Judul: Karakterisasi Dosimeter Saku Digital Merek Aloka PDM-112
Abstrak: Karakterisasi darf dosimeter saku digital detektor semikonduktor terhadap pengarub dosis radiasi, laju radiasi dan perubahan suhu dapat diketahui melalui tingkat kepekaan dosimeter. Dengan mengetahui karakterisasi aIat ini kEfelamatan bagi pekerja dan Iingkungan lebih teJjamin. Mengingat bahaya radiasi yang ditimbulkan temadap efck biologi. Penelitian ini dilakukan dengan menggunakan prinsip proteksi radiasi. Alat ya~ dipergunakan berupa dosimeter saku detektor semikonduktor yang diujikan terhadap vatiasi dosis radiasi, laju dosis radiasi serta variasi suhu. Prinsip kerja dari alat ini terdiri dari detektor seflt,ikonduktor ballan silikon, power sup/y,peralatan p,nunjang seperti : penguat. counter, memory dU. Metode yang dilakukan untuk memberikan variasi dosis yaitu dengan mengubah lamanya radiati. Semakin lama radiasj yang ditembakkan maka semakin besar dosis radiasi. Metode untuk memberikan pengaruh variasi laju dosis radiasi yaitu dengan merubah jarak dosimeter saku digital dengan sumber radiasi (Co-60). Semakin dekat jarak sumber radiasi dengan dosimeter maka semakin besar laju dosis yang diberikan. Metode terakhir yaitu variasi suhu_ Pada metode
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Judul: Karakterisasi Dosimeter Termoluminesensi Dengan Balian Kristal CaSO4
Abstrak: Sojak tabung sinar-X diciptakan pads taboo 1895 dan zat radioaktil" aIam ditanukan pads taboo 1896, manusia teIoIt mananlialkan nuIiasi da1gan bori>ogoi c:ara. Bcrbeda dmgan bmtuk ""'Bi yang lain sepcrti ... ..-gi Iistrik, panos, buoy;, maupun mcbnik, ""'Bi nuldir tidak dapat _ secara 1angsung oleh panca indc:ra manusia. Sebingga untuk mauleteksi ada atau tidak adanya radiasi dip.dukan suatu alai bantu. Di bidang proteksi radiasi, sistan pengukur ;ru mutlak diporlukan boik \Dltuk· keselamatan setiap pekerja maupun lingkungann)1L SetcIah keselamatan terbadap radiasi terjamin maka ban)1lk aplikasi teknik nuklir y8ng • diman1iatbn datgan mengglmabn sistem pcngukur radiasi ini. Metode peogulruran radiasi dcngan mcmanfiudkan fcoomcna tamoluminc:sensi _ koIi diporkcnaIkon pads tabun 1953. Podo .... ito behllD sepenuhnyadikdabui bohwametode tennolmninesensi dapat dikembangkan untuk tujuan pemantauan dosis pcrmmgaD. Tinggi rm.iabnya tanggopon TLD terbodop nuIiasi b<o"gontung pada ....... IreciInya dosis ndiasi yang ditaima TLD tcrsebut. Scmakin ....... dosis nuIiasi yang di_a, _ tinggi ......... _ TL yang dihasi1kaonya. Namun kmaikan da1am pcuerimaan dosis !:JeIum tcotll diikuti dcngaD kenaibn huil bac:aan inteositas TL secara linear untuk sctiap niJai dosis. Mak.1anya OOs'.Dlctcr hlDya maniliki dacrah linear pada rcotang nilai dosis tertentu. Berdasarkan hal taxbut maka pcrlu diIakukwt peDelitill_ yang dapat memberikan gambaran bubungan antara nilai dosis daD intensitas n, pada dosis ..... dab binggo tinggi. Untuk m",getahui besarnya laju dosis yang diterimaidiserap TLD dapat digunakon persamoan laj. dosis. Dan dangan ma>gintegrasilaumya ta"badop waktu onaka akan diperolelt besarnya nilai dosis.
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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