Judul: Characteristic, Source and Environmental Risk of Polycylclic Aromatic Hydrocarbons (PAHs) in Cilincing Waters, Jakarta Bay
Abstrak: Senyawa PAH merupakan kontaminan organik yang bersifat persisten, toksik, karsinogenik, dan mutagenik terhadap lingkungan laut. Senyawa PAH dapat diidentifikasi melalui komponen abiotik dan biotik untuk menentukan tingkat kosentrasinya. Komponen abiotik yang biasa digunakan untuk monitoring kualitas perairan yaitu air laut, padatan tersuspensi dan sedimen, sedangkan untuk komponen biotik umumnya menggunakan biota bentik yang menetap di ekosistem perairan. Biota bentik yang umum digunakan yaitu dari jenis bivalvia, seperti kerang hijau. Kerang hijau telah lama digunakan sebagai bioindikator kontaminan organik di perairan laut seperti PAH. Oleh karena itu, penelitian ini bertujuan untuk menentukan kecenderungan nilai konsentrasi PAH dalam kurun waktu tahun 2007-2019, menduga sumber dan mekanisme masuknya PAH ke perairan, melakukan penilaian terhadap risiko lingkungan PAH yang mungkin terjadi di Perairan Cilincing, Teluk Jakarta. Sampel air laut, TSS, sedimen, dan kerang hijau diambil di Perairan Cilincing dan disimpan dalam kontainer es dengan suhu 4oC, untuk selanjutnya dianalisis di laboratorium. Semua jenis sampel kemudian diekstraksi dan dilanjutkan dengan fraksinasi dengan pelarut n-pentana: diklorometana, kemudian hasilnya diinjeksikan di alat kromatografi gas-spektrometer massa (GCMS). Studi pustaka digunakan untuk melihat perbandingan tingkat konsentrasi PAH pada penelitian terdahulu. Hasil penelitian menunjukkan nilai konsentrasi PAH yang fluktuatif dalam rentang periode waktu tahun 2007-2019 di Perairan Cilincing, sehingga masih belum dapat ditentukan kecenderunganya. Sumber PAH di perairan ini berasal dari kombinasi hasil proses pirogenik dan petrogenik, yang diindikasikan berasal dari buangan emisi mesin kendaraan bermotor dan petroleum. Berdasarkan sumber PAH yang diidentifikasi maka dapat diduga bahwa PAH diperairan ini berasal dari input aliran sungai dan deposisi atmosfer, kemudian tenggelam, mengendap dan terakumulasi dalam sedimen. Sementara itu, risiko dari adanya PAH di Perairan Cilincing ini belum memberikan efek buruk secara biologis terhadap organisme yang hidup perairan ini. Pada biota kerang hijau, risiko keamanan pangan terhadap konsumsi biota ini berdasarkan kandungan senyawa karsinogenik, nilainya masih di bawah nilai ambang batas yang diperbolehkan. Namun demikian, diperlukan kehati-hatian dan kewaspadaan dalam pemanfaatan biota kerang hijau ini sebagai sumber daya konsumsi dari jenis makanan laut oleh masyarakat karena adanya kandungan senyawa karsinogenik.
Keyword: Cilincing, Polycyclic Aromatic Hydrocarbon (PAH), Source and Risk

Judul: Karakteristik Senyawa Polychlorinated Biphenyls (PCBs) pada Air dan Sedimen Muara Sungai Ciliwung, DKI Jakarta
Abstrak: Polychlorinated biphenyls (PCBs) is a compound that toxic, bio accumulative, persistent, and widely used in industrial activities. Ciliwung downstream in DKI Jakarta is near the industrial and residence area, so that potentially contaminated by PCBs waste. This research aims to identify the characteristic and concentration of PCBs compound in water and sediment at Ciliwung Downstream. The water samples were collected from Condet, pintu air Manggarai, Gunung Sahari, and Muara Angke, also sediment sample was collected from Muara Angke. The samples were extracted by n-hexane solution, then were analyzed by Gas Chromatography-Mass Spectrophotometry (GC-MS). The result showed that total PCBs concentration in water from Ciliwung downstream is between limit of detection (<BD) to 459.858 ppb, and 372.442 ppb in sediment. The water samples from Condet and pintu air Manggarai are dominated by high chlorination of PCBs, whereas the water sample from Gunung Sahari and sediment from Muara Angke are dominated by low chlorination of PCBs.
Keyword: water, sediment, polychlorinated biphenyls

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