Judul: Analisis energi pada proses pembuatan minyak nyamplung
Abstrak: Energi merupakan sumber kebutuhan vital bagi masyarakat. Kondisi empiris menunjukkan bahwa sebagian besar masyarakat masih tergantung pada sumber energi yang berasal dari fosil seperti minyak dan gas. Penggunaan bahan bakar fosil saat ini mulai dikaji ulang karena ketersediaannya mulai menipis dan butuh waktu yang relatif lama untuk terbentuk kembali. Sementara itu konsumsi bahan bakar minyak (BBM) pada sektor transportasi untuk jenis gasoline oktan 88 (premium atau bensin), gasoline oktan 92 (pertamax), dan biodiesel 5% (biosolar) juga meningkat tiap tahunnya. Salah satu alternatif sebagai pengganti bahan bakar fosil adalah biofuel yang berasal dari sumberdaya hayati yang bisa diperbarui berupa minyak nabati dan hewani. Salah satu sumber minyak nabati yang dapat digunakan sebagai bahan bakar adalah Nyamplung (Calophyllum inophyllum). Kelebihan Nyamplung sebagai bahan bahan baku biofuel adalah menghasilkan rendemen yang cukup tinggi, mencapai 72%, tersebar merata secara alami di Indonesia dan memiliki daya bertahan hidup yang tinggi, produktivitasnya tinggi hingga 20 ton/ ha sedangkan jarak pagar 5 ton/ha, berbuah sepanjang tahun, sebagai wind breaker yang melindungi tanaman pertanian agar tidak rebah terkena angin besar, dan tidak berkompetisi dengan kebutuhan pangan. Biji nyamplung mengandung minyak 40-72%; air 25-35%; dan abu 1.1-1.3%. Minyak kasar mengandung asam resin 9.7-15% yang menyebabkan minyak berwarna hijau, rasanya pahit, dan beracun. Berdasarkan jumlah tegakan pohon nyamplung yang ada di Indonesia, potensi minyak nyamplung yang dihasilkan sebesar 39,405.6 ton/ tahun atau 43,784,000 kl/tahun
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Judul: Analisa teknoekonomi pendirian industry biodiesel dari biji nyamplung (Calophyllum inophyllum L.)
Abstrak: World crises that occurred in the last decades caused the scarcity of fuel oil. During 2004 fuel oil consumption in Indonesia reached 26.9 billion liters, and in 2011 is estimated to reach more than 34.71 billion liters. Fuel consumption is not in line with fuel production, necessitating the development of alternatives energy. One of them is to use Nyamplung seed that is processed into biodiesel. Nyamplung (Calophyllum inophyllum L.) is a plant which commonly found around the coast. Nyamplung plant is one of the biodiesel alternative materials that have a large potential to be developed to overcome the problem of scarcity of fuel. The advantages of Nyamplung as raw material for biodiesel are the seeds have high rendement (approximately 70%) and its use does not compete with food purposes. The purpose of this study is to analyze techno-economic aspect of biodiesel industry establishment of Nyamplung seed. Scope of research includes techno-economic on the market and marketing aspects, technical and technological, management and organization, environmental, legal, valuation and commercialization, as well as financial aspects. This biodiesel industry is planned to be located in Banyuwangi with the selection criteria associated with using AHP (Analytical Hierarchy Process) and the industrial capacity of 1000 liters per day with raw materials as much as 4500 kg per day. After a deep assessment of all aspects, it can be concluded that the industry of biodiesel from Nyamplung seed is feasible to be realized.
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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