scholarly journals Desain Model Data Mining pada Model SECI untuk Pemetaan dan Ekstraksi Pengetahuan Kompetensi Lulusan

2021 ◽  
Vol 8 (3) ◽  
pp. 1607-1614
Author(s):  
Mardiani Mardiani
Keyword(s):  
Data Mining ◽  
Data Transfer ◽  
Model Data ◽  
Clustering Model

Manajemen pengetahuan menggunakan Model SECI membantu dalam transfer pengetahuan tacit dan eksplisit. Keterbatasan kemampuan sumber daya manusia dalam transfer pengetauan membutuhkan alat bantu dalam prosesnya. Ekstraksi pengetahuan dapat dilakukan dengan implementasi data mining. Hasil keluaran data mining yang besar akan dimanfaatkan oleh dunia pendidikan untuk tujuan strategis, misalnya evaluasi penyusunan profil lulusan dari hasil analisis kompetensi lulusan. Kurikulum Program Studi disusun berdasarkan profil Lulusan dan Program Studi membutuhkan pemetaan kebutuhan dari data alumni dalam menyusun kurikulum, sementara alumni membutuhkan mata kuliah yang mendukung setelah selesai kuliah. Manajemen Pengetahuan menampung pengetahuan dari lulusannya, sementara Data mining digunakan sebagai alat dalam mengolah data. Transfer pengetahuan dan pengolahan data kompetensi lulusan, dan memungkinkan munculnya pengetahuan baru bagi perguruan tinggi yang bisa dimanfaatkan dalam proses penyusunan kurikulum berikutnya. Model yang digunakan adalah SECI dikombinasikan dengan algoritma klasifikasi dan clustering. Model SECI yang sudah dipetakan alat bantu teknologinya pada setiap prosesnya, dibuat lebih jelas dan spesifik pengelompokkannya dengan implementasi Data Mining pada setiap kuadran Model SECI. Desain model SECI yang dikombinasikan dengan teknologi Data Mining akan memperbaiki kekurangan yang terdapat pada model sebelumnya.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

scholarly journals Mining multiple sources of historical data: The example of a standardized dataset of medieval monasteries and convents in France

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Adam Mertel ◽  
David Zbíral
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Data Model ◽  
Computational Analysis ◽  
Historical Data ◽  
Model Data ◽  
Multiple Sources ◽  
The Public ◽  
Comprehensive Collection ◽  
Online Databases

<p><strong>Abstract.</strong> In this paper, we present a dataset of medieval monasteries and convents on the territory of today’s France and discuss the workflow of its integration. Spatial historical data are usually dispersed and stored in various forms &amp;ndash; encyclopedias and catalogues, websites, online databases, and printed maps. In order to cope with this heterogeneity and proceed to computational analysis, we have devised a method that includes the creation of a data model, data mining from sources, data transformation, geocoding, editing, and conflicts solving.</p><p> The resulting dataset is probably the most comprehensive collection of records on medieval monasteries within the borders of today’s France. It can be used for understanding the spatial patterns of medieval Christian monasticism and the implantation of the official Church infrastructure, as well as the relation between this official infrastructure and phenomena covered in other datasets. We open this dataset, as well as scripts for mining, to the public (https://github.com/adammertel/dissinet.monasteries) and provide a map tool to visualize, filter, and download the records (http://hde.geogr.muni.cz/monasteries).</p>

Using Grids for Distributed Knowledge Discovery

2011 ◽  
pp. 284-298 ◽  
Author(s):  
Antonio Congiusta ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
High Performance ◽  
Data Transfer ◽  
Grid Services ◽  
Distributed Knowledge ◽  
Data Intensive ◽  
Knowledge Grid ◽  
Complex Knowledge ◽  
High Level

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.

Data Transfer from Heterogeneous CAD to Division Mockup Virtual Prototype

2007 ◽  
Vol 10-12 ◽  
pp. 456-459 ◽  
Author(s):  
B. Yuan ◽  
Wei Xiong ◽  
Zu Wen Wang
Keyword(s):  
Virtual Reality ◽  
Design Process ◽  
Data Transfer ◽  
Virtual Prototype ◽  
Digital Model ◽  
Cad Model ◽  
Model Data ◽  
Transfer Data ◽  
Virtual Prototype Technology ◽  
Prototype Software

Virtual prototype technology could integrate different CAD formats parts into an digital model. In this way the design process will be more efficient. This issue reviewed three methods to transfer data from CAD parts to virtual reality element. And find an efficient way to transfer different CAD model data to virtual prototype software Division MockUp.

Data Mining Model in Regional Target Decomposition of Water Resources Utilization Efficiency

2014 ◽  
Vol 580-583 ◽  
pp. 2082-2087 ◽  
Author(s):  
Hua Wei Chen ◽  
Ji Wen Huang ◽  
Bing Li ◽  
Shi Dong Fu ◽  
Xin Zhang
Keyword(s):  
Data Mining ◽  
Water Resources ◽  
Shandong Province ◽  
Utilization Efficiency ◽  
Water Withdrawal ◽  
Industrial Water ◽  
Water Utilization ◽  
Clustering Model ◽  
Mining Model ◽  
Municipal Level

Data mining model is the most important technical basis of the control target decomposition for the most stringent water resources management of Shandong province. K-means clustering model is adopted to analysis the water withdrawal of industrial added value per ten thousand yuan in 2010. Based on the yearly industrial water consumption trend from 1995 to 2010 of 17 municipal-level cities in Shandong province, the ARIMA (p, d, q) model is established through a lot of fitting and optimization and then the regional industrial water demand and water utilization efficiency in 2015 were forecasted. According to the proposed principal and technical route of target decomposition, the industrial water utilization efficiency target in 2015 of the whole province and 17 municipal-level cities are defined respectively.

scholarly journals Clustering of Drug Sampling Data to Determine Drug Distribution Patterns with K-Means Method : Study on Central Kalimantan Province, Indonesia

2019 ◽  
Vol 5 (2) ◽  
pp. 208
Author(s):  
Wahyuri Wahyuri ◽  
Umi Athiyah ◽  
Ira Puspitasari ◽  
Yunita Nita
Keyword(s):  
Data Mining ◽  
Drug Distribution ◽  
Distribution Patterns ◽  
Good Manufacturing Practice ◽  
Depth Information ◽  
National Agency ◽  
Data Mining Technique ◽  
Central Kalimantan ◽  
Clustering Model ◽  
Post Marketing

Background: Drug sampling and testing in the context of post-marketing control is an important component to ensure drug safety in the supply chains. The results are used by the Indonesian National Agency for Drug and Food Control (NA-FDC) for conducting public warnings, evaluating the Good Manufacturing Practice (GMP) and Good Distribution Practice (GDP) implementation, and enforcing the law against drug violation.Objective: This study aimed to identify and analyze drug distribution patterns to provide an overview of drug sampling in the public sector. Methods: The data was collected from Balai Besar Pengawas Obat dan Makanan (BBPOM) Palangka Raya’s database. The collected data were the drug sampling data from Integrated Information Reporting Systems (IIRS) application from 2014 to 2018. Next, we employed CRISP-DM methodology to analyze the data and to identify the pattern. K-means clustering model was selected for data modeling.Results: The dataset contained five attributes, i.e., drug name, therapeutic classes, district/city, sample category, and evaluation of drug surveillance. The drug distribution pattern formed three clusters. First cluster contained 522 drug items in eight therapeutic classes and spread over ten districts, second cluster contained 1542 drug items in five therapeutic classes and spread over five districts, and third cluster contained 503 drug items in eleven therapeutic classes and spread across nine districts.Conclusion: To conclude, the applied data mining technique has improved the decision on the drug sampling planning. It also provides in-depth information on the improvement of drug post-marketing control performance in Central Kalimantan Province.Keywords: Clustering, CRISP-DM, Data Mining, Drug distribution patterns, Drug quality control, Drug sampling

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