Judul: Telaah ekologis komunitas makrozoobenthos di perairan ekosistem hutan mangrove Mayangan, Pamanukan, Subang
Abstrak: Perairan hutan mangrove merupakan salah satu bentuk ekosistem di kawasan estuaria yang sangat khas dan sensitif terhadap perubahan lingkungan. Selain terdiri dari vegetasi mangrove, perairan ekosistem hutan mangrove tersusun atas spesies unik dari jenis benthos, plankton, alga, ikan maupun invertebrata lainnya. Degradasi hutan mangrove akibat penebangan untuk areal tambak maupun untuk pembukaan lokasi wisata serta adanya abrasi yang merusak vegetasi mangrove dapat mempengaruhi kondisi fisika, kimia dan biologi yang ada dalam ekosistem, salah satunya adalah benthos. Perubahan perairan ekosistem hutan mangrove dapat menimbulkan perubahan struktur komunitas benthos. Penelitian ini bertujuan untuk mengetahui struktur komunitas makrozoobenthos serta interaksinya dengan karakteristik habitat pada perairan ekosistem hutan mangrove Mayangan, Pamanukan, Subang. Hasil penelitian ini diharapkan dapat merepresentasikan kualitas lingkungan perairan hutan mangrove dengan mengacu pada kualitas biologisnya (makrozoobenthos). Penelitian dilaksanakan pada tangggal 12-13 Januari dan 9-10 Februari 2002, di perairan hutan mangrove Mayangan, Pamanukan, Subang. Penelitian dilakukan pada 6 stasiun yang terbagi ke dalam dua daerah. Stasiun I, II dan III berada di tepi pantai, Stasiun IV, V dan VI berada menjauh dari pantai ke arah daratan. Parameter fisika-kimia perairan yang terukur di lokasi penelitian kondisinya masih cukup baik untuk pertumbuhan dan perkembangan makrozoobenthos. Suhu berkisar antara 28°-32° C, salinitas 5-2400, pH 7,5-8, oksigen terlarut 3,0-6,2 mg/l, kedalaman 20-95 cm, kecerahan 58-100%, C-organik 0,32-4,28% dan bahan organik 0,55-7,38%. Analisis kondisi fisika-kimia perairan dengan Indeks Kesamaan Canberra menunjukkan keadaannya relatif homogen dengan tingkat kesamaan di atas 60%, ini berarti bahwa kondisi habitat yang dikelompokkan berdasarkan parameter fisika-kimianya cenderung identik atau sama.
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Judul: Macrozoobenthic Community Structure in the Mangrove Aquatic Ecosystem of Pejarakan Village, Bali
Abstrak: Struktur komunitas makrozoobentos yang menurun biasanya menjadi indikator adanya gangguan ekosistem di perairan tersebut, hal ini disebabkan makrozoobentos mobilitas rendah serta siklus hidup relatif panjang, sehingga mampu merespon dengan segera gangguan di lingkungan secara kontinyu. Penelitian ini bertujuan menganalisis struktur komunitas makrozoobentos, yang mencakup komposisi jenis, kepadatan, indeks keanekaragaman, indeks keseragaman, dan indeks dominansi pada lingkungan perairan mangrove dan sekitarnya di Desa Pejarakan, Bali. Penelitian ini dilakukan pada bulan September 2022, Januari 2023, September 2023 dan Januari 2024 dengan enam titik stasiun pengamatan, yang mewakili inlet tambak, outlet tambak dan area mangrove. Sampel bentos diambil menggunakan Van Veen Grab serta dilakukan pengukuran kualitas air secara insitu. Data dianalisis menggunakan struktur komunitas, PCA, indeks similaritas Bray-Curtis, serta AMBI dan M-AMBI. Makrozoobentos di lokasi penelitian tersusun atas lima filum. Kepadatan tertinggi pada Januari 2024 yaitu 289 ind/m2. Indeks keanekaragaman (H’) rendah-sedang (1,99 3,17), keseragaman (E) tinggi (0,69-0,96), dominanasi (C) rendah-sedang (0,13-0,38). Analisis PCA menunjukkan persebaran makrozoobentos dipengaruhi oleh parameter kekeruhan dan TSS. Analisis AMBI menunjukkan dominan makrozoobentos bersifat umum dengan nilai AMBI berkisar 0,62-1,29., Decreased macrozoobenthic community structure is usually an indicator of ecosystem disturbance in these waters, this is due to macrozoobenthic low mobility and a relatively longlife cycle, so that it is able to respond immediately to continuous disturbances in the environment. This research aims to analyze macrozoobenthic community structure, which includes genera composition, density, diversity index, evenness index, and dominance index in the mangrove ecosystem waters and its surroundings in Pejarakan Village, Bali. This research was conducted in September 2022, January 2023, September 2023 and January 2024 with six observation stations representing shrimp pond inlet, outlet, and mangrove area. Benthos samples were taken using a VanVeen Grab and water quality measurements were carried out in-situ. Data were analyzed using community structure, PCA, Bray-Curtis similarity index, and AMBI and M-AMBI. The macrozoobenthos at the research location is composed of five phyla. The highest density will be in January 2024, (289 ind/m2). Diversity index (H') is low-medium (1.99-3.17), uniformity (E) is high (0,69-0,96), dominance (C) is low medium (0,13-0,38). PCA analysis shows that the distribution of macrozoobenthos is influenced by turbidity and TSS parameters. AMBI analysis shows that the dominant macrozoobenthos is general with AMBI values ranging from 0,62-1,29.
Keyword: struktur komunitas, makrozoobentos, Desa Pejarakan, community structure, macrozoobenthos, Pejarakan Village

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