Sistem Pendukung Keputusan Penentuan Jenis Tanaman Pangan Berdasarkan Kandungan Tanah Menggunakan Metode Analytical Hierarchy Process (Ahp) dengan Algoritma Genetika

Samosir regency is a from North Sumatera province which dominantly people is live on agriculture. The most of the farmers using autodidact learning to determining the types of plants that will be process the appropriate with their land, where farmers just learn from their experiences and exchange opinion with another farmers. Based of the problem, the system will be built by Analytical Hierarchy Process (AHP) with Genetic Algorithm. The result of the testing from method Analytical Hierarchy Process (AHP) obtained result with consistency ratio values < 0,1, which is -0,5936 while the result of the testing from Genetic Algorithm obtained value of the fitness is 157,25. Based on the test results, it can be concluded that the method Analytical Hierarchy Process (AHP) obtained the result more consistent with the value of consistency ratio should be < 0,1, so it will be easier to making decision, while on the analysis of Genetic Algorithm, obtained result are probabilistic as always having random process, so that decision making can not be consistent. So, Decision Support System to determination of plants based by contents of soil which are good and suitable is method Analytical Hierarchy Process (AHP)

Computation in generalised probabilistic theories

From the existence of an efficient quantum algorithm for factoring, it is likely that quantum computation is intrinsically more powerful than classical computation. At present, the best upper bound known for the power of quantum computation is that BQP is in AWPP. This work investigates limits on computational power that are imposed by physical principles. To this end, we define a circuit-based model of computation in a class of operationally-defined theories more general than quantum theory, and ask: what is the minimal set of physical assumptions under which the above inclusion still holds? We show that given only an assumption of tomographic locality (roughly, that multipartite states can be characterised by local measurements), efficient computations are contained in AWPP. This inclusion still holds even without assuming a basic notion of causality (where the notion is, roughly, that probabilities for outcomes cannot depend on future measurement choices). Following Aaronson, we extend the computational model by allowing post-selection on measurement outcomes. Aaronson showed that the corresponding quantum complexity class is equal to PP. Given only the assumption of tomographic locality, the inclusion in PP still holds for post-selected computation in general theories. Thus in a world with post-selection, quantum theory is optimal for computation in the space of all general theories. We then consider if relativised complexity results can be obtained for general theories. It is not clear how to define a sensible notion of an oracle in the general framework that reduces to the standard notion in the quantum case. Nevertheless, it is possible to define computation relative to a `classical oracle'. Then, we show there exists a classical oracle relative to which efficient computation in any theory satisfying the causality assumption and tomographic locality does not include NP.Comment: 14+9 pages. Comments welcom

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover