Judul: Sebaran Terumbu Karang di Perairan Pulau Menjangan Taman Nasional Bali Barat dan Biorock Pemuteran, Bali
Abstrak: Penelitian ini bertujuan mengetahui sebaran genus karang keras, bentuk pertumbuhan (lifeform) karang keras, komposisi komunitas bentik serta persentase tutupan karang keras di perairan Pulau Menjangan dan Biorock Pemuteran, Bali. Pengambilan data dilakukan pada bulan Juni 2016 menggunakan metode Point Intercept Transect (PIT) dengan panjang setiap transeknya 50 meter dan dilakukan sebanyak tiga kali pengulangan yang dibentangkan sejajar garis pantai. Komposisi komunitas bentik di kedua lokasi penelitian didominasi oleh dead coral with algae (Perairan Pulau Menjangan 51%, Biorock Pemuteran 48.9%). Diikuti oleh persentase abiotic (18%), hard coral (16%), soft coral (10%), dead coral (4.5%) dan other fauna (0.5%) di perairan Pulau Menjangan. Sedangkan di Biorock Pemuteran persentase abiotic (35%), hard coral (12%), dead coral (4%) dan other fauna (0.1%). Alveopora dan Stylophora merupakan genus karang keras yang paling dominan ditemukan di perairan Pulau Menjangan, sedangkan Acropora dan Porites dominan di Biorock Pemuteran. Coral branching dan Coral massive merupakan lifeform karang yang mendominasi kedua lokasi penelitian., This study aims to determine the distribution of hard coral genus, hard coral lifeform, benthic community composition and the percentage of hard coral cover in the waters of Menjangan Island and Biorock Pemuteran, Bali. Data collection was carried out in June 2016 using the Point Intercept Transect (PIT) method with a length of each transect of 50 meters and carried out three repetitions spread parallel to the shoreline. The composition of benthic communities in both study sites was dominated by dead coral with algae (Menjangan Island waters 51%, Biorock Pemuteran 48.9%). Followed by abiotic percentages (18%), hard coral (16%), soft coral (10%), dead coral (4.5%) and other fauna (0.5%) in the waters of Menjangan Island. Meanwhile in Biorock Pemuteran the percentage of abiotic (35%), hard coral (12%), dead coral (4%) and other fauna (0.1%). Alveopora and Stylophora are the dominant hard coral genus found in Menjangan Island waters, while Acropora and Porites are dominant in Biorock Pemuteran. Coral branching and Coral massive are coral lifeforms that dominate both study sites.
Keyword: benthic, biorock, hard coral genus, lifeform, Menjangan

Judul: : Struktur Komunitas Ikan Karang pada Ekosistem Terumbu Karang di Pulau Menjangan, Taman Nasional Bali Barat
Abstrak: Terumbu karang merupakan ekosistem yang banyak ditemukan di pulaupulau kecil di wilayah tropis, termasuk di Pulau Menjangan. Penelitian ini bertujuan untuk melihat kondisi ekosistem terumbu dan struktur komunitas ikan karang serta hubungan antara kelimpahan ikan karang dan bentuk pertumbuhan karang keras di Pulau Menjangan Taman Nasional Bali Barat. Pengambilan data ikan dilakukan dengan metode Underwater Visual Cencus dan pengambilan data terumbu karang menggunakan metode Point Intercept Transect. Persentase tutupan karang hidup termasuk ke dalam kategori sedang hingga baik sekali. Ditemukan total 10 bentuk pertumbuhan karang keras, 25 famili, 59 genus, 105 spesies dan 3104 individu ikan. Jenis-jenis ikan karang yang teridentifikasi dari hasil pengamatan dibedakan menjadi tiga kelompok yaitu ikan indikator, ikan mayor, dan ikan target. Kelompok ikan mayor paling banyak ditemukan di keempat Stasiun pengamatan. Nilai indeks keanekaragaman, keseragaman dan dominansi menunjukkan kondisi komunitas ikan karang di Pulau Menjangan berada dalam kondisi yang cukup baik atau stabil. Hasil visualisasi keterkaitan antara famili ikan karang dengan pertumbuhan karang keras berupa biplot dari analisis memberikan keragaman informasi sebesar 79,92% dari keseluruhan data yang didapat., Coral reefs are ecosystems commonly found on small islands in tropical regions, including Menjangan Island. This research aims to examine the condition of the reef ecosystem and the structure of coral fish communities, as well as the relationship between the abundance of coral fish and hard coral lifeform in Menjangan Island, West Bali National Park. Fish data collection was carried out using the Underwater Visual Census method, and coral reef data were collected using the Point Intercept Transect method. The percentage of live coral cover falls into the categories of fairly to very good. A total of 10 forms of hard coral growth, 25 families, 59 genera, 105 species, and 3104 individual fish were found. Coral fish species identified from observations were categorized into three groups: indicator fish, major fish, and target fish. The major fish group was most commonly found in all four observation stations. Diversity index, evenness, and dominance values indicate that the coral fish community at Menjangan Island is in fairly good or stable condition. The visualization results of the relationship between coral fish families and hard coral growth in the form of a biplot from the analysis provide 79.92% information diversity from the overall data obtained.
Keyword: terumbu karang, ikan karang, komunitas bentik, karang keras

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