Judul: Status Mutu Air Kali Angke di Bogor, Tangerang, dan Jakarta
Abstrak: Masuknya bahan-bahan pencemar ke dalam badan air sungai menyebabkan turunnya kualitas air sungai. Salah satu sungai yang diduga telah mengalami pencemaran adalah Kali Angke. Penelitian ini bertujuan untuk menentukan status mutu air dan tingkat pencemaran Kali Angke menggunakan metode Indeks Pencemaran (IP) dan Indeks Canadian Council of Minister of The Environment (CCME). Pengambilan data kualitas air dilakukan pada lima segmen sebanyak 21 titik pengambilan contoh pada tanggal 2–4 Oktober 2017. Data sekunder kualitas air berasal dari Dinas Lingkungan Hidup Kota Bogor, Kabupaten Bogor, Tangerang Selatan, Tangerang, dan Jakarta Barat. Parameter kualitas air meliputi parameter fisika (suhu, TSS, dan TDS), parameter kimia (DO, BOD, COD, NO2--N, NO3--N, pH, total posfat, Zn, minyak lemak, Hg, dan Cu), dan parameter biologi (fecal coliform dan total coliform). Indeks kualitas air CCME lebih mewakili kondisi perairan daripada Indeks Pencemaran. Tingkat pencemaran semakin meningkat dari hulu ke hilir dan dari tahun 2014 sampai 2016, kemudian menurun pada tahun 2017. Status mutu Kali Angke tergolong cemar ringan menurut IP dan tergolong buruk menurut Indeks CCME.
Keyword: indeks CCME, indeks pencemaran, kualitas air

Judul: Penetapan Indeks Mutu Air Sungai Cikeas, Sungai Cileungsi, dan Kali Bekasi Berkaitan Tata Guna Lahan
Abstrak: Penggunaan lahan pada daerah aliran Sungai Cileungsi, Cikeas, dan Kali Bekasi yang sangat beragam dapat memberikan pengaruh terhadap kualitas air di ketiga perairan tersebut. Penelitian ini bertujuan untuk menilai status mutu kualitas air sungai Cileungsi, Cikeas, dan Kali Bekasi dalam kaitannya dengan penggunaan lahan. Penelitian dilakukan di Sungai Cileungsi, Sungai Cikeas, dan Kali Bekasi dengan 11 titik lokasi pengamatan. Data spasial yang digunakan dalam penentuan tata guna lahan beserta luasnya berasal dari pengolahan citra satelit dan DEM (digital elevation model). Status kualitas air Sungai Cikeas, Cileungsi, dan Kali Bekasi dalam kategori kelas II berdasarkan metode Indeks Pencemaran dikategorikan tercemar ringan. Hasil analisis menunjukkan luasan tata guna lahan tidak berhubungan secara signifikan dengan parameter DO, BOD, COD, dan amonia kecuali parameter TSS. Kualitas air Sungai Cikeas, Sungai Cileungsi dan Kali Bekasi cenderung menurun ke arah hilir terutama pada segmen sungai yang didominasi lahan terbangun., Land use patterns in the watershed of Cileungsi, Cikeas, and Kali Bekasi Rivers might have deleterious impacts on the water quality of those rivers. In correlation to the land utilization, a study was conducted for the purpose to evaluating water quality status of Cileungsi, Cikeas, and Kali Bekasi Rivers. The study was carried out by sampling of waters at the aforementioned rivers at 11 sampling points. The spatial data used for land use patterns determination and their areas were processed from satellite image data and DEM (Digital Elevation Model). Using Pollution Index Method, the water quality status of Cileungsi, Cikeas and Kali Bekasi Rivers were classified as Category II indicating that the rivers were slightly polluted. Except for TSS, results of the analyses demonstrated that levels of DO, BOD, COD, and ammonia were not significantly correlated to land use patterns. From upstream to downstream, the water quality of Cileungsi, Cikeas, and Kali Bekasi Rivers were decreasing particularly at the river segment dominated by built-up land.
Keyword: land use, pollution index, water quality

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