scholarly journals Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering

KOMTEKINFO ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 1-9
Author(s):  
Andri Nofiar ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Palm Oil ◽  
Laboratory Testing ◽  
Quality Data ◽  
Test Results ◽  
Clustering Method ◽  
Data Mining Approach ◽  
Cluster 2

The classification of the quality of palm oil in PT Tasma Puja is still done by laboratory testing and then the data is saved manually in Excel. The method of grouping takes time and allows data to be lost. With the development of knowledge, it can be replaced by a data mining approach that can be used to classify the quality of palm oil based on its standards. The k-Means clustering method can be applied to classify the quality of palm oil based on water, dirt and free fatty acids. The data used is the quality data of palm oil in December 2017 as many as 31 data with criteria of good, very good and not good. The test results contained 3 clusters, namely cluster 0 for good categories amounted to 12 data, cluster 1 for very good category amounted to 13 data and cluster 2 for less good categories amounted to 6 data. The k-Means clustering method can be used for data processing using the concept of data mining in grouping data according to criteria.

scholarly journals Goods Stock Management using the K-Means Algorithm Method

2020 ◽  
Vol 10 (1) ◽  
pp. 22-45
Author(s):  
Dhio Saputra
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Test Results ◽  
Stock Management ◽  
Clustering Method ◽  
Sales Data ◽  
Using Data ◽  
Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

scholarly journals Analisis Cluster Hasil Uji Kompetensi Lembaga Sertifikasi Profesi (LSP) Melalui Teknologi Data Mining

2021 ◽  
Vol 19 (2) ◽  
pp. 76-81
Author(s):  
Raditya Danar Dana ◽  
Ahmad Faqih
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Data Cleaning ◽  
Quality Of Education ◽  
Data Mining Approach ◽  
Collection Data ◽  
Competency Tests ◽  
Cluster 2 ◽  
Made In

The implementation of the Competency Test at the LSP institution in higher education is an effort to ensure that students have abilities in certain fields according to predetermined competency standards. Education providers are required to always strive to improve the quality and quality of education with the aim that the student's academic performance will always improve. From the results of observations made in the research location, it was found a problem with the high number of failures in the implementation of the competency test. This study aims to conduct cluster analysis of the data resulting from the implementation of competency tests with the Data Mining approach through several stages in the form of data collection, data cleaning, data transformation, data modeling and data evaluation. This study resulted in grouping the results of competency tests which were divided into 3 clusters, namely cluster 1 as much as 38%, cluster 2 as much as 32% and cluster 3 as much as 30%..

Fuzzy Miner

2011 ◽  
pp. 299-321 ◽  
Author(s):  
Nikos Pelekis ◽  
Babis Theodoulidis ◽  
Ioannis Kopanakis ◽  
Yannis Theodoridis
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Internet Routing ◽  
Data Mining Approach ◽  
New Information

QOSP Quality of Service Open Shortest Path First based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their appli-cations for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute-selection prob-lem in internet routing.

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

Finding the Most Significant Elements for the Classification of Organic Orange Leaves: A Data Mining Approach

2017 ◽  
Vol 50 (14) ◽  
pp. 2292-2307 ◽  
Author(s):  
Camila Maione ◽  
Christian Turra ◽  
Elisabete A. De Nadai Fernandes ◽  
Márcio Arruda Bacchi ◽  
Fernando Barbosa ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Approach

scholarly journals A Data Mining Approach to In Vivo Classification of Psychopharmacological Drugs

2008 ◽  
Vol 34 (3) ◽  
pp. 607-623 ◽  
Author(s):  
Neri Kafkafi ◽  
Daniel Yekutieli ◽  
Greg I Elmer
Keyword(s):  
Data Mining ◽  
Data Mining Approach

scholarly journals Comparison of European Standard Patch Test Results of 330 Patients from an Occupational Diseases Hospital

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Özge Gündüz ◽  
Aslı Aytekin ◽  
Engin Tutkun ◽  
Hınç Yılmaz
Keyword(s):  
Patch Test ◽  
Preventive Measures ◽  
Potassium Dichromate ◽  
Standard Test ◽  
Working Environment ◽  
Test Results ◽  
European Standard ◽  
Household Activities

Background and Aim.Contact dermatitis (CD) is the most prevalent occupational skin disease with a significant impact on quality of life. Patch testing is used for the identification of responsible allergens which may improve protective and preventive measures in the workplace. Herein, we aim to identify the demographic characteristics and occupation of patients with early diagnosis of occupational CD and compare patch test results.Materials and Methods.The study included 330 patients referred to our clinic between April 2009 and April 2011 and who were patch-tested with 28-allergen European Standard Test.Results.126 (38%) patients were female and 204 (62%) were male with a mean age of 36.12 (±13.13) years. Positive allergic reactions were observed in 182 (55%) patients. Nickel sulphate (41/126) and potassium dichromate (39/204) were significantly the most common allergens in women and men, respectively (P<0.005). Additionally, the most common occupation in women was household activities (83/126) and in men was manufacturing (80/204).Conclusion.The allergens to which people become sensitized differ according to their working environment and occupation. Classification of occupations is important for identification of sensitization risks and monitoring of changes in allergen distribution of different occupations.

Multiple Trait Selection and Classification of Genotype — A Data Mining Approach

2003 ◽  
Vol 02 (03) ◽  
pp. 445-457 ◽  
Author(s):  
Chien-Hsiung Lin ◽  
Yi-Hsin Liu
Keyword(s):  
Data Mining ◽  
Mathematical Programming ◽  
Programming Approach ◽  
Fuzzy Mathematical Programming ◽  
Real Numbers ◽  
Multiple Trait ◽  
Data Mining Approach ◽  
Mathematical Programming Approach ◽  
Genotype A

A set of data represented by a set of real numbers can be handled by the computer much easier than non-real valued data. This paper develops bicriteria linear program solution through a fuzzy mathematical programming approach which assigns a real number to each member of the data. This method integrates data information and the decision maker's objective opinion to construct a tool (function) of selection and classification.

scholarly journals Creating Informative Data Warehouses: Exploring Data and Information Quality through Data Mining

10.28945/2584 ◽  
2002 ◽  
Author(s):  
Herna L. Viktor ◽  
Wayne Motha
Keyword(s):  
Data Mining ◽  
Information Quality ◽  
Data Warehousing ◽  
Poor Quality ◽  
Quality Data ◽  
Data Warehouses ◽  
Quality Of Data ◽  
Poor Quality Data ◽  
Daunting Task

Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.

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