Judul: Karakteristik Ekologi, Potensi dan Upaya Konservasi Tumbuhan Tebu Rawa (Hanguana malayana) di Kabupaten Mappi, Papua
Abstrak: Hanguana is known as endemic plant in Papua, grows rapidly in the river of Obaa, Miwamen, Keeme, Widelman and Eilanden. Its presence is considered to become obstruction of river transportation, at the same time it is consumed by local people. Hanguana’s ecological characteristic and its potency have not been studied intensively. This research is aimed to study its ecological characteristic, to analyze the content of its primary and secondary metabolism compounds, its conservation efforts and to identify the local wisdom of Mappi indigenous people on this plant. The methods employed in this research are site observation, direct measurement, laboratory analysis, and interview with the local people at the observation sites. Purposive sampling method is utilized to determine the sampling of survey sites, interview with local people and local government officers. Hanguana is an aquatic plant therefore the tip of its stems and stolons up to its tip of the leaves are above the surface of the water while its roots, stems, and stolons are growing submerged under the water. Its roots are not attaching to any substrate. Ecological characteristics of the river water has a pH of approximately 4, temperature ranges from 28.2° to 30° C and total dissolved solid (TDS) of 6-8 ppm. The monthly average of rainfall varies between 46 to 391.5 mm. The soil of river base has an organic content of C of 1.67% (w/w), nitrogen 0,21% (w/w), potassium 279.44 ppm, phosphor 280.90 ppm and C/N ratio of 7.95. The vegetations that grow at the surrounding of Hanguana include Oryza rufipogon, Eleocharis dulcis, Pandanaceae, Nuclea grandifolia. Melaleuca symphiocarpa and Livistona benthamii are the common vegetations that grow at the river banks at the observation sites. Laboratory analysis results of Hanguana’s stem contain primary metabolism compounds are ccarbohydrate 2.62% (w/w), protein 2.7% (w/w), sucrose 0.01% (w/w), glucose <0.01% (w/w), fructose <0.01% (w/w), while secondary metabolism compounds are anti-oxidant 340.29 ppm, and toxicity of 1438.79 ppm. The stolon’s content primary metabolism compounds are carbohydrate 18.59% (w/w), protein 1.57%(w/w), sucrose <0.01%(w/w), glucose 0.5% (w/w) and fructose 0.38%(w/w), while secondary metabolism compounds are flavonoid 1.27%(w/w), tanin 0.30% (w/w), and toxicity of 712.84 ppm. Furthermore, Hanguana’s leave contains secondary metabolism compounds are flavonoid 1.54% (w/w), tanin 0.30% (w/w), fiber 32.03% (w/w), and toxicity of 850.70 ppm. Due to those compounds content, Hanguana is believed to have a potential as a medicine for prevention of cardiovascular diseases.
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Judul: Ethnobotany of Food Plant Used by Minangkabau Community in Lima Puluh Kota District
Abstrak: Minangkabau merupakan salah satu etnis di Indonesia dengan kekayaan alam dan keberagaman tradisi budaya. Salah satu wilayah pusat asal nenek moyang Minangkabau yang memiliki peninggalan sejarah megalit berupa batu tegak (menhir) terletak di Kabupaten Lima Puluh Kota. Masyarakat Minangkabau memiliki filosofi yang menggambarkan hubungan masyarakat dengan alam, salah satunya dalam pemanfaatan tumbuhan sebagai bahan pangan. Masyarakat di wilayah ini mengolah berbagai jenis tumbuhan menjadi rendang daun. Informasi mengenai jenis, pemanfaatan dan hubungan masyarakat Minangkabau dengan tumbuhan pangan di Kabupaten Lima Puluh Kota belum tersedia. Penelitian ini bertujuan untuk: (1) menginventarisasi serta mengidentifikasi jenis dan manfaat tumbuhan sebagai bahan pangan; (2) menganalisis nilai penting ekologi, ekonomi, dan tingkat kepentingan pemanfaatan tumbuhan dari sudut pandang adat dan budaya masyarakat Minangkabau; (3) menginventarisasi jenis-jenis yang berpotensi dikembangkan sebagai sumber daya genetik dan (4) memberikan masukan tentang pengelolaan keanekaragaman jenis tumbuhan bahan pangan secara berkelanjutan. Penelitian dilakukan dari bulan Juli 2021 hingga Juni 2022. Pengumpulan data dilakukan di enam desa yaitu Desa Maek, Desa Andiang, Desa Galugua, Desa Koto Alam, Desa Koto Tinggi dan Desa Guguak VIII Koto yang dikelompokkan menjadi desa kategori tradisional, sedang dan maju berdasarkan ketersediaan sarana dan prasarana. Pengumpulan data dilakukan dengan melakukan wawancara terbuka, semi terstruktur dan terstruktur melalui kuesioner. Data yang diambil meliputi keanekaragaman jenis tumbuhan, bagian yang digunakan dan tempat diperolehnya tumbuhan. Tumbuhan di setiap satuan lingkungan diinventarisasi dengan analisis vegetasi. Pengumpulan data kuantitatif nilai kepentingan satuan lingkungan dan tumbuhan dihitung menggunakan metode sebaran kerikil atau Pebble Distribution Method (PDM). Data dianalisis menggunakan indeks nilai penting tumbuhan (INP), Use Values (UVs), dan Local User's Value Index (LUVI), Index of Cultural Significant (ICS) dan valuasi ekonomi. Masyarakat Minangkabau memanfaatkan 154 jenis dari 51 suku sebagai tumbuhan pangan. Jumlah jenis tumbuhan yang dimanfaatkan masing-masing desa di lokasi penelitian berbeda. Masyarakat Desa Galugua (desa tradisional) memanfaatkan lebih banyak jenis tumbuhan pangan dibandingkan masyarakat Desa Guguak VIII Koto (desa maju). Keanekaragaman jenis tumbuhan paling banyak dimanfaatkan berasal dari suku Fabaceae, Zingiberaceae dan Solanaceae. Masyarakat memperoleh tumbuhan pangan dari enam satuan lingkungan yaitu pekarangan, kebun, sawah, ladang, parak dan hutan. Tumbuhan pangan paling banyak ditemukan di satuan lingkungan pekarangan. Jenis tumbuhan pada tiap satuan lingkungan bervariasi dan memiliki indeks nilai penting (INP) yang berbeda di setiap desa. Masyarakat memanfaatkan tumbuhan pangan sebagai bahan pangan pokok (1 jenis), pangan tambahan (8 jenis), biji-bijian (3 jenis), sayuran (66 jenis), buah-buahan (55 jenis), bumbu (41 jenis) dan minuman (6 jenis). Masyarakat memanfaatkan delapan bagian tumbuhan dan bagian tumbuhan yang paling banyak dimanfaatkan adalah buah (79 jenis) dan daun (54 jenis). Berdasarkan hasil analisis, tumbuhan pangan memiliki nilai kegunaan (UVs) berkisar 0,36-3,40. Karambia (Cocos nucifera) dan pisang (Musa paradisiaca) merupakan jenis tumbuhan pangan dengan nilai UVs dua tertinggi di semua desa. Tumbuhan budi daya memiliki nilai kepentingan lokal (LUVI) berkisar 0,04-4,58%. Karambia dinilai sebagai tumbuhan pangan budi daya paling penting kedua (LUVI tertinggi kedua) setelah padi (Oryza sativa) oleh masyarakat di semua desa. Nilai kepentingan budaya (ICS) tumbuhan pangan budi daya berkisar 9-148. Dalam kehidupan masyarakat karambia memiliki nilai ICS tertinggi dibanding tumbuhan lainnya termasuk padi karena memiliki banyak kegunaan sebagai tumbuhan pangan maupun kegunaan lainnya. Tumbuhan liar
Keyword: economic value of plants, ICS, landscapes, local wisdom, plant diversity

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