Judul: Potential for Development of Healing Forest in Pancoran Mas Grand Forest Park, Depok City
Abstrak: Padatnya rutinitas yang terjadi di lingkungan perkotaan umumnya dapat menimbulkan seseorang mengalami ketegangan dan tekanan sehingga dapat menimbulkan stres dan mengganggu aktivitas seseorang dalam melakukan suatu pekerjaan. Healing forest menjadi salah satu alternatif untuk menghilangkan stres dengan menampilkan suasana alam yang memberikan efek ketenangan dan kenyamanan. Penelitian ini bertujuan menginventarisasi potensi biofisik dan merumuskan strategi pengembangan Taman Hutan Raya Pancoran Mas sebagai healing forest. Metode yang digunakan adalah studi literatur, obervasi langsung, kuesioner kepada masyarakat, dan wawancara pengelola. Hasil Data dianalisis menggunakan metode deskriptif kualitatif. Hasil penelitian menunjukkan Taman Hutan Raya Pancoran Mas memiliki potensi untuk dijadikan healing forest berdasarkan standar yang digunakan. Strategi analisis SWOT menunjukkan strategi yang meminimalisir atau mengatasi kelemahan untuk memanfaatkan peluang yang dimiliki Taman Hutan Raya Pancoran Mas (Strategi WO).
Keyword: comfort, healing forest, grand forest park, stress

Judul: Development of Healing Forest at Bedengan Campground and Sengkaling Recreational Park
Abstrak: Kepadatan penduduk di perkotaan menyebabkan timbulnya stress pada seseorang dan karenanya membutuhkan lingkungan yang mendukung untuk pemulihan, memberikan ketenangan dan kenyamanan. Konsep pemanfaatan hutan sebagai penyembuhan atau bisa disebut healing forest kini menjadi sebuah tren baru di dunia. Penelitian ini bertujuan mengkaji potensi biofisik healing forest di Bumi Perkemahan Bedengan dan Taman Rekreasi Sengkaling. Metode yang digunakan yaitu melakukan survei lapang, wawancara, dan melakukan studi litelatur. Setalah itu, data diolah dan dianalisis menggunakan metode deskriptif kualitatif dan mengkomparasikan dengan kriteria healing forest menurut SNI 9006:2021. Hasil penelitian menyebutkan bahwa Bumi Perkemahan Bedengan dapat dijadikan area healing forest, sedangkan pada Taman Rekreasi Sengkaling masih perlu adanya pengembangan dibeberapa potensi biofisiknya karena tidak memenuhi kriteria. Kegiatan healing forest yang dapat dilakukan seperti berjalan menelusuri hutan dan menikmati pemandangan. Kedua lokasi yang berpotensi sebagai area healing forest dapat dikembangkan dengan cara menigkatkan pengelolaan dan penataan wilayah dengan baik, serta pemeliharaan tapak., Urban population density causes stress and therefore raises necessitates a supportive environment for recovery, providing tranquility and comfort. The concept of using forests for healing, known as healing forests, has become global trend in recent. This research aims to assess the biophysical potential of healing forests at the Bedengan Campground and Sengkaling Recreation Park. The methods used include field surveys, interviews, and literature studies. The data is then processed and analyzed using qualitative descriptive methods and compared with the healing forest criteria according to SNI 9006:2021. The research concludes that Bedengan Camping Ground can be designated as a healing forest area, while Sengkaling Recreational Park requires further development in certain biophysical aspects as it doesn't meet the criteria. Healing forest activities include walking through the forest and enjoying the scenery. Both potential locations can be developed through proper land management and further research on site development as healing forest areas is needed.
Keyword: Healing Forest, Kepadatan penduduk, Stres, population density

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