Judul: Estimasi Nilai Kerugian Ekonomi Masyarakat Akibat Pencemaran di Sekitar Kawasan Pabrik Bijih Plastik (Studi Kasus: Kelurahan Cirimekar, Kecamatan Cibinong, Kabupaten Bogor).
Abstrak: Aktivitas industri tidak hanya memberikan manfaat tetapi dapat menimbulkan eksternalitas negatif seperti penurunan tingkat kualitas lingkungan. Kelurahan Cirimekar merupakan kelurahan yang terkena eksternalitas negatif akibat pencemaran di sekitar pabrik pengolahan bijih plastik. Penelitian ini bertujuan untuk mengidentifikasi eksternalitas negatif dan persepsi masyarakat terhadap pencemaran serta mengestimasi besarnya nilai kerugian ekonomi yang ditanggung oleh masyakat akibat pencemaran. Penelitian ini menggunakan metode analisis deskriptif, skala likert, cost of illness, loss of earnings dan replacement cost. Hasil dari penelitian ini menunjukkan bahwa masyarakat merasakan pencemaran air tanah, pencemaran udara dan debu, serta kebisingan suara. Hasil dari estimasi total kerugian ekonomi masyarakat diperoleh sebesar Rp 210 792 872/tahun.
Keyword: cost of illness, kerugian ekonomi, loss of earning, pencemaran replacement cost

Judul: Estimasi Nilai Kerugian Ekonomi Masyarakat Akibat Pencemaran Di Sekitar Kawasan Industri Baja (Kelurahan Tegal Ratu, Kecamatan Ciwandan, Kota Cilegon).
Abstrak: Industri baja telah berkembang pesat di Indonesia salah satunya di Kelurahan Tegal Ratu. Penelitian ini dilakukan untuk mengidentifikasi eksternalitas positif dan negatif yang dirasakan masyarakat akibat aktivitas industri baja menggunakan metode analisis deskriptif kualitatif, mengestimasi nilai kerugian ekonomi yang ditanggung oleh masyarakat menggunakan metode cost of illness dan loss of earnings, mengestimasi nilai dana kompensasi yang bersedia diterima oleh masyarakat menggunakan Contingent Valuation Method (CVM), dan mengidentifikasi faktor-faktor yang memengaruhi besarnya kesediaan masyarakat dalam menerima dana kompensasi menggunakan analisis regresi linier berganda. Eksternalitas dirasakan oleh responden di RW 01 dan RW 06 Kelurahan Tegal Ratu, Kecamatan Ciwandan. Hasil yang diperoleh yaitu eksternalitas positif yang terjadi yaitu peningkatan lapangan pekerjaan dan peningkatan usaha mikro. Eksternalitas negatif yang terjadi yaitu pencemaran udara dan debu, kebisingan, serta terganggunya kenyamanan. Rata-rata kerugian setiap rumah tangga akibat pencemaran oleh industri baja yaitu sebesar Rp217 767.48/rumah tangga/tahun. Nilai dugaan rata-rata Willingness to Accept (WTA) responden yaitu sebesar Rp515 769.24/rumah tangga/tahun. Faktor-faktor yang berpengaruh nyata pada besarnya nilai WTA responden adalah usia, tingkat pendidikan, jarak tempat tinggal ke industri, dan dummy udara menyesakkan.
Keyword: eksternalitas, industri baja, kerugian ekonomi, pencemaran udara, Willingness to Accept (WTA)

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