scholarly journals Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means

2021 ◽  
Vol 6 (3) ◽  
pp. 153
Author(s):  
Iis Setyawan Mangku Negara ◽  
Purwono Purwono ◽  
Imam Ahmad Ashari
Keyword(s):  
Cluster Data

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

scholarly journals Non-negative sparse Laplacian regularized latent multi-view subspace clustering

2021 ◽  
Vol 15 ◽  
pp. 174830262110249
Author(s):  
Cong-Zhe You ◽  
Zhen-Qiu Shu ◽  
Hong-Hui Fan
Keyword(s):  
Subspace Clustering ◽  
Structural Features ◽  
Space Representation ◽  
Graph Regularization ◽  
Structure Information ◽  
Cluster Data ◽  
Latent Space ◽  
Low Dimensional ◽  
Relationship Of ◽  
The Relationship

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

scholarly journals Reanalysis of nearby open clusters using Gaia DR1/TGAS and HSOY

2018 ◽  
Vol 615 ◽  
pp. A12 ◽  
Author(s):  
Steffi X. Yen ◽  
Sabine Reffert ◽  
Elena Schilbach ◽  
Siegfried Röser ◽  
Nina V. Kharchenko ◽  
...  
Keyword(s):  
Milky Way ◽  
Open Cluster ◽  
Star Clusters ◽  
Open Clusters ◽  
Parameter Estimates ◽  
Cluster Membership ◽  
Fitting Procedure ◽  
Cluster Data ◽  
The Galaxy ◽  
Automatic Tool

Context. Open clusters have long been used to gain insights into the structure, composition, and evolution of the Galaxy. With the large amount of stellar data available for many clusters in the Gaia era, new techniques must be developed for analyzing open clusters, as visual inspection of cluster color-magnitude diagrams is no longer feasible. An automatic tool will be required to analyze large samples of open clusters. Aims. We seek to develop an automatic isochrone-fitting procedure to consistently determine cluster membership and the fundamental cluster parameters. Methods. Our cluster characterization pipeline first determined cluster membership with precise astrometry, primarily from TGAS and HSOY. With initial cluster members established, isochrones were fitted, using a χ2 minimization, to the cluster photometry in order to determine cluster mean distances, ages, and reddening. Cluster membership was also refined based on the stellar photometry. We used multiband photometry, which includes ASCC-2.5 BV, 2MASS JHKs, and Gaia G band. Results. We present parameter estimates for all 24 clusters closer than 333 pc as determined by the Catalogue of Open Cluster Data and the Milky Way Star Clusters catalog. We find that our parameters are consistent to those in the Milky Way Star Clusters catalog. Conclusions. We demonstrate that it is feasible to develop an automated pipeline that determines cluster parameters and membership reliably. After additional modifications, our pipeline will be able to use Gaia DR2 as input, leading to better cluster memberships and more accurate cluster parameters for a much larger number of clusters.

scholarly journals A general Cluster data and global MHD simulation comparison

2008 ◽  
Vol 26 (11) ◽  
pp. 3411-3428 ◽  
Author(s):  
P. Daum ◽  
M. H. Denton ◽  
J. A. Wild ◽  
M. G. G. T. Taylor ◽  
J. Šafránková ◽  
...  
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Mhd Simulation ◽  
Global Context ◽  
Cluster Data ◽  
Verification Methods ◽  
Modelling Studies ◽  
Global Mhd Simulation ◽  
The Many

Abstract. Among the many challenges facing the space weather modelling community today, is the need for validation and verification methods of the numerical models available describing the complex nonlinear Sun-Earth system. Magnetohydrodynamic (MHD) models represent the latest numerical models of this environment and have the unique ability to span the enormous distances present in the magnetosphere, from several hundred kilometres to several thousand kilometres above the Earth's surface. This makes it especially difficult to develop verification and validation methods which posses the same range spans as the models. In this paper we present a first general large-scale comparison between four years (2001–2004) worth of in situ Cluster plasma observations and the corresponding simulated predictions from the coupled Block-Adaptive-Tree-Solarwind-Roe-Upwind-Scheme (BATS-R-US) MHD code. The comparison between the in situ measurements and the model predictions reveals that by systematically constraining the MHD model inflow boundary conditions a good correlation between the in situ observations and the modeled data can be found. These results have an implication for modelling studies addressing also smaller scale features of the magnetosphere. The global MHD simulation can therefore be used to place localised satellite and/or ground-based observations into a global context and fill the gaps left by measurements.

scholarly journals Penetapan Instruktur Diklat Menggunakan Metode Clustering K-Means dan Topsis Pada PT PLN (Persero) Udiklat Jakarta

Petir ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 111-121
Author(s):  
Nurul Dyah Budiana ◽  
Riki Ruli A. Siregar ◽  
Meilia Nur Indah Susanti
Keyword(s):  
Study Data ◽  
Topsis Method ◽  
Group Approach ◽  
Application System ◽  
Cluster Data ◽  
Training Materials ◽  
Group Data ◽  
Learning Development ◽  
Work Done ◽  
Data Group

Instructor is the main aspect that exists in the implementation of the training. The increasing number of instructors and the need for training is also increasing every year there is no system that can help the process of determining quickly and precisely. In need of a method that can classify the instructor data in accordance with the title of training materials and can be assigned instructor each of the training materials and do not ignore aspects of assessment of the instructor. In this study data mining techniques are used to help recommend instructors for each subject matter of the training based on the cluster data group approach. So it can be used in determining the instructor's assignment per training materials in the future. K-Means clustering method is used to group data into clusters by looking at the centroid  value that has been determined. And the Topsis method is used to assign one instructor's name through the rankings of preference values. In this research CRISP-DM method is used as software engineering method system work done in sequence or linearly. In the testing process has been generated if the manual data and data processing if the application system is the same. This application to facilitate the Supervisor and Learning Development staff in setting instructors per training materials.

scholarly journals ANALISA CLUSTERING PADA DATA PELANGGARAN LALULINTAS DI PENGADILAN NEGERI DUMAI DENGAN MENGGUNAKAN METODE K-MEANS

2019 ◽  
Vol 4 (2) ◽  
pp. 1
Author(s):  
Elisawati Elisawati ◽  
Deasy Wahyuni ◽  
Adi Arianto
Keyword(s):  
Clustering Analysis ◽  
Motor Vehicle ◽  
District Court ◽  
Central Cluster ◽  
The Road ◽  
The Poor ◽  
Cluster Data ◽  
Traffic Violations ◽  
On The Road

The order of traffic on the road is very important for motorists on the highway, the lack of awareness of motor vehicle users and the poor drivers of traffic discipline make the level of traffic violations in driving on the highway always increase so that the number of ticket data received by the Dumai District Court. This research was conducted to analyze and classify data violations using the k-means method to facilitate knowing the types of violations that are often violated by vehicle users. The attributes to be analyzed are the types of violations and types of vehicles. The test was carried out using the Rapidminer 5 application where the data tested was data from the Dumai District Court on December 2017, as many as 616 violations. Central cluster data consists of 3 clusters, namely C1 = Many, C2 = moderate and C3 = few who commit traffic violations. So the results of the data obtained where C1 produces 1 data, C2 gets as much as 4 data and C3 as many as 7 data. Where the type of violation that is often violated is the type of violation that does not use a helmet and the type of vehicle is a motorcycle. From the results of this study can be used or can be followed up with the holding of socialization to reduce the number of traffic violations. Keywords: Clustering Analysis, K-Means, Traffic Violations, Rapidminer

scholarly journals CHEMICAL CHARACTERIZATION OF ATMOSPHERIC PARTICLES IN URBAN AREAS OF RIO GRANDE DO SUL, BRAZIL-PRELIMINARY STUDY

2006 ◽  
Vol 14 (14) ◽  
pp. 1-16
Author(s):  
E. Teixeira ◽  
J. Fachel Braga ◽  
J.D. D. Migliavacca ◽  
M.L.L.Fomoso Sanchez
Keyword(s):  
Chemical Composition ◽  
Urban Areas ◽  
Chemical Characterization ◽  
Rio Grande ◽  
Atmospheric Particles ◽  
Rio Grande Do Sul ◽  
Power Stations ◽  
Cluster Data ◽  
Steel Industries

This work reports the determination of the concentration and chemical composition of atmospheric particles in the urban districts of Charqueadas and Sapucaia do Sul, State of Rio Grande do Sul, Brazil. Chemical composition, morphology, and particle size were analyzed using a scanning electron microscope with energy dispersive x=ray microanalysis (SEM-EDS). Cluster analysis showed that there were six types of particles: Fe-Zn, Fe, Si=Al, Si, Ca-S, and Na. Factorial analysis from cluster data showed that particles rich in Fe-Zn, Si-Al, and Ca-S appeared more frequently, indicating anthropogenic influence (vehicles, steel plants, coal-fired power stations). The experimental results and consideration of the wind directions show that the main source of pollution in Charqueadas appears to be due to coal mining and steel industries, while in Sapucaia do Sul due to steel plants and vehicles.

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