Minimum Support Value Research Articles

Pemodelan Data Mining Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus: Kedai Ngodeng & Smoothies)

The role of UMKM is very large in the growth of the Indonesian economy, Kedai Ngodeng & Smoothies, as one of the UMKM that is active in the food and beverage industry. This shop has not made maximum use of sales transaction data. The transaction data is only used as an archive and is not used to organize sales strategies. In this research, the method used is the a priori algorithm to determine the relationship between one product item and other product items so that it can be used as a promotional package or bundling to increase sales profits. The data used is sales transaction data for the Ngodeng & Smoothies shop from January to May 2024. The research began with initial data collection, followed by data description and evaluation of data selection. After that, attribute selection is carried out. The CRISP-DM method is applied in this research which includes six stages, namely business understanding, data understanding, data preparation, modeling, evaluation and dissemination. Based on the results of the research, there are 95 Association Rules based on previously determined parameters, namely the minimum support value is 50% and the minimum confidence is 90%. One of the rules that was formed is that Smoothies Mangga Large, Twister, Kembang Cumi --> Odeng Spicy has a confidence value of 100%. The information obtained can provide important insights into customer preferences and can be used to design more effective product bundling promotions.

Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space

There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.

Clustering of uninhabitable houses using the optimized apriori algorithm

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.

Clustering of uninhabitable houses using the optimized apriori algorithm

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.

Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".

Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision

Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data

The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.

Penerapan Data Mining Algoritma Apriori untuk Menemukan Pola Hubungan Status Gizi Balita

Malnutrition of toddlers can cause severely health problems, growth retardation, and decreased intelligence. Therefore, it is important to know the pattern of the relationship between the nutritional status of toddlers, especially against the condition of malnutrition to get information about the possible risk of nutritional coexistence problems. The purpose of this research is to apply data mining using the apriori algorithm in finding patterns of relationships betweennutritional status and to produce an accuracy level of association rules based on the value of support, confidence, and validation with the lift ratio. The results of this study are expected to generate information that can help prevent and treat malnutrition, as well as appropriate nutrition interventions. Analysis and implementation of data using the a priori algorithm method. Manual calculations are carried out using Microsoft Excell and accurate calculations with python programming. Data on the nutritional status of children under five, especially undernourished conditions will be processed to find patterns of association relationships. Determined the minimum support value of 0.1 or 10% and the minimum confidence of 0.5 or 50%. The research produces association rules in the form of association relationship patterns of nutritional status specifically malnutrition conditions (Underweight?Stunting ) with a support of 0.25 or 25% and confidence 0.57 or 57%. The rules have met the minimum support and minimum confidence values and have been validated by the lift ratio with a value > 1.

ANALYSIS OF THE INFLUENCE OF BUSINESS INTELLIGENCE ON BEVERAGE SALES KONNICHIWA COFFEE USING THE METHOD EQUIVALENCE CLASS TRANSFORMATION

Konnichiwa Coffee shop is one of the business beverage that sells various types of drinks. Determination of price discounts or product promotions sometimes doesn’t match what customers wants and needs. Another obstacle found was that there were no promotions for consumers who bought directly at Konnichiwa Coffee outlets. This causes less than optimal sales strategies and store promotion strategies. Determining menus that are often purchased simultaneously by consumers can be a reference for owners in determining promotional strategies. Therefore, this research was conducted to look for association patterns between menus that can implement business intelligence (BI) in the association rules method. One of the association rules algorithms is the ECLAT algorithm. The ECLAT algorithm is used because it is more efficient and faster in terms of time. The data used in this research were 214 products from 100 transactions with 26 types of drink menus. The resulting pattern refers to a minimum support value of 3% and a minimum confidence of 30%. This means that transaction data that has association patterns or that were purchased together is only 3% of the total transaction data with a confidence level of 30%. From the results obtained, the Java Latte, Kopi Latte and Sapporo Latte menus are the menus that are most often purchased together so they can be used as a marketing strategy for Konnichiwa Coffee.

ATURAN ASOSIASI UNTUK REKOMENDASI STRATEGI PEMASARAN PRODUK KERAJINAN MENGGUNAKAN FREQUENT PATTERN GROWTH

Besides selling equipments and spareparts for two-wheeled vehicles, NTN Racing Store also sells handicraft products made from water hyacinth. Lately, NTN Racing Store facing challenges in selling its products, as evidence by the unpredictable monthly revenue. The reason is insufficiency of customer purchasing patterns understanding. The impacts are the declining of the marketing and sales performance for creative water hyacinth handicrafts products since 2020. The main purpose of this studi is to identify which products are purchase together by customers in one transaction when shopping for water hyacinth products. Market Basket Analysis is a rule that determines associations between various attributes and is a key application in the retail industry. This method is use to analyze past consumer purchasing behavior to determine items that are frequently purchase together. Its purpose is to uncover relationships between products purchased by customers in a single transaction. On the other hand, Frequent Pattern Growth is an alternative algorithm use to identify the most frequently occurring datasets within a collection of data. The testing results of 435 transactional data points from NTN Racing Store, using 1% minimum support value and 25% minimum confidence value, resulting four association rules within lift ratios above 1 which indicates significant relationships among itemsets. From these evaluation results, customer purchasing patterns can be identifying trough the association analysis rules. This information can assist the store owner in determining bundling packages to sell and to devise store sales strategies.

Penerapan Data Mining Dengan Algoritma Apriori Untuk Analisa Pola Pembelian Pelanggan Pada Penjualan Martabak Surya Kencana

The purpose of this study is to analyze customer buying patterns at Martabak Surya Kencana. The method used is the Apriori algorithm, the Apriori algorithm is one of the most well-known algorithms in data mining in finding data patterns or patterns of occurrence/data frequency. Usually the Apriori algorithm is used to find customer buying patterns at a minimarket based on purchase transactions. The results of this study are with a minimum support value of 20% and a minimum confidence value of 70%, it produces 2 rules, namely if you buy Martabak Telor 3 Super Eggs, you will buy Martabak Sultan Cheese with a support value of 26.8% and a confidence value of 79. 8% and if you buy "Chocolate Sweet Martabak" then you will buy "Dry Nutella / Ovomaltine / Toblerone Thin Martabak" with a support value of 24.2% and a confidence value of 78.9%.

Product Stock Supply Analysis System with FP Growth Algorithm

This study explores the application of Data Mining in deciphering consumer purchasing patterns at Tani Heritage Shop, a retailer specializing in agricultural products. Facing the challenge of managing a high volume of daily sales transactions, the shop often encounters difficulties in tracking which products are frequently purchased together. This lack of insight leads to a critical issue: popular products running out of stock unexpectedly. To address this, the research focuses on developing a product stock supply analysis system, utilizing the FP Growth Algorithm. The FP Growth Algorithm, a powerful tool in Data Mining, is employed to analyze sales transaction data and identify consumer purchasing trends, particularly products bought simultaneously. This approach is designed to provide insights into optimal stocking strategies, ensuring the availability of in-demand products. The research methodology involves applying the FP Growth Algorithm to model the product stock supply system, using specific sales data attributes. The results of this study are significant. By setting parameters such as a minimum support value of 30%, a confidence value of 70%, and targeting the highest lift ratio value of 3.67, the research successfully derives several key association rules from the FP Growth algorithm. These rules are instrumental in optimizing the product stock supply analysis system.

Application of the Association Method Using FP-Growth Algorithm to Find Pattern Of Medicine Purchasing Transactions at Pharmacy

This research was conducted with the aim of finding patterns of medicine sales transactions at the Family Pharmacy so that it can be used as a reference for providing medicine stock so that service to consumers can be improved. This study uses the association method by implementing the FP-Growth algorithm. Through the association method, patterns of medicine purchases will be found in the form of medicine that are purchased together. Then, this study wants to test the FP-Growth algorithm in finding patterns of medicine purchases. The data used is data for the second half of 2021. This dataset consists of 20,266 transactions and 1,166 attributes in the form of medicine. The results of the study by setting different minimum support and minimum confidence values, 7 association rules are obtained with a minimum support value of 10% and a minimum confidence of 90% and the accuracy value of each rule formed is 2,683. The level of association rules formed is strong because it shows more than 1. After knowing the association rules that are formed, these rules will be used as a reference in providing medicine stocks and medicine recommendations.

Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart

Sakinah Mart is a retail business that focuses on determining the layout of goods based on perceptions and implementing a discount system for specific items, but without offering bundling packages. This research aims to provide recommendations using the apriori algorithm as a decision-making tool for analyzing the layout of goods and bundling packages. The apriori algorithm is a data mining technique used to discover association rules and analyze customer purchases, specifically identifying the likelihood of customers buying item X along with item Y. The algorithm consists of two main components: support and confidence. The research applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, utilizing the apriori algorithm to analyze sales transaction data. The dataset includes 2000 sales transactions with two attributes, resulting in the identification of 2 and 3 itemsets. The findings include 16 rules with a minimum support value of 42% and a minimum confidence of 85% for the layout of goods. For bundling packages, 5 rules with a minimum support value of 40% and a minimum confidence of 90% were generated. These results offer valuable recommendations to the company, using the apriori algorithm for analyzing the layout of goods and bundling packages.

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

The Effect of Social Media on Student Learning Motivation Using the Apriori Method

The success of student learning can be determined by their motivation. Students who have high learning motivation tend to have high achievement as well, otherwise their learning motivation is low, their learning achievement will also be low. student learning motivation in the subject is very low. Some students prefer to play social media rather than pay attention to the material explained by the teacher during class hours. Therefore, this study aims to explore the influence of social media on students' learning motivation. This research uses data mining method with Apriori algorithm to identify patterns related to social media usage and students' learning motivation. The Apriori algorithm is one of many algorithms in data mining that is used for frequent itemsets and association rules in databases on transactional data that are generated by identifying each item that exists, and combining larger sets of items provided that the items appear frequently enough in the database. Based on the research that has been done, the author can draw the conclusion that using the Rapid Miner 7.1 application tools in applying the apriori algorithm produces the same rules as manual calculations using 300 data on the learning motivation of Abdi Negara Binjai SMKS students and the system can generate association rules using 300 student learning motivation data with a minimum support of 12% and a minimum confidence of 75% and produce 5 association rules 3 itemsets to determine the learning motivation of Abdi Negara Binjai SMKS students. One of the rules that has the highest confidence value is, if YT and J2 then M1. Which means that every student who uses YOUTUBE Social Media with a length of use is 3-4 HOURS then INCREASES STUDY MOTIVATION. Then the less the ɸ (frequent) value is set, the more data that can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.

Application of Data Mining in Analyzing the Effect of Parents' Employment and Education Level on Student Behavior Using the A PRIORI Method (Case Study: SDN 024769 Binjai)

Behavior is a person's reaction to a stimulus that comes from the external environment. Parents are one of the main factors in the formation of children's behavior. This study aims to find out the effect of parents' work and education on student behavior. By using RapidMiner in testing 234 SDN 024769 Binjai student data, using the Apriori method and setting a minimum support value of 8% and 70% confidence, 1207 rules were obtained in the entire set and 2 rules in 9 itemsets. And the best rule with the highest value is obtained, if the father's job is self-employed, the mother's job is self-employed, the father's last education is high school, the mother's last education is high school, the time the father spends working is more than 8 hours per day, the time the mother spends working is more than 8 hours per day, the time the father spends on family is every day, and the time the mother spends on family is every day then the student has good behavior at school, with a support value of 8.5% and a certainty value of 95.2%.

Correlation Between Technological Advances On Employee Performance Using A PRIORI Method (Case Study: PLN City of Binjai)

Technology is a set of tools that can be used or utilized by humans to facilitate various forms of work. Employee performance is the ability to achieve job requirements, where a work target can be completed in a timely manner or does not exceed the time limit provided so that the goal will be in accordance with company morals and ethics. This study aims to determine the correlation of technological progress on employee performance. In this study using RapidMiner as a test of 230 data on employees of PLN Binjai City. By using the Apriori algorithm method with a minimum support value of 10% and confidence of 50%, 44 association rules are obtained in the entire set and there are 2 rules in 4 itemsets. From the test results, the best rule with the highest value is obtained, namely if the data is T7, A3, F7 then SB, which means if using Ms.Word and Ms.Excel and Ms.PPT, using FSO Mobile and PLN Mobile, using Computers and Printers and Fax Machines then employee performance is Very Good with a support value of 30% and a confidence value of 96%.

Employee Education and Training Recommendations using the Apriori Algorithm

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data on the implementation of education and training in 2022 in the MoF Central ICT Department, only 27.35% of the employees participated in education and training according to the proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or substitute participants who are not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the a priori algorithm to analyze historical data on employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resources Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.

FP GROWTH ALGORITHM MODELING FOR PRODUCT INVENTORY ANALYSIS

Pusaka Tani is a shop that provides agricultural needs such as fertilizers, rice seeds and plant medicines. Sales transactions at the Farmer's Library still use a manual system, namely by using notes as proof of sales transactions. The amount of data that accumulates results in less useful data. So that Pusaka Tani cannot know the products that are often purchased by consumers simultaneously which results in many products being sold out without the knowledge of Pusaka Tani which results when consumers are not going to buy these products and products that are rarely purchased by consumers become unsold. The purpose of this study is to model FP Growt in the analysis of product stock supply. In the research conducted, the support value and confidence value used is a minimum support value of 30% and a minimum confidence value of 70%. The results obtained from the research conducted are for the highest lift ratio value of 1.67 and the lowest is 1.09