scholarly journals Penerapan Metode Association Rule Untuk Menganalisa Pola Pemakaian Bahan Kimia Di Laboratorium Menggunakan Algoritma FP-Growth (Studi Kasus di Laboratorium Kimia PT. PLN (Persero) Sektor Pembangkitan Belawan Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 56
Author(s):  
Buyung Solihin Hasugian
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Several Variables ◽  
Alternative Algorithm ◽  
Chemical Usage ◽  
Tree Development

<p class="Default"><em>The pattern of using chemicals in the laboratory of PT. PLN (Persero) </em><em>Sektor Pembangkitan </em><em>Belawan Medan is not only to find out what chemicals are used but also to find out the amount of chemicals left so that laboratory officials can properly manage the use of these chemicals. One appropriate way to determine the pattern of use of these chemicals is to use data mining techniques. The Data Mining technique used in this case is the FP-Growth Algorithm. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a data set. The study was conducted using several variables, namely the date and chemicals used. The results of this study are in the form of a chemical usage pattern which is processed using software, namely implementing the FP-Growth algorithm using the concept of FP-Tree development in searching for Frequent Itemset.</em></p><p class="Default"><em> </em></p><pre><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth</em></pre>

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN METODE ASSICIATION RULE DENGAN ALGORITMA APRIORI UNTUK ANALISA POLA PENJUALAN BARANG

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 193-198
Author(s):  
Yori Apridonal M ◽  
Wirdah Choiriah ◽  
Akmal Akmal
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Data Set ◽  
Processing Data ◽  
New Information ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Events Data

Abstract: Fantasy Kids is a children's clothing distribution in the Bangkinang area, Kampar Regency, Riau. In its operations, distros sell their products to the general public, including the sale of children's shirts, children's shirts, jackets or children's sweaters which are usually sold in other distros. These distributions carry out product updates at certain events. Data Mining is the development or discovery of new information by looking for certain patterns or rules of a large amount of data expected to overcome these conditions. The method that will be used in the construction of this application is the Association Rule method with the Apriori Algorithm. Association Rule method is a procedure to find relationships between items in a specified data set. In determining a Association Rule, there is a measure of trust obtained from the results of processing data with certain calculations. Apriori Algorithm is an alternative Algorithm that can be used to determine the frequent itemset in a data set. Keywords : Data Mining, Algoritma, Apriori, Association Rule, Sales, Distro  Abstrak: Fantasy Kids merupakan sebuah distro baju anak-anak di kawasan Bangkinang, Kabupaten Kampar, Riau. Dalam operasionalnya, distro menjual produknya kepada masyarakat umum meliputi penjualan kaos anak, kemeja anak, bag, jaket atau sweater anak yang biasa dijual di distro-distro lainnya. Distro ini melakukan pembaruan produk pada event tertentu. Data Mining merupakan pegembangan atau penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar diharapkan dapat mengatasi kondisi tersebut. Metode yang akan digunakan dalam pembangunan aplikasi ini adalah metode Association Rule dengan Algoritma Apriori. Metode Association Rule adalah suatu prosedur untuk mencari hubungan antara item dalam suatu kumpulan data yang ditentukan. Dalam menentukan suatu Association Rule, terdapat suatu ukuran kepercayaan yang di dapatkan dari hasil pengolahan data dengan perhitungan tertentu. Algoritma Apriori merupakan salah satu alternatif Algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam suatu kumpulan data. Kata kunci: Data Mining, Algoritma, Apriori, Association Rule, Penjuaan, Distro

scholarly journals The Observation Report of Red Blood Cell Morphology in Thailand Teenager by Using Data Mining Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Sarawut Saichanma ◽  
Sucha Chulsomlee ◽  
Nonthaya Thangrua ◽  
Pornsuri Pongsuchart ◽  
Duangmanee Sanmun
Keyword(s):  
Data Mining ◽  
Blood Cell ◽  
Hematological Parameters ◽  
Medical Science ◽  
Peripheral Blood Smear ◽  
Medical Laboratory ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Laboratory Results

It is undeniable that laboratory information is important in healthcare in many ways such as management, planning, and quality improvement. Laboratory diagnosis and laboratory results from each patient are organized from every treatment. These data are useful for retrospective study exploring a relationship between laboratory results and diseases. By doing so, it increases efficiency in diagnosis and quality in laboratory report. Our study will utilize J48 algorithm, a data mining technique to predict abnormality in peripheral blood smear from 1,362 students by using 13 data set of hematological parameters gathered from automated blood cell counter. We found that the decision tree which is created from the algorithm can be used as a practical guideline for RBC morphology prediction by using 4 hematological parameters (MCV, MCH, Hct, and RBC). The average prediction of RBC morphology has true positive, false positive, precision, recall, and accuracy of 0.940, 0.050, 0.945, 0.940, and 0.943, respectively. A newly found paradigm in managing medical laboratory information will be helpful in organizing, researching, and assisting correlation in multiple disciplinary other than medical science which will eventually lead to an improvement in quality of test results and more accurate diagnosis.

scholarly journals Application of Data Mining Technique in Stock Market : An Analysis

2014 ◽  
pp. 294-295
Author(s):  
SACHIN KAMBEY ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Association Rule ◽  
Movement Direction ◽  
Data Mining Technique ◽  
Business World ◽  
Mining Technique ◽  
Hidden Patterns ◽  
Technology Tool ◽  
And Behavior

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

scholarly journals Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

2011 ◽  
Vol 26 (3) ◽  
pp. 337-353 ◽  
Author(s):  
Ruixin Yang ◽  
Jiang Tang ◽  
Donglian Sun
Keyword(s):  
Data Mining ◽  
Tropical Cyclone ◽  
Association Rule ◽  
Tropical Storms ◽  
Rapid Intensification ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Intensity Prediction ◽  
Intensity Changes ◽  
Tropical Depressions

Abstract This study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensifying hurricanes are more strongly associated with lower convergence values. The mined association rules showed that cofactors usually display higher-intensity prediction power in the stratified TC groups. The data mining results also identified a predictor set with fewer factors but improved probabilities of rapid intensification. This study found that the data mining technique not only sheds light on the roles of multiple-associated physical processes in tropical cyclone development—especially in rapid intensification processes—but also will help improve TC intensity forecasting. This paper provides an outline on how to use this data mining technique and how to overcome low occurrences of mined conditions in order to improve TC intensity forecasting capabilities.

scholarly journals Penerapan Data Mining Association Rule Menggunakan Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel

2021 ◽  
Vol 5 (2) ◽  
pp. 112-121
Author(s):  
Guntoro Guntoro ◽  
Charles Parmonangan Hutabarat
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Data Gathering ◽  
Engine Oil ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Spark Plug ◽  
Gathering Data ◽  
Mining Association Rule

Many individuals are interested in starting a workshop. By responding to each customer's desires, the workshop company may continue to develop, and so the data mining technique can address this challenge. The FP-Growth algorithm is one of the methods that may be used to determine the stock availability of automotive spare components such as engine oil, spark plugs, oil filters, ac filters, batteries, tires, and so on. This research is divided into four stages: problem identification, data gathering, data processing, and data testing. Based on the results of the testing, AK (Battery), OM (Engine Oil), and BS (Spark plug) received support values of 33% and 80%, respectively. Furthermore, the BN (Ban) and KR (Kampas Bram) values were found with 33% support and 80% confidence. Furthermore, we obtain AK (Battery) and OM (Engine Oil) with 33% support and 80% confidence, and BN (Tires) and OM (Engine Oil) with 33% support and 80% confidence. OM (Engineering Oil), AK (Battery), and BS (Battery Storage) are the abbreviations for the terms OM (Engineering Oil), AK (Battery), and BS (Battery (Spark plug)).

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