Judul: Biodiversity of Water Birds in the Mulyorejo Mangrove Forest Tirto District Pekalongan
Abstrak: Hutan mangrove merupakan tipe hutan yang tumbuh di lahan basah dan dipengaruhi oleh pasang-surut air laut serta menjadi habitat bagi burung air. Penelitian ini bertujuan mengidentifikasi jenis, menghitung dan mendeskripsikan komposisi, kelimpahan, dan keragaman burung air, mendeskripsikan kondisi habitat dan vegetasi yang dimanfaatkan burung air, serta memberikan implikasi terhadap pengelolaan Hutan Mangrove Mulyorejo Kecamatan Tirto Pekalongan. Penelitian dilakukan pada bulan Desember 2022 hingga Mei 2023. Pengambilan data burung air menggunakan metode eksplorasi dan titik hitung. Hasil penelitian menunjukkan terdapat 12 spesies burung air dari tiga famili. Indeks keragaman burung air (H’) di lokasi penelitian sebesar 1,226 dengan indeks kemerataan (E’) sebesar 0,493. Indeks kelimpahan menunjukkan spesies blekok sawah (Ardeola speciosa) termasuk kategori spesies dominant dengan nilai sebesar 68,713. Spesies mangrove yang paling banyak dimanfaatkan oleh burung air yaitu api-api (Avicennia officinalis), terutama di tajuk atas pohon. Monitoring habitat dan burung air secara berkala diperlukan sebagai bahan pertimbangan dalam pengelolaan kawasan.
Keyword: water birds community, wetlands, water birds habitats

Judul: Bird Community in Three Different Habitat Types in Muara Gembong and Their Contributing Factors
Abstrak: Rawa air asin dan mangrove merupakan salah satu habitat yang terancam di Indonesia, karena pembangunan dan konversi besar-besaran di daerah pesisir. Muara Gembong merupakan salah satu lahan basah pesisir yang tersisa di Jawa dan menjadi salah satu suaka bagi burung-burung rawa dan mangrove. Meskipun sudah banyak penelitian tentang komunitas burung di lahan basah, masih sedikit penelitian komunitas burung di lahan basah Indonesia. Komunitas burung diobservasi di 31 plot di tiga tipe habitat. Sebanyak sebelas variabel biotik dan abiotik juga diukur dalam penelitian ini untuk menentukan faktor yang paling memengaruhi komunitas burung, yaitu jumlah jenis tumbuhan (pohon, semak, herba, dan keseluruhan), persentase tutupan pohon, semak, dan herba, nilai pH air, kekeruhan air, kedalaman air, serta kadar garam pada air. Metode point count digunakan dalam penelitian ini untuk mengamati komunitas burung. Data komunitas burung kemudian dianalisis dengan analisis keanekaragaman, analisis kemerataan, dan estimasi kelimpahan. Faktor-faktor habitat dianalisis dengan redundancy analysis dan principal component analysis (PCA) untuk menganalisis faktor-faktor yang memengaruhi komunitas burung. Komunitas burung di Muara Gembong merupakan campuran dari jenis-jenis burung spesialis dan generalis. Komunitas burung paling beragam dapat dijumpai di habitat mangrove (H’ = 3,306). Mangrove (42 jenis) juga menjadi habitat dengan jumlah jenis burung terbanyak, diikuti oleh habitat rawa berumput (39 jenis) dan tambak ikan (34 jenis). Berdasarkan estimasi kelimpahan, diperkirakan terdapat 55 jenis burung di habitat mangrove, 39 jenis di rawa berumput dan 42 jenis di tambak ikan. Berdasarkan analisis guild, burung yang paling banyak dijumpai merupakan burung insektivora, diikuti dengan burung omnivora dan piscivora. Jumlah jenis tumbuhan (pohon, semak, tumbuhan bawah, dan keseluruhan) menjadi faktor yang paling memengaruhi komunitas burung. Jumlah total tumbuhan juga menjadi vektor terpanjang yang dapat menjelaskan keanekaragaman jenis burung paling signifikan. Analisis PCA terpisah pada tiap tipe habitat menunjukkan faktor abiotik lain dapat berpengaruh dengan keanekaragaman burung. Komposisi jenis tumbuhan mangrove (43,33% variasi) menjadi komponen paling berpengaruh di habitat mangrove. Karakteristik vegetasi rawa berumput (36,67% variasi) dan faktor-faktor abiotik lain (18,6% variasi) menjadi komponen paling berpengaruh di habitat rawa berumput. Tumbuhan air di tambak ikan (34,27% variasi) dan tutupan area bersemak (20,45% variasi) menjadi komponen paling berpengaruh di habitat tambak ikan. Hal ini menunjukkan bahwa komunitas burung di lahan basah masih sangat bergantung pada vegetasi sebagai tempat mencari makan dan beristirahat., Marshlands and backswamps are among threatened habitat in Indonesia along with mangroves, particularly in Java due to area development and land conversion. Muara Gembong is one of the remaining wetlands in Java and serve as one of the last refuges for bird community in wetlands, particularly mangrove and marsh-associated bird. Although there are plenty of bird community study in terrestrial wetlands, there are still less studies about wetland bird community conducted in Indonesia. The bird community were observed in 31 plots in three habitats. There are also eleven habitat variables collected in three habitats, i.e. mangrove, marsh, and fishpond, to see which variable affects bird abundance and diversity most. The variables are the number of plant species (trees, shrubs, herbs, and overall), cover percentage (trees, shrubs, and herbs), water pH, water turbidity, water salinity, and water body depth. The bird community were observed with
Keyword: vegetation, mangrove, marshland, bird community analysis

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