Judul: Modal sosial dalam pengembangan ekowisata pada masyarakat adat di Taman Nasional Betung Kerihun (TNBK), Kalimantan Barat
Abstrak: Betung Kerihun National Park (BKNP) is conservation area with national park status with stated the decree of Mimistry of Forestry No. 467/Kpts-II/1995 dated September 5, 1995. Traditional community with heterogonous characteristic inhabited the area surrounding the park. The characteristics were seen in the social interaction of the communitty (Dephut 2006).
Keyword: 

Judul: Peran Stakeholders dalam Pengembangan Ekowisata di Taman Nasional Betung Kerihun Danau Sentarum Kalimantan Barat
Abstrak: Taman Nasional Betung Kerihun Danau Sentarum (TNBKDS) merupakan penggabungan kawasan Taman Nasional Betung Kerihun (TNBK) dan Taman Nasional Danau Sentarum (TNDS) berdasarkan Peraturan Menteri Lingkungan Hidup dan Kehutanan No: P.7/Menlhk/Setjen/OTL.0/1/2016 tanggal 29 Januari 2016. TNBKDS adalah kawasan konservasi lintas batas negara (transboundary ptotected area) Indonesia-Malaysia yang terletak di Kabupaten Kapuas Hulu Provinsi Kalimantan Barat. Kawasan TNBKDS memiliki berbagai potensi ekowisata menarik yang tercakup dalam konteks nature, culture dan adventure. Kendala yang muncul dalam pengelolaan ekowisata membuat TNBKDS belum menjadi destinasi unggulan. Pengelolaan ekowisata seyogyanya melibatkan berbagai stakeholders yang berkepentingan dengan berbagai peran yang dimilikinya. Kajian tentang siapa saja stakeholders yang terlibat dan pemetaan stakeholders sangat diperlukan untuk keberhasilan pengembangan ekowisata. Tujuan penelitian ini yaitu untuk merumuskan peran stakeholders terkait pengembangan ekowisata di TNBKDS. Penelitian dilakukan dari bulan Januari 2016 hingga April 2016. Data yang diperoleh dianalisis menggunakan metode analisis stakeholders, analisis kebutuhan, dan analisis peraturan perundangundangan. Hasil pemetaan stakeholders menunjukkan enam stakeholders berada di kelompok Key player yaitu BBTNBKDS, Disbudpar, Bappeda, Disparekraf, Dishubkominfo dan Disbunhut. Stakeholders ini mempunyai pengaruh dan kepentingan tinggi terhadap keberhasilan pengembangan ekowisata di TNBKDS. Hasil identifikasi kebutuhan pengembangan ekowisata di TNBKDS dikelompokkan menjadi enam aspek kebutuhan utama yaitu: (a) objek daya tarik wisata, (b) infrastruktur sarana dan prasarana, (c) promosi publikasi dan informasi, (d) peningkatan SDM, (e) perencanaan pengembangan ekowisata, dan (f) dana. Hasil identifikasi peraturan perundang-undangan ditemukan 16 peraturan terkait pengembangan ekowisata di TNBKDS. Peraturan perundang-undangan tersebut sebagai dasar untuk mengelola kawasan TN dan pembangunan pariwisata di Kabupaten Kapuas Hulu. Rumusan peran stakeholders sesuai dengan aspek-aspek manajemen terhadap pengembangan ekowisata di TNBKDS yaitu: (a) stakeholders yang berperan dalam proses perencanaan, pengorganisasian, pelaksanaan dan pengawasan program kegiatan pengembangan ekowisata di TNBKDS (b) stakeholders yang berperan sebagai mitra dalam pelaksanaan program pengembangan ekowisata di TNBKDS; dan (c) stakeholders yang berperan sebagai perencanaan dan pengendalian pembangunan
Keyword: kebutuhan ekowisata, konservasi biodiversitas, manajemen, pemetaan, stakeholders, peraturan

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