Judul: Faktor-faktor yang berkaitan dengan asupan energi dan zat gizi pasien kanker di Rumah Sakit Kanker Dharmais Jakarta
Abstrak: WHO (2003) reported that more than 10 million people suffering from cancer which rate of development was about 20% annually. In Indonesia, prevalence of cancer was 4.3 out of 1000 and it’s predicted that 12 million people are suffering from cancer each year (Riskesdas 2008). Shike (1996) stated that the result of cancer treatment (operation, chemotherapy, and/or radiation) was malnourished which about more than 40% patients of cancer. The objectiveof this studywas toanalyze factorsassociated withenergy and nutrients intake of cancer patients at Cancer Hospital of DharmaisJakarta. The study was conductedusinga cross-sectional design.The number of samplesin this studywere 63who were hospitalizedat least threedays and can consump which were served by hospital .Based on thesurvey showed that the most prevalent of cancer was breast cancer(19%) and there are 19 kind of tumors and cancers. More than half(60%) of samples had anormal nutritional status, 41% ofsamplescome tohospitalaims tocarry outthe chemotherapy, and 56% of samples were hospitalize during5-10 days. The averageadequacy level ofenergy andprotein were 50.1% and 44.9%, respectively.Furthermore, the adequacy level ofvitamin A, C, and E were 19.3%, 82%, and 8.2%, respectively. Based onlinearregressionanalysis showed thatperiod of hospitalizationand medication/treatment significantly influenced theenergy and protein intake by21.6% and 21.7%, respectively.
Keyword: 

Judul: Deskriptions of Cancer Patients at Dharmais Cancer Hospital Jakarta
Abstrak: Insidensi penyakit kanker dibandingkan dengan penyakit lain relatif sedikit, tetapi akan terus bertambah seiring dengan berjalannya waktu. Dalam penelitian ini ditelusuri perkembangan jumlah penderita kanker yang menjalani perawatan menginap dan yang meninggal di Rumah Sakit Kanker Dharmais (RSKD) pada tahun 1994-1997 ditinjau dari segi jenis kelamin, usia dan pekerjaan serta melihat ketergantungan ketiga faktor tersebut terhadap diagnosis yang dijatuhkan. Perkembangan jumlah penderita dan jumlah yang meninggal dijelaskan dengan menggunakan tabel sebaran frekuensi yang dilanjutkan dengan uji kehomogenan. Untuk menilai ketergantungan dilakukan dengan menggunakan uji ketergantungan yang mempunyai pendekatan khi kuadrat dan dilanjutkan dengan statistik Cramer untuk menilai besarnya ketergantungan. Sepanjang tahun 1994-1997 jumlah penderita kanker di RSKD bersifat homogen atau secara proporsi cenderung sama ditinjau dari segi diagnosis, usia, jenis kelamin dan pekerjaan. Bagi pasien wanita bagian tubuh yang sering terkena kanker adalah alat genital (20.79%) dan payudara (16.20%) terutama untuk wanita yang berusia di antara 45-64 tahun. Sedangkan untuk pasien pria kanker yang paling sering ditemukan adalah kanker alat pernapasan dan intratorak dan kanker alat pencernaan yaitu sebesar 10.46% dan 9.21%. Dalam kasus di RSKD antara diagnosis dengan usia, jenis kelamin dan pekerjaan terjadi suatu ketergantungan, walaupun ketergantungan tersebut berdasarkan statistik Cramer bernilai lemah. Pasien yang mengalami kematian di RSKD tersering diakibatkan oleh kanker alat pernapasan dan intratorak terutama pada pria, sedangkan untuk wanita sering diakibatkan oleh kanker alat genital dan payudara.
Keyword: 

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