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)).

scholarly journals IMPLEMENTASI DATA MINING DALAM ANALISA POLA PEMINJAMAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

JURTEKSI ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 89-96
Author(s):  
Edi Kurniawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Growth Data ◽  
Data Mining Technique ◽  
Transaction Data ◽  
Mining Technique ◽  
New Information ◽  
Important Means ◽  
Mining Association Rule

Abstract: The library is one of the most important means to add insight and knowledge to everyone. In general, borrowing transaction data books that exist in a library are only left to accumulate by the library in the database without any utilization or further processing of the data that has long been stored. By utilizing the Data Mining technique using association rules with FP-Growth, these data will be very useful. Because from the data lending books to the library, new information can be gleaned about what books are often borrowed and know the pattern of relationships between books that have been borrowed together so that later it can be used to compile books in accordance with the existing borrowing patterns so that they can facilitate library visitors in the process of finding books. Keywords: Data Mining, Association Rule, FP-Growth, Library Abstrak: Perpustakaan merupakan salah satu sarana yang sangat penting untuk menambah wawasan dan keilmuan setiap orang. Pada umumnya data transaksi peminjaman buku yang ada pada sebuah perpustakaan hanya dibiarkan saja menumpuk oleh pihak perpustakaan di dalam database tanpa ada pemanfaatan atau pengolahan lebih lanjut dari data-data yang telah lama tersimpan tersebut. Dengan melakukan pemanfaatan menggunakan Teknik Data Mining metode association rules dengan FP-Growth, data-data tersebut akan jadi sangat bermanfaat. Karena dari data peminjaman buku pada perpustakaan tersebut dapat diggali informasi baru tentang buku-buku apa yang sering dipinjam dan mengetahui pola hubungan antara buku yang telah dipinjam secara bersama-sama sehingga nantinya dapat dimanfaatkan untuk melakukan penyusunan buku sesuai dengan pola peminjaman buku yang ada sehingga dapat mempermudah para pengunjung perpustakaan dalam proses pencarian buku. Kata Kunci : Data Mining, Asociation Rule, FP-Growth, Perpustakaan

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 Clustering k-means untuk menentukan zona pasar tanah pada penilaian KJPP Rija Husaeni dan Rekan

JURNAL WIDYA ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 210-220
Author(s):  
Elsa Fitri Surya
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Land Market ◽  
Fixed Costs ◽  
Market Information ◽  
Data Mining Technique ◽  
Market Data ◽  
Mining Technique ◽  
Published Report

One of the jobs is Appraisal Service, whice is determined by this fee is fixed costs such as : land, buildings, vehicles, machinery, and other buildings. In this report, one of which requires information as information as management and producing reports, in KJPP Rija Husaeni and Partners, the published production reports are recapitulated by the admin as market information data, currently market data information is recapitulated by the admin division in the form of The data entered is based on published report numbers and has not been grouped based on the area’s land market zone to make it easier if there are employees of KJPP Rija Husaeni and Partners who need information about the area’s land market zone. On the basis of identifying the problems described above, a data processing process using a data mining technique is required. The data mining technique used in this research is clustering with the K-Means algorithm to determine the land market zone in the KJPP Rija Husaeni and Partners assessment.

scholarly journals PENERAPAN METODE ASSOCIATION RULE MENGGUNAKAN ALGORITMA APRIORI PADA SIMULASI PREDIKSI HUJAN WILAYAH KOTA BANDUNG

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohamad Fauzy ◽  
Kemas Rahmat Saleh W ◽  
Ibnu Asror
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Weather Forecast ◽  
Weather Data ◽  
Rule Mining ◽  
Similarity Relationships ◽  
Mining Association Rule

[Id] Prakiraan cuaca saat ini telah menjadi satu hal yang dibutuhkan bagi banyak orang di dunia. Dalam memprediksi hujan pengolahan data cuaca merupakan hal yang penting. Namun permasalahannya, data cuaca yang semakin hari semakin bertambah menyebabkan penumpukan data sehingga pengolahan data tersebut perlu penanganan lebih lanjut. Oleh karena itu pemanfaatan data mining digunakan untuk menyelesaikan masalah ini. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Penelitian ini dilakukan dengan tiga tahapan utama yaitu : 1) melakukan analisa pola frekuensi tinggi menggunakan algortima apriori; 2) pembentukan aturan asosiasi (association rule); 3) uji kekuatan rule yang terbentuk dengan menghitung lift ratio pada masing-masing rule. Dataset yang digunakan adalah data klimatologi yang diambil dari BMKG stasiun geofisika kelas 1 Bandung. Hasil akhir dari Penelitian ini berupa aturan-aturan asosiasi (association rules) dimana aturan-aturan ini dapat dijadikan sebagai acuan dalam memprediksi cuaca hujan atau tidak hujan untuk satu hari kedepan. Kata kunci : Data mining, association rule, apriori, prediksi hujan [En] Weather forecast today has become a necessary thing for many people in the world. In predicting rain weather data processing is essential. But the problem, weather data that is increasingly growing cause the accumulation of data so that the data processing needs further treatment. Therefore, the use of data mining is used to solve this problem. Association rule mining is one of data mining methods that can identify similarity relationships between items. This research is performed by three main stages, namely: 1) to analyze high frequency patterns using algorithms priori; 2) the establishment of an association rule (association rule); 3) test the strength of the rule which is formed by calculating the ratio elevator on each rule. The dataset used is the climatological data taken from BMKG station 1st class geophysical Bandung. The end result of this research in the form of rules of association (association rules) in which these rules can be used as a reference in predicting the weather is rain or not rain for the next day. Keywords : data mining, association rule, apriori, rain forecast

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>

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