IMPLEMENTASI ALGORITMA K-MEANS UNTUK PEMILIHAN KERAMIK DAN PELANGGAN POTENSIAL PADA CV. JAYA TUNGGAL KERAMIK

CV. Jaya Tunggal Keramik is a company that sale of ceramics. CV. Jaya Tunggal Keramik experienced some problems regarding ceramics and customers such as difficulties in sale ceramics to customers so that some ceramic products accumulate in the warehouse, such as being damaged and ceramic display becomes less good because it is stored too long and difficulty retaining customers because some customers do not want to order ceramic products. Lack of precise decision taken by the management CV. Jaya Tunggal Keramik in determining the strategy to supply ceramic and how to make it CV. Jaya Tunggal Keramik is difficult to estimate the stock of ceramic products to be provided and it is difficult to determine which potential customers can be maintained as a regular customer. This research uses K-Means algorithm. K-Means algorithm is a partitioning clustering method that separates data into different groups with iterative partitioning. By using this application, users can find out the estimated stock and price of ceramics as well as information about potential customers. Testing in this research using data of November 2017 that compared with data of December 2017. Based on ceramic data test results, there are some ceramics that are not in accordance with the predicted results so it can be concluded that the K-Means algorithm on the test inventory data inventory in this study is not fully can provide accurate estimates, this is because the use of the K-Means algorithm is strongly influenced by the cluster center results and the attributes used.

Accelerating Flux Calculations Using Sparse Sampling

The ongoing miniaturization in electronics poses various challenges in the designing of modern devices and also in the development and optimization of the corresponding fabrication processes. Computer simulations offer a cost- and time-saving possibility to investigate and optimize these fabrication processes. However, modern device designs require complex three-dimensional shapes, which significantly increases the computational complexity. For instance, in high-resolution topography simulations of etching and deposition, the evaluation of the particle flux on the substrate surface has to be re-evaluated in each timestep. This re-evaluation dominates the overall runtime of a simulation. To overcome this bottleneck, we introduce a method to enhance the performance of the re-evaluation step by calculating the particle flux only on a subset of the surface elements. This subset is selected using an advanced multi-material iterative partitioning scheme, taking local flux differences as well as geometrical variations into account. We show the applicability of our approach using an etching simulation of a dielectric layer embedded in a multi-material stack. We obtain speedups ranging from 1.8 to 8.0, with surface deviations being below two grid cells (0.6–3% of the size of the etched feature) for all tested configurations, both underlining the feasibility of our approach.

Modern Cooperative Parallel SAT Solving

Nowadays, powerful parallel SAT solvers are based on an algorithm portfolio. Thealternative approach, (iterative) search space partitioning, cannot keep up, although, ac-cording to the literature, iterative partitioning systems should scale better than portfoliosolvers. In this paper we identify key problems in current parallel cooperative SAT solvingapproaches, most importantly communication, how to partition the search space, and howto utilize the sequential search engine. First, we improve on each problem separately. Ina further step, we show that combining all the improvements leads to a state-of-the-artparallel SAT solver, which does not use the portfolio approach, but instead relies on it-erative partitioning. The experimental evaluation of this system completely changes thepicture about the performance of search space partitioning SAT solvers: on instances ofa combined benchmark of recent SAT competitions, the presented approach can keep upwith the winners of last years SAT competition. The combined improvements improve theexisting cooperative solver splitter by 24%: instead of 561 out of 880 instances, the newsolver Pcasso can solve 696 instances.