Minimum Confidence Value Research Articles

Penggunaan Data Mining untuk Analisis Pola Pembelian Pelanggan Menggunakan Metode Association Rule Algoritma Apriori (Studi Kasus di Toko Waspada)

Alert Shop is a store that sells a variety of products such as household items, daily necessities, accessories, and cosmetics. Analyze customer buying behavior with Alert Store bulk transaction data.. The Apriori algorithm is one of the algorithms for extracting correlation rules in the field of data mining. We apply the Apriori algorithm by using Rapid Miner to find customer buying patterns in Waspada Store sales transaction data. The methodology of this research is structured to serve as a reference for the researcher's guidance and support to the researcher in the research process, and the research design plays an important role in guiding the researcher from problem description to testing result. The rules used by the a priori algorithm can be used as criteria to evaluate elements that meet the minimum support and minimum confidence values. The confidence value of the relationship between the two points above can be considered high, and the results of these rules can be used as the basis for discussing the points above.

MARKET BASED ANALYSIS SEBAGAI PENINGKATAN PENJUALAN PRODUK MENGGUNAKAN ALGORITMA K-MEDOIDS DAN FP-GROWTH

The COVID-19 pandemic has greatly affected the economy in Indonesia. The emphasis on the development of covid-19 through Enforcement of Restrictions on Community Activities has an impact on the Micro, Small and Medium Enterprises (MSME) sector, including product sales at the Distro Sextors. Therefore, a strategy is needed to increase sales, one of which is through data mining techniques with market based analysis. Data mining is one of the processes of Knowledge Discovery in Database (KDD) that uses scientific methods in its application. The data used in this study were 6531 sales transactions. The data mining algorithm used in this research is K-Medoids for grouping which aims to find the best cluster. The cluster experiments used are K-3, K-4, K-5, K-6 and K-7 then cluster testing is carried out using the Davies Bouldien Index (DBI) method and the results are cluster K-3 is the best cluster because it has a homogeneous value lowest. Then the association rule uses the FP-Growth algorithm and the minimum support value used is 6% to 7% while the minimum confidence value is 60%. The results of the association rule analysis are product recommendations that can be used as promotions to increase sales in Distro Sextors. The products are SE128, SE131, SE130, SE431, SE792, dan SE804.

Continuous Ship Unloader Availability Analysis Using Association Rules Method with Apriori Algorithm


 Petrokimia Gresik is a company engaged in the argo industry. In an effort to increase productivity, PT. Petrokimia Gresik has a special port used for loading and unloading activities. During loading and unloading activities, special equipment is needed to make it easier to move cargo. Not infrequently this equipment suffers damage that is not known what object affects the damage. Based on the 2021 Asset Utilization Data, internal tools whose availability is still below the target of more than 1% are CSU I and 81.66%. The percentage of equipment availability that is below the target causes the process of unloading raw materials to be not optimal and can cause demurrage costs or company fines to the ship if the cause of the damage is not immediately identified and addressed. This study aims to help companies obtain information about objects that affect CSU I and CSU II experiencing breakdowns. The role of data mining that will be used in this research is association rules with a priori algorithms. Data processing is assisted by Microsoft Excel, RapidMiner software and WEKA software. There are no association rules that are formed with the application of a minimum support value of 50% and a minimum value of 50% confidence, both in CSU I and CSU II data processing. The association rules formed by applying a minimum support value of 20% and a minimum confidence value of 50% for CSU I data processing are 3 rules, while the association rules obtained for CSU II data processing are 2 rules. Based on the rules formed in CSU I and CSU II, the breakdown item that is likely to be damaged is Vertical – Motor 2M1.

Aplikasi Data Mining Asosiasi Barang Menggunakan Algoritma Apriori-TID


 
 
 Information about consumer spending patterns is needed by retail companies in determining promotion strategies, placing merchandise on shelves, and inventory management. Most applications in retail companies only produce information from the company side. Applications owned by the company have not been able to generate transaction activity information from the customer side. The transaction activity information from the customer side is knowing consumer spending patterns in terms of buying goods. This information can overcome problems that are often faced by companies, namely customer complaints when there is a vacancy in inventory of pairs of goods that are often purchased by customers. This article describes the application of association data mining with the Apriori TID algorithm. Apriori-TID is a development of the Apriori algorithm. Apriori-TID plays a role in finding frequent itemsets based on minimum support. The application can generate goods association rules based on the minimum support and confidence values entered by the user. The association data mining application features dataset import, dataset analysis, and print analysis result reports. The association rules generated by the application are evaluated using the lift ratio method. The number of transactions, minimum support, and minimum confidence affect the number of rules generated by the application.
 
 

Utilizing Apriori Data Mining Techniques on Sales Transactions

The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1Cre2), (Cre1Cre12) and (Cre9Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

AbstractNowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre‐processing phase, data pre‐processing, frequent itemset mining, and ARM. In data pre‐processing phase, the collected twitter datasets are pre‐processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid‐19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

Penerapan Apriori Hybrid Pada Transaksi Penjualan Barang

Ayu Nadi Swalayan is a retail company that produces a lot of every day sales transaction data, and it is stored for years without knowing the benefits and the placement of the goods is still random. From these problems, an effort is needed to process the data so the data is useful in the future. One of the process is using data mining techniques with apriori hybrid algorithm to find association rules for an items combination. Data product sale in a certain period is used to find the association rules. The results of this study are the development of applications that are used to determine consumer spending habits. So that the company can develop a strategy to promote the product sale and close placement for items that are often purchased together. The application testing found the effect of minimum support, minimum confidence on the number of rules, and lift ratio testing. The smaller the minimum support and minimum confidence, the more rules are generated and vice versa. The lift ratio value is directly proportional to the minimum confidence value and inversely proportional to the minimum support value. The higher the minimum confidence value, the higher the lift ratio value and vice versa. The more items in the transaction cause the minimum support threshold to be lowered in order to generate rules for the data analysis process with the hybrid apriori algorithm

Analisis Pola Transaksi Pelanggan Menggunakan Algoritme Apriori

Diengva is a shop that sells various kinds of goods such as household items, household appliances, accessories, flower buckets, clothes, bags, shoes, cosmetics, and others. The large number of purchase transaction data in diengva can be used to analyze customer behavior in purchasing goods. Apriori algorithm is one of the algorithms in the field of data mining for extracting association rules. This study applies the apriori algorithm to find customer buying patterns in diengva store sales transaction data using rapid miner. The rules resulting from the application of the apriori algorithm can be used as a basis for stocking items that meet the minimum support and minimum confidence values. Items that meet these rules are eyelashes, eyelash glue, soft lens, and soft lens water. The confidence value of the relationship between two items can be high so that the results of these rules can be used as the basis for stocking.

Implementation of Improved Apriori Algorithm for Drug Planograms in XYZ Pharmacies and Analysis of Medicinal Procurement Planning Using ABC Critical Index Methods

In conducting transactions in the present era where people no longer carry out transactions manually, where now there is modern technology, so that it can make it easier to conduct transactions, such as pharmacies. Pharmacy owners can now put their medicines in a position that matches the combination of goods sold and can also conduct an analysis of drug procurement planning. Improved Apriori Algorithm Method is one of the methods to assist in finding the combination of goods most often purchased by transforming data into a matrix and then cutting columns and rows according to the minimum amount of support specified and the iteration values that are ongoing, then displaying the results of the combination that is in accordance with the specified minimum confidence and the ABC Critical Index Method to analyze the critical stock procurement planning using the use value, investment value and VEN value to calculate the critical index value for each drug, after which each drug will be included in each class vary based on the level of drug sales. The results of testing manual calculations and programs obtained on 101 drug data and 64 transaction data in the Improved Apriori method are 4 drugs that can be combined with a predetermined minimum confidence value of 100%. Then in the Critical Index ABC method there is a percentage of drugs that go to group A is 17.82%, Group B is 66.34% and Group C is 15.84%.

Analisis Penerapan Metode Algoritma Apriori Untuk Melihat Minat Pengunjung Pada Objek Wisata Kota Palembang

The management of tourist destinations in Palembang City is managed by the Dinas Pariwisata, Tourism in Palembang City is classified into 3 Tourism, namely Nature Tourism, Cultural History Tourism, and Special Interests. However, Dinas Pariwisata has not seen the pattern of interest of visitors from grouped tours. This makes the authors want to determine the patterns of interest of the respondents by distributing questionnaires to 100 respondent. The results show that there are 10 rules established by the WEKA application with a minimum support value of 5% and a minimum confidence value of 50%. The results of the pattern formed are Al-Qur'an Al-akbar, Musi River, and Ampera Bridge with a confidence value of 90%.

IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)

Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.

Implementation of Apriori Algorithm for Determining Product Purchase Patterns

In this study, the implementation of Data Mining association method using Apriori algorithm to determine product purchase pattern. Data obtained from the sales transaction data in the Toko Jaya Putra Bumi Agung in the form of a purchase note that will then be implemented using Apriori algorithm. The data mining technique used is the association rule method to know the pattern between items one and other items using support and confidence. In this study of the calculation process with Apriori algorithm obtained minimum value of support 50% and minimum confidence value 70% then resulting tendency of products purchased by consumers ie if buying cooking oil then buy eggs with confidence 75%. If buying Sumendo coffee then buy sugar with confidence 77.8%. This research is expected to be helpful and useful for the owner of Toko Jaya Putra Bumi Agung to predict and analyze the combinations of what kinds of products are often purchased by consumers simultaneously. For further research need to be developed again by combining other data mining algorithms. Preferably the use of datasets that are used will be better to use larger datasets so that they can obtain higher accuracy values.

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Objectives: This study aims to implement data analytics and machine learning approaches to rice data and to establish association rules on fixed attributes and their correlations for yield prediction of crops. Methods: The data of rice crop is collected from district Larkana as per defined parameters: area, production, yield, temperature, rainfall, humidity and wind speed. The pre-processing operations are applied on prepared dataset to execute data analytics and machine learning algorithms. The processed data are then input into an Apriori algorithm for generating association rules. Neural network model is used to perform optimization on resulted association rules. Findings: Minimum support and confidence values equal to 3 and 80 respectively were set using Apriori algorithm on prepared rice dataset and obtained 88 association rules. Among them, results of 28 associated rules predicted `High Yield Production\'. Neural network model is experimented to optimize the predicted yield of district Larkana through which overall network performance of 97.8% is calculated. Previously, rice yield data of Larkana were not statistically and digitally predicted and investigated. Application: Group of effective and well-built association rules of yield prediction are core outcome of this study which will be helpful for researchers, farmers and government officials to improve the productivity of rice crop. Keywords: Rice data sets; Association rules; Yield prediction; Data optimization; Neural network

Consumer purchase patterns based on market basket analysis using apriori algorithms

Analyzing customer purchasing patterns can help minimarket expand marketing strategies by gaining insight into which items are often bought together by customers. Also, transaction data is a source of information available at the convenience store and one thing that can be used for business decision making. In this paper, we aim to use the Apriori algorithm method to obtain consumer purchasing patterns to analyze consumer purchasing patterns. This system uses a priori algorithm calculation method where the input data is consumer transaction data. Transaction data will be sorted and calculated by providing a minimum support value and a minimum confidence value. In this study, the authors conclude that the results of the analysis of information systems in determining consumer purchasing patterns can be as information to determine sales and in the application of Apriori algorithms can provide information in the form of a combination of consumer purchase patterns based on consumer transaction data with a minimum support value above 10% and the minimum confidence value is above 65%.

PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)

Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.

Combination, Pola, Apriori ANALISIS POLA BELANJA KONSUMEN MENGGUNAKAN ALGORITMA APRIORI PADA RAFFA PHOTOCOPY

Raffa Photocopy is a shop that started its business in 2016. This business not only provides photocopy services, but also provides office stationery and school supplies. Every day there are sales transactions where the recording of goods sold has a relationship between one another, because in recording sometimes consumers do not just buy one item, but two items even more as when buying a book, it is likely that consumers also buy a pen. This recording is only stored as an archive by Raffa Photocopy, even though the number of sales transactions that occur every day can lead to a pile of data. One effort to increase sales at Raffa Photocopy can be done by processing transaction data that overlaps by using data mining association techniques. This association rule technique uses the Apriori algorithm which deals with the study of 'what is with what' or discovers the association pattern of items that are often bought. The results of this study in the form of rules include the first, if you buy an eraser, it is likely that consumers also buy notebooks simultaneously. Second, if you buy Tipex, then consumers also buy a double folio. The results of the Apriori algorithm process are based on a minimum support value of 35% and a minimum confidence value of 80%.

Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

Penerapan Algoritma FP-Growth dalam Penentuan Pola Pembelian Konsumen pada Kain Tenun Medali Mas

In Kediri City there is a very popular woven fabric shop called Medali Mas. It has high sales transaction activity resulting in a large stack of data purchases. This data stack is examined as an information pattern for consumer purchases using data mining association rule techniques and FP-Growth algorithms. The FP-Growth algorithm uses the concept of development tree in searching for frequent item sets. The data used are, 26 types of woven fabric items and 200 transaction data provided that 2 or 3 types of items in 1 transaction. Determined minimum support value of 20 percent and minimum confidence value of 10 percent. It also used Chi-Square testing to find out how much correlation between variables from the results of frequent itemsets that have been calculated. The final result of the consumer purchasing pattern is obtained (m to no) when buying Semi sutra Lusi = grey, Pakan = Blue Flowers, then the consumer might buy Sarong Lusi = black, Pakan= green Lurik and Cotton Lusi= yellow, Pakan = Tosca Bamboo with the results of the correlation between variables at 19.1397274913.