Judul: Studi karakteristik satwa mangsa macan tutul (Panthera pardus melas cuvier) di Taman Nasional Meru Betiri, Jawa Timur
Abstrak: Tujuan penelitian ini adalah untuk mempelajari karakteristik satwa mangsa macan tutul (Panthera pardus melas Cuvier), yaitu jenis-jenis satwa mangsa yang menjadi makanan macan tutul di Taman Nasional Meru Betiri. Metoda yang digunakan untuk mendapatkan data tentang jenis-jenis satwa mangsa macan tutul di Taman Nasional Meru Betiri adalah pengamatan langsung di lapangan dan analisis feses (secara tidak langsung). Pengamatan dan pengum pulan feses dilakukan di daerah-daerah contoh pengamatan yang dipilih secara purposif. Enam jenis satwa mangsa macan tutul yang berhasil diketahui berdasarkan analisis feses macan tutul yang telah dilakukan adalah kera (Macaca irus), lutung/budeng (Presbytis pyrrhus), landak (Histrix brachyura), babi hutan (Sus sp.), kijang (Muntiacus muncak), dan kucing hutan (Felis bengalensis). Dari ke enam jenis satwa mangsa macan tutul tersebut hanya babi hutan (Sus sp.) saja yang diketahui secara langsung sedang dimangsa macan tutul (di tepi sungai Permisan). Potensi total satwa mangsa macan tutul di Taman Nasional Meru Betiri masih cukup baik. Di antara jenis-jenis satwa mangsa yang ada tersebut, satwa kera (Macaca irus), lutung (Presbytis pyrrhus) dan babi hutan (Sus sp.) secara berturut-turut merupakan satwa mangsa macan tutul yang pa- ling potensial, karena selain memiliki populasi yang lebih besar dari pada satwa mangsa yang lain, ketiga satwa tersebut juga mempunyai penyebaran yang merata di selúruh kawasan Taman Nasional Meru Betiri. Diduga potensi satwa mangsa tersebut terus mengalami penurunan dari tahun ke tahun, sesuai dengan adanya degradasi habitat satwa (secara umum) di Taman Nasional Meru Betiri selama ini…dst
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Judul: Wilayah Jelajah Macan Tutul Jawa dan Sebaran Satwa Mangsa Potensial di Kawasan Hutan Gunung Sawal
Abstrak: Kawasan Hutan Gunung Sawal adalah salah satu habitat macan tutul jawa. Spesies ini merupakan endemik Pulau Jawa yang terancam punah (critically endangered) berdasarkan IUCN. Penelitian ini bertujuan untuk menghitung luasan wilayah jelajah macan tutul jawa, mengidentifikasi dan memetakan sebaran satwa mangsa potensial macan tutul jawa. Penelitian dilakukan dari bulan Desember 2019 sampai Februari 2020 dengan menggunakan kamera penjebak. Hasil penelitian menunjukkan terdapat enam individu macan tutul jawa, dengan tingkat perjumpaan 2.95 foto/100 hari rekam. Berdasarkan hasil perhitungan menggunakan metode Minimum Convex Polygon (MCP) diperoleh luas wilayah jelajah individu Jantan Dewasa 1 (JD 1) sebesar 15.19 km2, Jantan Dewasa 2 (JD 2) sebesar 3.84 km2, dan Betina Dewasa 2 (BD 2) sebesar 3.82 km2. Sebaran satwa potensial mangsa macan tutul jawa cukup merata di seluruh wilayah kawasan hutan Gunung Sawal. Keragaman tertinggi satwa potensial mangsa berada di wilayah Cibuyut dengan jumlah jenis satwa mangsa sebanyak tujuh jenis. Total ditemukan 13 satwa mangsa potensial di wilayah studi yakni musang luwak (Paradoxurus hermaproditus), babi hutan (Sus scrofa), sigung (Mydaus javanensis), kijang (Muntiacus muntjak), anjing kampung (Canis familiaris), kucing hutan (Prionailurus bengalensis), tikus belukar (Ratus tiomanicus), biul slentek (Melogale orientalis), trenggiling (Manis javanica), Surili (Presbytis comata), monyet ekor panjang (Macaca fascicularis), puyuh gonggong jawa (Arborophila javanica), dan paok pancawarna (Pitta guajana). Berdasarkan indeks kelimpahan relatif, musang luwak, sigung, babi hutan, kijang, puyuh gonggong, dan anjing kampung memiliki nilai foto per 100 hari rekam tertinggi berturut-turut yakni 5.8, 1.40, 0.62, 0.62, 0.62, 0.62.
Keyword: kamera penjebak, macan tutul jawa, MCP, satwa mangsa potensial, sebaran, wilayah jelajah

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