Judul: pH Analysis of Tapanuli Orangutan Diet
Abstrak: Tapanuli orangutans are the newest and most endangered great ape species that have been known. Meanwhile, the pH values of Tapanuli orangutan diet is not available. Therefore, the aim of this study is to analysis the pH values of Tapanuli orangutan diet. The study was conducted from February to April 2019 at Camp Mayang, Batang Toru Research Station, North Sumatera. Next, foraging activity data were collected using focal animal sampling method. The pH values of diet sample were measured by MColorpHastTM as universal indicator of pH. SPSS 16.0 software was used in data processing. Data analysis was done with Shapiro-Wilk for normality test, while Kruskal-Wallis and Mann-Whitney for significant diferences test. In this study, five Tapanuli orangutans, two adolescent female and three adult female, have been followed. Fruits are their dominant food types. Camnosperma auriculatum fruits are their dominant diet during the observation. There is no significant differences between adolescent and adult female Tapanuli orangutans in the numbers of diet items at each pH values. On one hand, Tapanuli orangutan diets have pH values range between 3-7. On the other hand, Bornean and Sumatran orangutans prefer to eat fruits with range of pH values between 4-5.
Keyword: diet, food types, foraging activity, pH values, Tapanuli orangutans

Judul: Activity  Budget and Diet in Sumatran Orangutan (Pongo abelii) at Soraya Research Station, Aceh
Abstrak: The Sumatran orangutan (Pongo abelii) is categorised as critically endangered by the International Union for Conservation of Nature (IUCN). Orangutan existence in nature is increasingly threatened due to poaching and high rates of habitat deforestation. Understanding the daily activities and foraging behaviour of orangutans and their relationship to food availability can provide information on how orangutans can adapt to environmental changes in their habitat . Soraya Research Station (SRS) is a research station located in The Leuser Ecosystem and is a natural habitat for the Sumatran orangutan. However, information regarding the relationship between food availability and its relation to the daily activities and diet of orangutans in this location has not been reported. This study aims to observe orangutans daily activities and diet composition in SRS and their relationship with food availability. We observed the activity budget and diet from October 2021 until March 2022 at the Soraya Research Station (SRS). SRS is located in Pasir Belo Village, Sultan Daulat District, Subulussalam City, Aceh Province (N 02o 55’414”-E97o55’731”). The daily activity and feeding behaviour observation was conducted used focal animal sampling methods and instantaneous method with an interval of two minute. All data collected followed the standard collection of orangutan data in the field. The collection of phenological data and the food availability was carried out using the existing phenological plots at the research station. The number of phenological plots observed every month was 20 plots with a plot size of 20 x 20 m (0.8 Ha), estimated to consist of 550 trees. The phenological phases observed included young leaves, flowers, fruit, and ripe fruit. The phenological data and annual fruit availibility from 2017-2022 were also analyzed. Later, our data were analysed at the Laboratory of Biosystematics and Animal Ecology, Department of Biology, IPB University. The results show that the FAI index at SRS were low, only reaching 1.01%. Daily activity observations were carried out on five individual Sumatran orangutans in SPS, consisting of one adult female, two flanged males, one unflanged male, and one juvenile male in total 648 hours observations. Resting is the activity with the highest percentage (48%), followed by foraging (22.5%) and moving (21.8%). Low fruit availability affects Sumatran orangutans in SPS to have a high percentage of resting activity. The high percentage of rest activity aims to limit their energy expenditure. Analysis of diet composition showed that Sumatran orangutans in SPS consumed the highest percentage of bark (40.6%), followed by fruits and other plants (28.9% and 15.6%, respectively). The high consumption of bark by orangutans can be a fallback food in SPS and is thought to be a form of adaptation when fruit is scarce. The uniqueness of the SRS’s ecology characteristic might not be suitable for orangutans to live. Nevertheless, SRS can be considered a connected forest between the primary forest (Leuser National Park) and the surrounding belt forest area and other used forest areas. It can be considered a natural habitat for the Sumatran orangutans to meet their daily roaming area. The study on daily activities, population and phenology in SRS needs to be continued.
Keyword: Orangutan sumatra, Aktivitas harian, Fenologi, FAI, Komposisi pakan, Adaptasi

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