Judul: Ethnobotany of the Medicinal Plant Kratom Borneo  (Mitragyna speciosa Korth) in Pantas Bersatu Village, Kapuas Hulu Regency, West  Kalimantan
Abstrak: Kratom (Mitragyna speciosa Korth.) merupakan salah satu tanaman obat yang banyak ditemukan di Kalimantan Barat. Penelitian ini bertujuan mengetahui etnobotani, proses pasca panen, dan harga jual tanaman kratom oleh masyarakat Desa Pantas Bersatu. Lokasi penelitian terletak di Desa Pantas Bersatu, Kabupaten Kapuas Hulu, Provinsi Kalimantan Barat. Data yang dikumpulkan dan dianalisis berupa kondisi umum penelitian, karakteristik responden, jumlah tanaman, pendugaan nilai ekonomi, dan pemanfaatan kratom. Masyarakat Desa Pantas Bersatu memanfaatkan kratom sebagai obat tradisional untuk diabetes, darah tinggi, penambah stamina, dan sebagai obat untuk mengurangi kecanduan minuman keras. Kratom diolah dengan cara direbus atau diseduh dan diminum 1-2 kali dalam seminggu. Proses pasca panen yang dilakukan oleh masyarakat Desa Pantas Bersatu masih tergolong sangat tradisional. Harga jual yang ditawarkan oleh para tengkulak cukup beragam berdasarkan dengan kualitas dan jenis hasil yang dijual.
Keyword: etnobotani

Judul: Financial Analysis of Kratom (Mitragyna speciosa)
Cultivation as a Prospective New Commodity: A Comparative Study with Rubber
Cultivation
Abstrak: Penurunan harga jual karet akibat fluktuasi pasar global mendorong petani di Desa Saujung Giling Manik, Kecamatan Embaloh Hulu, Kabupaten Kapuas Hulu, Kalimantan Barat untuk beralih dari budidaya karet ke budidaya kratom. Tanaman kratom mulai dilirik sebagai komoditas yang menjanjikan di masyarakat karena memiliki harga yang lebih tinggi dibanding karet. Selain itu permintaan untuk ekspor kratom ke luar negeri terus meningkat dan belum dapat dipenuhi oleh eksportir. Penelitian ini menganalisis usaha budidaya kratom di Desa Saujung Giling Manik dibandingkan dengan budidaya karet dengan mempertimbangkan aspek finansial seperti NPV, IRR, BCR, dan payback period. Hasil perbandingan dari kedua usaha menunjukkan bahwa usaha budidaya kratom memiliki keuntungan yang lebih besar dibandingkan dengan usaha budidaya karet. Usaha budidaya kratom dalam jangka waktu 10 tahun dengan luasan 1 ha menghasilkan keuntungan bersih sebesar Rp 195.746.220,47 dengan tingkat pengembalian sebesar 113%, rasio manfaat biaya sebesar 2,09 serta pengembalian modal usaha 1 tahun., The decline in rubber selling prices due to global market fluctuations has encouraged farmers in Saujung Giling Manik Village, Embaloh Hulu District, Kapuas Hulu Regency, West Kalimantan, to switch from rubber cultivation to kratom cultivation. The kratom plant is starting to be looked at as a promising commodity in the community because it has a higher price than rubber. In addition, the demand for kratom exports abroad continues to increase and cannot be fulfilled by exporters. This study analyzed kratom cultivation in Saujung Giling Manik Village compared to rubber cultivation by considering financial aspects such as NPV, IRR, BCR, and payback period. The results of the comparison of the two businesses show that kratom cultivation has a greater profit than rubber cultivation. The kratom cultivation business in 10 years with an area of 1 ha creates a net benefit of Rp 195.746.220,47 with a rate of return of 113%, a cost-benefit ratio of 2.09, and a 1-year payback period.
Keyword: analisis finansial, budidaya kratom, budidaya karet

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