Simulasi dalam Optimalisasi Pengadaan Barang menggunakan Metode K-Mean Clustering

Products provided by a store have an influence on store sales. Consumers will be attracted to stores that provide products according to their wants and needs. The purpose of this research is to find out what ornamental flower products are most in demand by consumers, in demand by consumers and less desirable to consumers. Keywords: inventory of goods, K-Mean Clustering, Data Mining, cluster, optimal. Store managers can get information about goods that have been depleted of inventory stock to be updated immediately. The method used in this study is the K-Mean Clustering method which belongs to one of the branches of Data Mining. The data used in the study is data from January 2020 to December 2020 as many as 100 pieces taken from naafilah official shop, Padang. The data variables used in the entry of goods are the year, product name, price and amount sold. Furthermore, the data is processed using Rapid Miner software. The first stage of processing is to determine the value of clusters randomly, in this study researchers divided the cluster values into 3 groups. Next, the centroid value of each group will be determined. Centroid is derived from the minimum value, middle value and maximum value of the data provided. Then, the cluster process is calculated using the euclidean distance formula. Cluster calculations are done by calculating the closest distance to the data. The final result of this study is to find out the best-selling, best-selling and less-selling ornamental flowers, so that sellers can optimize the provision of ornamental flowers for the future.

A Guide to Benchmarking Enzymatically Catalysed Reactions: The Importance of Accurate Reference Energies and the Chemical Environment

We explore two significant factors on the outcomes of benchmark studies for enzymatically catalysed reactions, namely the level of theory of the benchmarks and the size of the model system used to represent the enzyme active site. For the benchmarks, we compare two potential alternatives to canonical coupled cluster results for situations where CCSD(T) is computationally too demanding: a strategy to estimate finite basis set coupled cluster values and the local-correlation DLPNO-CCSD(T) method at the complete basis set limit. We confirm the high accuracy of DLPNO-CCSD(T) used with tight thresholds. We also show that notable differences can be seen when using both sets of references for a benchmark study, with absolute deviations from the higher quality references generally smaller than those from lowerquality ones as well as changes in the ranking of the assessed methods. For geometries, we test three models for the active site of 4-oxalocrotonate tautomerase: one typical of the QM region that may be used in QM/MM studies, and two smaller variants that neglect the surrounding chemical environment. Benchmarking of 12 density functionals known to perform well on enzymatically catalysed reactions shows inconsistent performance of each method across the three models, contradicting the common idea that small representative systems can be used to accurately assess the applicability of low-level methods for larger biochemical applications. Our findings shall serve as a reminder on the standards that should be adhered to in benchmark studies, and as a guide for future studies, both on enzyme-related and other chemical problems.

A Guide to Benchmarking Enzymatically Catalysed Reactions: The Importance of Accurate Reference Energies and the Chemical Environment

We explore two significant factors on the outcomes of benchmark studies for enzymatically catalysed reactions, namely the level of theory of the benchmarks and the size of the model system used to represent the enzyme active site. For the benchmarks, we compare two potential alternatives to canonical coupled cluster results for situations where CCSD(T) is computationally too demanding: a strategy to estimate finite basis set coupled cluster values and the local-correlation DLPNO-CCSD(T) method at the complete basis set limit. We confirm the high accuracy of DLPNO-CCSD(T) used with tight thresholds. We also show that notable differences can be seen when using both sets of references for a benchmark study, with absolute deviations from the higher quality references generally smaller than those from lowerquality ones as well as changes in the ranking of the assessed methods. For geometries, we test three models for the active site of 4-oxalocrotonate tautomerase: one typical of the QM region that may be used in QM/MM studies, and two smaller variants that neglect the surrounding chemical environment. Benchmarking of 12 density functionals known to perform well on enzymatically catalysed reactions shows inconsistent performance of each method across the three models, contradicting the common idea that small representative systems can be used to accurately assess the applicability of low-level methods for larger biochemical applications. Our findings shall serve as a reminder on the standards that should be adhered to in benchmark studies, and as a guide for future studies, both on enzyme-related and other chemical problems.

Cosmic magnetic fields with masclet: an application to galaxy clusters

ABSTRACT We describe and test a new version of the adaptive mesh refinement cosmological code masclet. The new version of the code includes all the ingredients of its previous version plus a description of the evolution of the magnetic field under the approximation of the ideal magnetohydrodynamics (MHD). To preserve the divergence-free condition of MHD, the original divergence cleaning algorithm of Dedner et al. (2002) is implemented. We present a set of well-known 1D and 2D tests, such as several shock tube problems, the fast rotor, and the Orszag–Tang vortex. The performance of the code in all the tests is excellent with estimated median relative errors of ∇ · B in the 2D tests smaller than 5 × 10−5 for the fast rotor test, and 5 × 10−3 for the Orszag–Tang vortex. As an astrophysical application of the code, we present a simulation of a cosmological box of 40 comoving Mpc side length in which a primordial uniform comoving magnetic field of strength 0.1 nG is seeded. The simulation shows how the magnetic field is channelled along the filaments of gas and is concentrated and amplified within galaxy clusters. Comparison with the values expected from pure compression reveals an additional amplification of the magnetic field caused by turbulence in the central region of the cluster. Values of the order of ∼1µG are obtained in clusters at z ∼ 0 with median relative errors of ∇ · B below 0.4 per cent. The implications of a proper description of the dynamics of the magnetic field and their possible observational counterparts in future facilities are discussed.

Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity

Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative.

Genetic distance of maize inbred lines based on SSR markers for prediction of heterosis and combining ability

Genetic distance among six elite maize inbred lines was analyzed using SSR markers. Hybrid progeny obtained by crossing inbred lines according an incomplete diallel design was tested in field trials together with inbred lines per se.The objective of this study was to determine genetic distance of inbred maize lines and to examine if a significant correlation exist between the genetic distance of parental lines and the exhibited high parent heterosis (HPH) and specific combining abilities (SCA) for grain yield, ear lenght, kernel row numberand number of kernels per row. Twenty one SSR primers were used for genetic assesment of inbreds with detected 92 alleles. Genetically most distant lines were ZPL1 and ZPL5 and ZPL6 with the GD value of 0.549, while the closest one were ZPL2 and ZPL3 with GD value of 0.11. The dendrogram distinguished two main groups of inbreds: ZPL5 and ZPL6 grouped in a smaller cluster and ZPL1, ZPL2, ZPL3 and ZPL4 forming the second cluster. Values of the Spearman?s rank correlation coefficient between genetic distance among inbred lines based on SSR markers and SCA for all analyzed traits were positive and significant with the exception of rows per ear. Highest correlation was exhibited between the genetic distance and SCA for number of kernels per row (0.643). Spearman?s rank correlation coefficient between GD and high parent heterosis was positive and significant for ear length and kernel number in row with coefficient values of 0.554 and 0.611, respectively.

Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning

Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster values with different granularities. It further models the couplings in value clusters within the same granularity and with different granularities to embed feature values into a new numerical space with independent dimensions. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods.