Cancer is one of the most deadly diseases in the world. The International Agency for Research on Cancer (IARC) noted 14.1 million new cancer cases and 8.2 million deaths from cancer in 2012. In the last few years, DNA microarray technology has increasingly been used to analyze and diagnose cancer. Analysis of gene expression data in the form of microarray allows medical experts to ascertain whether or not a person suffers from cancer. DNA microarray data has a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme that includes dimension reduction is needed. In this research, a Principal Component Analysis (PCA) dimension reduction method that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, a Support Vector Machine (SVM) and Levenberg-Marquardt Backpropagation (LMBP) algorithm were selected. Based on the tests performed, the classification method using LMBP was more stable than SVM. The LMBP method achieved an average 96.07% accuracy, while the SVM achieved 94.98% accuracy.

Background Risks associated with a software project have the potential to affect all stakeholders. Today much software makes use of off-the-shelf (OTS) components. A better understanding of OTS-derived software risks will help to define responsibilities for these risks, and also to avoid them. Aim Our objective is to identify, classify and compare risks of OTS-based software projects from both a software development and a software acquisition perspective. Method To identify and classify the risks, we performed a systematic mapping study. In order to compare risks of OTS-based software development and acquisition in the real world setting, we used the mapping study results to survey occurrences of 11 shared risks in OTS-based software, in 35 OTS-based software developments and 34 OTSbased software acquisitions of Indonesian background. The survey is a partial replication of a previous study. Results We identified 133 risks associated with OTS-based software development and 36 risks associated with OTS-based software acquisition. These risks are grouped into 17 risk categories. Risks occurred more frequently in software acquisition than in software development. In addition, two risks, insufficient OTS component documents and lack of provider technical support and training, frequently occurred only in the software development. Conclusions In OTS-based projects, most risks for acquisition and development are similar. Technical-related risks are found less often in acquisition and project management related risks are found less often in development. Shared risks are perceived differently by developers and acquirers. Better understanding of actual and perceived risk in OTS-based software projects will improve risk management. Further work to validate these results is ongoing.

Data mining merupakan proses analisis data menggunakan perangkat lunak untuk menemukan pola dan aturan (rules) dalam himpunan data. Data mining dapat menganalisis data yang besar untuk menemukan pengetahuan guna mendukung pengambilan keputusan. Dalam penelitian ini akan dibahas Association Rule sebagai salah satu fungsi data mining yang diimplementasikan menggunakan Algoritma Apriori. Akan dianalisis pula dua teknik penghitungan support di candidate generation pada Algoritma Apriori, yakni : K-way dan 2 Group-By pada tiga sampel dataset dengan atribut transaksi id dan item. Pada penelitian ini terlihat bahwa permasalahan penghitungan support di candidate generation merupakan bottleneck dari Algoritma Apriori dimana perbaikan Algoritma Apriori ditekankan pada candidate generation dan efektivitas dari Algoritma Apriori. Penelitian ini dilakukan pada RDBMS Oracle dengan memanfaatkan tools TKPROF untuk mengukur performansi query berdasarkan operasi I/O pada penghitungan support di candidate generation. Hasil penelitian membuktikan bahwa metode support counting K-way lebih baik daripada Two Group-by.Kata Kunci : Data Mining, Association Rule, Algoritma Apriori, candidate generation, K-way, 2 Group-By

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