Judul: Pengaruh Budaya Organisasi dan Motivasi Kerja terhadap Employee Engagement pada Korporat PT Pertamina Bina Medika IHC.
Abstrak: Saat ini perusahaan dihadapkan dengan permasalahan employee engagement. Salah satu cara yang dapat dilakukan ialah penerapan budaya organisasi yang kuat dalam perusahaan untuk meningkatkan employee engagement. Pertamedika IHC melakukan perubahan budaya perusahaan yang diharapkan memacu motivasi kerja terhadap employee engagement pada karyawan. Korporat Pertamedika IHC sebagai induk perusahaan memiliki peran yang besar dalam penerapan budaya perusahaan, karena menjadi contoh bagi unit usaha. Penelitian ini bertujuan untuk mengetahui persepsi karyawan serta menganalisis pengaruh mengenai budaya organisasi, motivasi kerja terhadap employee engagement. Data yang digunakan dalam penelitian ini adalah data primer dan data sekunder. Populasi dalam penelitian berjumlah 79 karyawan dengan sampel yang digunakan sebanyak 68 responden. Metode analisis dalam penelitian adalah analisis deskriptif dan analisis regresi linier berganda. Hasil penelitian ini menunjukkan bahwa budaya organisasi dan motivasi kerja berpengaruh signifikan secara simultan terhadap employee engagement. Secara parsial, budaya organisasi tidak memiliki pengaruh terhadap employee engagement sedangkan motivasi kerja berpengaruh terhadap employee engagement.
Keyword: budaya organisasi, employee engagement, motivasi kerja

Judul: Analisis pengaruh budaya organisasi dan employee engagement terhadap kinerja tenaga pendidikan dan tenaga kependidikan Departemen Manajemen FEM IPB
Abstrak: Departemen Manajemen adalah salah satu pengelola program studi yang terdapat di Fakultas Ekonomi dan Manajemen Institut Pertanian Bogor. Tujuan penelitian ini adalah mengidentifikasi persepsi karyawan terhadap budaya organisasi, employee engagement dan kinerja, dan menganalisis pengaruh budaya organisasi dan employee engagement terhadap kinerja tenaga pendidik dan tenaga kependidikan departemen manajemen. Penelitian ini menggunakan metode analisis deskriptif, Structural Equation Modelling dengan metode Partial Least Square (SEM-PLS). Berdasarkan hasil analisis deskriptif diperoleh bahwa tenaga pendidik memiliki mean skor yang lebih tinggi dibandingkan dengan tenaga kependidikan, artinya tingkat pengaruh budaya organisasi dan employee engagement tenaga pendidik terhadap kinerja lebih berpengaruh. Untuk hasil uji PLS, budaya organisasi memiliki pengaruh positif dan nyata terhadap pembentukan employee engagement dan budaya organisasi berpengaruh postif dan nyata terhadap peningkatan kinerja. Sementara employee engagement tidak berpengaruh positif dan nyata terhadap peningkatan kinerja tenaga pendidik dan tenaga kependidikan di departemen manajemen FEM IPB.
Keyword: employee engagement,organizational behavior, performance appraisal.

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