Judul: Studi kualitas lingkungan fisika dan kimia perairan Teluk Banten Kabupaten Serang
Abstrak: Perairan Teluk Banten merupakan salah satu wilayah pesisir yang terdapat berbagai aktivitas dan kepentingan disana. Pada kawasan ini terdapat industri pariwisata di pantai Salira dan cagar alam P. Dua, industri dasar dan aneka industri di Bojonegara, sarana pelabuhan perikanan di Karangantu, usaha perikanan tambak, dan beberapa tempat se- bagai daerah penangkapan ikan bagi nelayan setempat. Berbagai kegiatan tersebut telah menghasilkan, limbah yang dibuang ke dalam perairan. Sebagai akibatnya akan mempengaruhi keadaan dan kualitas perairan tersebut. Ber- anjak dari hal tersebut penelitian ini bertujuan untuk me- ngetahui penyebaran horisontal dan vertikal beberapa para- meter fisika dan kimia perairan; dan mengkaji kondisi kualitas perairan dan tingkat pencemaran Teluk Banten. Penelitian dilakukan di perairan Teluk Banten pada bulan Nopember 1991. Parameter fisika yang diamati: suhu, kedalaman, kekeruhan, arus, daya hantar listrik, kecerahan, pasang surut, muatan padatan (tersuspensi dan terlarut). Parameter kimia yang diamati pH, salinitas, oksigen ter- larut, amonia, nitrat, fosfat, silikat, hidrokarbon minyak
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Judul: Water Pollution Index in Banten Bay Waters Based on Several Water Physical Parameters
Abstrak: Aktivitas antropogenik di sekitar kawasan perairan Teluk Banten dapat membawa limbah tercemar ke dalam perairan. Sehingga dapat memengaruhi kualitas perairan. Penelitian status mutu suatu perairan penting dilakukan untuk menduga kondisi pencemaran suatu perairan. Penelitian ini bertujuan menentukan tingkat pencemaran perairan Teluk Banten melalui pendekatan beberapa parameter fisika perairan. Data yang digunakan ialah data primer dan sekunder tahun 2019- 2022 dari enam lokasi penelitian. Parameter yang dianalisis antara lain suhu, kecerahan, kekeruhan, dan padatan tersuspensi (TSS). Hasil analisis yang diperoleh dibandingkan dengan baku mutu berdasarkan PP No 22 Tahun 2021. Berdasarkan hasil pengamatan suhu (25.1-39ᵒC), kecerahan (0.05-10 m), kekeruhan (0.24-380 NTU), TSS (0.4-742 mg/l), menunjukan bahwa sebagian parameter masih tergolong dibawah baku mutu beberapa parameter kurang sesuai dengan baku mutu perairan (antara lain suhu, kecerahan, kekeruhan, TSS). Berdasarkan hasil perhitungan indeks pencemaran perairan Teluk Banten memiliki indeks pencemaran kategori tercemar ringan (1.315-4.490)., Anthropogenic activities around the waters of Banten Bay can bring polluted waste into the waters. So that it can affect water quality. Research on the quality status of a waters is important to do to predict the pollution conditions of a waters. This study aims to determine the pollution level of Banten Bay waters through the approach of several water physics parameters. The data used are primary and secondary data for 2019-2022 from six research locations. Parameters analyzed included temperature, brightness, turbidity, and suspended solids (TSS). The results of the analysis obtained were compared with the quality standards based on PP No. 22 of 2021. Based on the results of observations of temperature (25.1-39ᵒC), brightness (0.05-10 m), turbidity (0.24-380 NTU), TSS (0.4-742 mg/l) , shows that some of the parameters are still below the quality standards, some parameters are not in accordance with the water quality standards (among others temperature, brightness, turbidity, TSS). Based on the results of the calculation of the water pollution index, Banten Bay has a pollution index of the lightly polluted category (1.315-4.490).
Keyword: baku mutu, indeks pencemaran, kualitas air, parameter fisika, teluk banten

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