PENGGUNAAN SUPPORT VECTOR REGRESSION (SVR) PADA PREDIKSI RETURN SAHAM SYARIAH BEI

Pada artikel ini algoritma support vector regression (SVR) digunakan untukmendapatkan model prediksi return saham syariah di bursa efek Indonsia. Sampeladalah emiten saham dengan likuiditas tinggi selama periode 2012. Pada penelitian iniPembentukan model didasarkan pada sebuah persamaan yang menghubungkan nilaiPBV dan ROE. Variabel terikat pada model adalah nilai proporsi rerata tahunan hargasaham pada dua tahun berurutan. Data harga saham merupakan hasil perkalian Priceto book value (PBV) dan Book value (BV). Sedangkan variabel bebasnya terdiri atasBook value (BV), tingkat pengembalian ekuitas (ROE) dan proporsi deviden yangdibayarkan ke investor public (POR). Performansi model prediksi berbasis SupportVector Machine (SVR) selanjutnya dibandingkan dengan model Regresi linearberganda berbasis Ordinary Least Squares (RLB-OLS) menggunakan pengukuran nilaiMean square error dan korelasi kuadratik untuk kesesuaian model. Hasil perbandingankedua model memperlihatkan bahwa model prediksi yang didapat menggunakan modelSVR lebih baik

Performance and programmability comparison of the thick control flow architecture and current multicore processors

Commercial multicore central processing units (CPU) integrate a number of processor cores on a single chip to support parallel execution of computational tasks. Multicore CPUs can possibly improve performance over single cores for independent parallel tasks nearly linearly as long as sufficient bandwidth is available. Ideal speedup is, however, difficult to achieve when dense intercommunication between the cores or complex memory access patterns is required. This is caused by expensive synchronization and thread switching, and insufficient latency toleration. These facts guide programmers away from straight-forward parallel processing patterns toward complex and error-prone programming techniques. To address these problems, we have introduced the Thick control flow (TCF) Processor Architecture. TCF is an abstraction of parallel computation that combines self-similar threads into computational entities. In this paper, we compare the performance and programmability of an entry-level TCF processor and two Intel Skylake multicore CPUs on commonly used parallel kernels to find out how well our architecture solves these issues that greatly reduce the productivity of parallel software development. Code examples are given and programming experiences recorded