<b>TRANSFORMASI DATA TRANSAKSI KE DERET WAKTU DAN EVALUASI MODEL PERAMALAN PERMINTAAN PADA MARKETPLACE PLAZA BANTEN</b>

Determining the combination of items for product bundle offerings is important to increase consumer buying interest. By analyzing historical data on sales transactions, purchasing patterns can be identified to find the right product combination that has a high probability of selling. This study aims to determine consumer purchasing patterns on large transaction data using market basket analysis with the help of RStudio Software. In this study, the pattern of purchasing fashion products is revealed from sales transaction data throughout 2020 using the a priori algorithm. The sales transaction data contains 262 fashion products that have been successfully transacted by consumers. Each product has a type of size ranging from medium, large, and extra-large. The transaction data contains 816566 completed transactions. This transaction data will then be processed by the a priori algorithm with a minimum support value of 0.003 and a minimum confidence level of 0.8, found 15 product combinations. Based on this combination, this study suggests that for each product combination it is recommended that there are black, white, maroon, and navy products. This study might be helpful for the fashion producer to increase customer buying interest by offering product bundle promotion.

Market Basket Analysis analyzes consumer buying habits by finding relationships between items in the consumer's shopping basket. This Market Basket Analysis can provide success to the retail industry with the ability to understand consumer behavior and the speed of response to information obtained by retail business owners. This understanding is the result of an analysis that can help business owners improve marketing and sales strategies while utilizing transaction data. Sales transaction data that has been accumulated so far has only become data warehouses, while large amounts of transaction data can bring major changes to the level of competition in business and business actors in order to survive in the business world. In addition, after the COVID-19 outbreak, Indonesia experienced a slowdown in economic growth of 5.31%. This can be overcome by utilizing Market Basket Analysis to increase sales from their businesses. MBA with the methods used are FP-Growth and Apriori to analyze store transaction data in order to obtain association rules that can be used in improving marketing strategies. This analysis was carried out with 3 scenarios for 3 different minimum support values (1%, 2% and 3%) but the same minimum confidence value of 0.6 (60%). The comparison of the two methods is that 2 out of 3 scenarios produce the same association rule, namely 1 final association rule result with a lift value of 1.42. The three scenario results from both methods can be used by business owners as a consideration in determining sales strategies.

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).

Sales transaction data contains rich information and can support company competitiveness. However, this transaction data is initially unstructured and needs to be processed into insight for the company's decision-making. Market Basket Analysis (MBA) is a data mining technique that can be used to study consumer purchasing patterns. This paper presents a case study on using an MBA to obtain consumer buying behaviors where the result of the MBA is then used to design a proposed layout. The animal market governed by Yogyakarta Province, known as Pasty Market, was used as a case study. Pasty Market is an animal trading center with around 30,000 square meters area and 255 sellers that sell various kinds of animals such as songbirds, dove birds, rabbits, cats, dogs, iguanas, turtles, ornamental chickens, and ornamental fish as well as animal food and cage. With this enormous area and number of merchants, the layout of Pasty Market becomes crucial in customer satisfaction. Association rules result in four priority levels in proposed layout planning, where these rules are used to determine the proximity among items in the proposed layout. These four levels of priority, ordered by the confidence value, are (1) “songbirds” and “bird food” (confidence value 91%), (2) “ornamental fish” and “turtles” (confidence value 80-90%), (3) “birdcages” and “songbirds” (confidence value 70-80%), and (4) “cats” and “dog” as well as “birdcages” and “birds” (confidence value 50-70%). Association rules were then used as the basis for determining the proximity value between merchants, where the proximity rules were then used for designing a proposed layout.

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

Data mining is a technique to dig up new information from a data warehouse, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in trading businesses such as the Iblite Luxury store. This store is located in Medan close to residents' houses, Sales transaction data will continue to grow, causing data storage to be even larger. Sales transaction data is only used as an archive without being properly utilized. Basically, a dataset has very useful information. Market basket analysis with a priori algorithm is one of the data mining methods that aims to find association patterns based on consumer shopping patterns, so that it can be known what items of goods are purchased in a At the same time, the results of this study found that the highest support and confidence values were Ysl and Chanel with a support value of 50% and confidence of 75%.

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention.
 Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

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.

Customers increasingly expect companies to understand their wants and needs and to market to those desires. Unfortunately, such levels of personalisation can be difficult to accomplish for traditional brick-and-mortar retailers, particularly when the fixed cost of personalised marketing is significant. This paper considers methods for providing customised promotions to customers in the form of weekly flyers. We consider how multiple versions of a weekly flyer can be used by a retailer, which products should be included in each flyer version, and how customer preferences, markup, and inventory considerations impact these decisions. Specifically, transaction histories are used to estimate customer preferences for various product offerings using market basket analysis. These probabilities are then incorporated into an optimisation model for grouping customers into market segments and presenting option sets within each segment that maximise expected marginal profits across the flyers. A heuristic is proposed for solving larger problems and evaluated against lower and upper bounds on the optimal profit. Computational results indicate that leveraging customer transaction data to optimise the product selection and assignment of four unique flyers can increase profit by 7.7% over the optimal single-flyer solution.

The Market Basket analysis is the key factor for customer-centric marketing in this era. It strongly requires the data mining techniques on massive sales transaction data. The aim in this paper is to study and find the different data mining solutions for large and sparse sales transaction data. Here a real-world data set has been summarized and analyzed. In this paper the problems related to Association rule mining (ARM) on large and sparse data has been discussed. It has also shown that the application of association rule mining on sparse data is not easy if implemented directly, so there is a significant need to find some other mining technique and solutions like k-means clustering in this paper, to pre-process the data for ARM. Recency, Frequency and Monetary (RFM) model has been discussed and implemented in detail, so that K-Means algorithm can be applied easily. Additionally, this analysis will be helpful in the future research horizons like multi label classification of temporal data set and sequence to sequence neural network implementation for prediction.

Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth

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.

An increase in sales transaction data every day will cause a very large amount of product sales transaction data to be stored. In most cases, data relating to very large sales transactions are only stored and used for archival purposes, not exploited adequately. In retail marketing, association data mining is used to investigate product purchasing patterns. However, if the company does not know the customer's purchasing patterns, it can have impacts such as inappropriate marketing strategies, decreased customer retention, lost business opportunities, lack of personalization, tough competition, stock/production inefficiencies, loss of customer trust. Knowing consumer purchasing patterns, the company can develop sales strategies and make the right decisions. In this study using Market Basket Analysis using the Apriori Algorithm and FP-Growth to determine consumer buying patterns. The results of this study resulted in two itemset combinations. The first combination is that if the buyer buys yogurt and sausage, the buyer also buys whole milk. The resulting support value is 0.00147 (0.0147%), the confidence value is 0.255814 (25.58%) and the lift value is 1.61986. the second combination, namely if the buyer buys sausage (sausages) and rolls/buns (bread rolls), then also buys whole milk (milk), this combination produces a support value of 0.001136 (0.0113%), a confidence of 0.2125 (21.25%) and a lift of 1.34559 . In addition to the combination of the itemset produced in this study, it also measures computational speed in processing Groceries data for Market Basket Analysis. The computational speed produced by the Apriori Algorithm is 3.1765 seconds, while the FP-Growth algorithm is 0.15892 seconds. The difference in computational speed between the Apriori Algorithm and FP-Growth is 3.0176 seconds.

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.

Market Basket Analysis often involves applying the de facto association rule mining method on massive sales transaction data. In this paper, we argue that association rule mining is not always the most suitable method for analysing big market-basket data. This is because the data matrix to be used for association rule mining is usually large and sparse, resulting in sluggish generation of many trivial rules with little insight. To address this problem, we summarise a real-world sales transaction data set into time series format. We then use time series clustering to discover commonly purchased items that are useful for pricing or formulating cross-selling strategies. We show that this approach uses a data set that is substantially smaller than the data to be used for association analysis. In addition, it reveals significant patterns and insights that are otherwise hard to uncover when using association analysis.