scholarly journals Reduce Time Process Using Apriori Algorithm On K-Wayjoin Based To Find Retail Business Data Relationship Pattern

2019 ◽  
Vol 1 (1) ◽  
pp. 12-22
Author(s):  
Joko Aryanto ◽  
Yuli Asriningtyas
Keyword(s):  
Data Mining ◽  
Query Language ◽  
Market Competition ◽  
Query Languages ◽  
Research Approach ◽  
Apriori Algorithm ◽  
Time Process ◽  
Business People ◽  
Retail Business ◽  
Short Time

The process of running a trading business, businesses must always update information on market competition that occurs. Running a trading business is not just opening a business place and waiting for consumers to shop. Consumers will lightly come to shopping places to shop for various reasons. From cheap prices, attractive arrangements, large parking lots, to the ease of finding items to buy. From these problems, business people will compete on how to easily attract customers to their place of business. One way to solve these problems is by structuring merchandise with the aim of customers to easily get the items they are looking for. One way to solve these problems is by structuring merchandise with the aim of customers to easily get the items they are looking for. Arrangements that are made do not originate from arranging the location of goods according to taste but are carried out on the basis of trends or trends in goods purchased by consumers when shopping. This process is often referred to as the "Data Mining" process is one of the effective methods for finding consumers' preferences to choose the items they buy. This process can be completed using the Apriori algorithm. The principle used by the Apriori algorithm is that if an itemset appears frequently, then all subset of itemset must also appear frequently. This results in repeated checking and will require a short time. Problems that require a short time, a method is proposed, namely by developing to be able to reduce the travel time of the process. The length of time that occurs in the process of calculating the value of support and confidance and the repetition process to find the value. The method used is to manipulate the use of query languages with a k-way join research approach so that the optimal query language arrangement can be obtained. The results obtained in this study are that execution times are relatively faster, with the results of the same association rules as those produced by the Priori method without any development or modification.

scholarly journals Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

2020 ◽  
Vol 3 (2) ◽  
pp. 89
Author(s):  
Adie Wahyudi Oktavia Gama ◽  
Ni Made Widnyani
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Simple Modification ◽  
Scanning Process ◽  
Iteration Methods ◽  
Short Time

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;

scholarly journals Implementasi Algoritma Apriori Dalam Menentukan Penjualan Mobil Yang Paling Diminati Pada Honda Permata Serpong

2020 ◽  
Vol 4 (2) ◽  
pp. 302
Author(s):  
Dewi Anggraini ◽  
Sukmawati Anggraeni Putri ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Information Needs ◽  
Decision Makers ◽  
Accurate Information ◽  
Apriori Algorithm ◽  
Analysis Techniques ◽  
Business People ◽  
Car Sales ◽  
Two Parameters ◽  
Confidence Value

The development of information technology is growing rapidly so that it enters various fields, the need for fast, accurate and accurate information is needed. But the fact is that high information needs are not balanced by the presentation of adequate information. Business development and competition are increasingly complex because consumers are very perspective making business people have to be smart in reading situations. So that business people can make a prediction of consumer interest to be used as a prediction of the company in making a decision, and change a strategy that is most appropriate for consumers. Decision makers try to utilize the available data warehouse, this encourages the emergence of new branches of science to overcome the extraction of information in very large amounts of data. To find out which Honda cars are most in demand by consumers, Data Mining techniques are required using the Apriori Algorithm method, and supported by the Tanagra Application by examining sales data for 1 year. Data Mining is an amalgamation of data analysis techniques, while Apriori Algorithm is the most frequently used method because it is very simple, easy and most widely proposed by some researchers, because there are two parameters namely Support Value and Confidence Value. Then the prediction results of the study found that Honda's car sales that most demanded by consumers were Brio Satya, HRV, Mobillio, Jazz, and CRV

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 Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals Data mining untuk memprediksi prestasi siswa berdasarkan sosial ekonomi, motivasi, kedisiplinan dan prestasi masa lalu

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Heri Susanto ◽  
Sudiyatno Sudiyatno
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Multiple Regression ◽  
Economic Status ◽  
Predictive Analysis ◽  
Research Approach ◽  
Significance Level ◽  
Achievement Prediction ◽  
Interaction Detection ◽  
Chi Squared

Penelitian ini bertujuan untuk membuat prediksi prestasi belajar siswa berdasarkan status sosial ekonomi orang tua, motivasi, kedisiplinan siswa dan prestasi masa lalu menggunakan metode data mining dengan algoritma J48. Sebagai perbandingan, data penelitian dianalisis juga dengan CHAID (Chi Squared Automatic Interaction Detection) dan regresi ganda. Pendekatan penelitian yang digunakan adalah kuantitatif. Subyek penelitian ini adalah siswa tingkat X SMK Negeri 4 Surakarta berjumlah 416 siswa. Teknik pengumpulan data yang digunakan adalah dokumentasi dan angket. Hasil penelitian menunjukkan bahwa analisis prediksi menggunakan decision tree algoritma J48 memiliki akurasi sebesar 95,7%, sedangkan analisis prediksi menggunakan CHAID memiliki tingat akurasi 82,1% dan analisis regresi ganda menghasilkan tingkat signifikansi sebesar 90,6%. Berdasarkan hasil tersebut bisa disimpulkan bahwa metode J48 lebih baik dibandingkan dengan metode CHAID dan regresi ganda. DATA MINING TO PREDICT STUDENT’S ACHIEVEMENT BASED ON SOCIO-ECONOMIC, MOTIVATION, DISCIPLINE AND ACHIEVEMENT OF THE PASTAbstractThis study aims to make student achievement prediction based on socio-economic status of parents, motivation, discipline students and past achievements using data mining methods with the J48 algorithm. For comparison, the data were analyzed also with CHAID (Chi Squared Automatic Interaction Detection) and multiple regression. The research approach is quantitative. The subjects of this study were student-first level at SMK Negeri 4 Surakarta totaled 416 students. Data collection techniques used are documentation and questionnaires. The results showed that the predictive analysis using J48 decision tree algorithm has an accuracy of 95.7%, while the predictive analysis using CHAID has the rank of an accuracy of 82.1% and a multiple regression analysis resulted in a significance level of 90.6%. Based on these results it can be concluded that the J48 method is better than the CHAID and multiple regression methods.

scholarly journals Penerapan Algoritma Apriori pada Sistem Informasi Perpustakaan Sekolah SMK Negeri 1 Siantar

2012 ◽  
Vol 6 (1) ◽  
pp. 226
Author(s):  
Risti DwiSyari ◽  
M Safii ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
School Library ◽  
School Libraries ◽  
Vocational School ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Great Leader ◽  
Library Staff ◽  
Strategic Layout

The SMK Negeri 1 Siantar School Library is one of the special libraries located at the SMK Negeri 1 Siantar School. Libraries provide various kinds of library materials such as books, lessons, lesson questions, and other vocational books. After the researcher made observations, the problem that often occurred was books that were borrowed and returned books that had a non-strategic layout, so that library visitors who did not know the placement found it difficult to find the books they wanted to borrow. This research uses data mining techniques, namely the Apriori Algorithm, the Apriori Method is a method for looking for patterns of relationships between one or more items in a dataset. The Apriori method can be used for data on borrowing books at the Siantar 1 State Vocational School School Library, where the composition of the library books (B1) X_Press UN 2019 B. Indonesia side by side with books (B4) School of Love is a Great Leader and Teacher, if the composition of the book is (B10) Moral Mulia side by side with book (B1) X_Press UN 2019 B. Indonesia, If the book arrangement (B7) X_Press Mathematics is side by side with the book (B5) Relationer, if the book arrangement (B7) X_Press Mathematics is side by side with the book (B9) Indonesian Wisdom Batak Toba, and if the arrangement of the book (B10) Morals Mulia is side by side with the book (B8) Hati Therapy, the data from these items each met the minimum confidance value of 0,5% or the same as the specified 50%. The result of this research is to help library staff arrange the book layout correctly. It is hoped that this research can provide input to the school

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

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