aset berpengaruh positif terhadap struktur modal (DAR) sedangkan risiko bisnis berpengaruh negatif signifikan terhadap struktur modal (DER). Variabel profitabilitas, pertumbuhan perusahaan tidak signifikan terhadap struktur modal.
Keyword: asset structure, capital structure, company growth, food and beverage industry, profitability

Judul: Factors Affecting the Capital Structure of Food and Beverage Companies on the Indonesia Stock Exchange
Abstrak: Perusahaan makanan dan minuman merupakan salah satu sektor yang diminati oleh para investor. Kinerja perusahaan yang digambarkan oleh nilai perusahaan dapat memengaruhi keputusan investasi para investor. Faktor yang memengaruhi nilai perusahaan salah satunya adalah struktur modal. Sehingga penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi pemilihan struktur modal perusahaan makanan dan minuman Struktur aktiva, profitabilitas, likuiditas, ukuran perusahaan, pertumbuhan penjualan, serta pertumbuhan perusahaan digunakan sebagai variabel independen, pandemi COVID-19 sebagai variabel dummy dan struktur modal perusahaan sebagai variabel dependen. Data yang digunakan adalah laporan keuangan perusahaan sektor makanan dan minuman selama tahun 2018 sampai 2021 dan dianalisis menggunakan regresi data panel. Pengambilan sampel penelitian dilakukan dengan metode purposive sampling dan didapati sepuluh perusahaan sektor makanan dan minuman. Hasil penelitian menunjukkan bahwa enam dari sepuluh perusahaan yang diteliti memiliki struktur modal yang konsisten dengan proporsi ekuitas lebih besar daripada hutang. Variabel yang memengaruhi struktur modal hanya struktur aktiva, profitabilitas, likuiditas, dan ukuran perusahaan.
Keyword: Regresi Data Panel, Capital Structure, Food and Beverages, Pandemic, Panel Data Regression

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