Journal articles: 'SALES PATTERN'

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Pramita, Made Dinda Pradnya, Made Sudarma, and Ida Bagus Alit Swamardika. "Analysis of Sales Pattern Determination System and Drug Stock Recommendation." Jurnal Ilmu Komputer 12, no. 2 (September 30, 2019): 53. http://dx.doi.org/10.24843/jik.2019.v12.i02.p04.

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The tight competition in the pharmacy industry, requires pharmacy owners to develop strategies in increasing drug sales. One of the strategies carried out is to analyze patterns of drug sales and determine drug stock recommendations based on sales transaction data. Based on this, an application was built to determine the pattern of drug sales and drug stock recommendations by using a modified Apriori Algorithm and Triple Exponential Smoothing Method. Apriori algorithm modification is used to overcome the problem of large amounts of sales transaction data, thus minimizing the time in the database scan process. Triple Exponential Smoothing method is used in determining drug stock recommendations based on sales patterns that have been generated to prevent excess or lack of stock. Application testing techniques used are performance testing, lift ratio and accuracy testing. The research resulted in a sales pattern that has a strong association rule and time analysis using Apriori Algorithm modification that is faster than using a traditional Apriori Algorithm and the percentage error value of drug stock recommendations by 31.84%. Keywords: Sales Pattern, Stock Recommendations, Apriori Algorithm Modification and Triple Exponential Smoothing

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Murlidharan, Vijayalakshmi, and Bernard Menezes. "Frequent pattern mining-based sales forecasting." OPSEARCH 50, no. 4 (January 10, 2013): 455–74. http://dx.doi.org/10.1007/s12597-012-0119-9.

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Boby, Solikhun, and Zulia Almaida Siregar. "Analisis Pola Penjualan Produk Makanan dan Minuman Menggunakan Algoritma Apriori." Journal of Informatics Management and Information Technology 2, no. 2 (April 30, 2022): 65–72. http://dx.doi.org/10.47065/jimat.v2i2.161.

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Sales are seen as a unit of business parameters that are very vital and valuable for business people to manage the business they are running, especially in the cafe business. Cafe Aksara as one of the business actors in the culinary world has a lot of demand in the sale and supply of goods on certain days due to the dynamic nature of visitor patterns and makes business people have to be wiser in setting strategies for the combination pattern of sales of food and beverage products found in Indonesia. Aksara cafe to attract customers. In a process of determining the sales pattern strategy at Cafe Aksara, customers or buyers greatly influence the sales transaction data recorded by the Aksara Cafe business person. However, from the results of these recordings, sales transaction data tend not to be used in a process of determining sales pattern strategies to attract customers and are only used as useless archives for Aksara Cafe business people. From these problems, there are many branches of computer science that can be used as reference points in overcoming sales problems, one of which is the branch of computer science Data Mining. Data Mining is a method that is quite categorical to be recommended in solving problems with the sale of food and beverage products, especially the Apriori Algorithm. The output of the Apriori Algorithm can help Cafe Aksara business people determine sales patterns on the sales data of their products

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Weng, Cheng-Hsiung, and Cheng-Kui Huang. "Discovering Specific Sales Patterns Among Different Market Segments." International Journal of Data Warehousing and Mining 16, no. 3 (July 2020): 37–59. http://dx.doi.org/10.4018/ijdwm.2020070103.

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Formulating different marketing strategies to apply to various market segments is a noteworthy undertaking for marketing managers. Accordingly, marketing managers should identify sales patterns among different market segments. The study initially applies the concept of recency–frequency–monetary (RFM) scores to segment transaction datasets into several sub-datasets (market segments) and discovers RFM itemsets from these market segments. In addition, three sales features (unique, common, and particular sales patterns) are defined to identify various sales patterns in this study. In particular, a new criterion (contrast support) is also proposed to discover notable sales patterns among different market segments. This study develops an algorithm, called sales pattern mining (SPMING), for discovering RFM itemsets from several RFM-based market segments and then identifying unique, common, and particular sales patterns. The experimental results from two real datasets show that the SPMING algorithm can discover specific sales patterns in various market segments.

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Fauzi, Cholid, and Aly Dzulfikar. "Implementation of Product Sales Forecast Using Artificial Neural Network Method." IJISTECH (International Journal of Information System & Technology) 5, no. 2 (August 30, 2021): 153. http://dx.doi.org/10.30645/ijistech.v5i2.126.

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Product sales forecasting is used by companies to estimate or predict future sales levels using sales data in the previous year. The Artificial Neural Network Backpropagation Algorithm can forecast the sales of goods for the next period for each item in the company. The forecasting process begins by determining the variables needed in the network pattern, and then the established network pattern continued in the network training process using the backpropagation algorithm. After carrying out the network training process, the researcher comparisons with several network patterns formed. This research was conducted to discuss the forecasting analysis of PT XYZ products on spiral and leaf springs. Forecasting carried out on Toyota 48210-25290 R3 type leaf springs using the Artificial Neural Network Backpropagation method with a learning rate weight value of 0.1 hidden layers four and an error of 0.01. From the data processing analysis that has been carried out based on the weight parameters selected, the prediction of sales in April.

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Kim, Jonghyuk, Hyunwoo Hwangbo, Sung Jun Kim, and Soyean Kim. "Location-Based Tracking Data and Customer Movement Pattern Analysis Using for Sustainable Fashion Business." Sustainability 11, no. 22 (November 6, 2019): 6209. http://dx.doi.org/10.3390/su11226209.

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Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.

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-, Rino, and Maman Novian. "Analysis of the Application of Customer Purchase Mining Data on Paint Sales Using Apriori Algorithm (Case Study: PT Indowarna Cemerlang Indonesia)." bit-Tech 2, no. 3 (November 9, 2020): 131–40. http://dx.doi.org/10.32877/bt.v2i3.161.

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Sales transaction data is one thing that can be used for making business decisions. Most sales transaction data is not reused, and is only stored as an archive and only used for making a sales report. Paint sales data is one science that can be applied in cases like this. Sales transactions that are not utilized properly can be extracted and reprocessed into useful information using data mining techniques. Using one of the data mining methods, namely the a priori algorithm, sales transaction data can be reprocessed so that it can produce a consumer buying pattern. This consumer buying pattern will later help companies make business decisions. PT Indowarna Cemerlang Indonesia is a company engaged in the paint trade, where the main activity is selling various wall paints, oil / wood paints, NC paints (car paints), epoxy paints (floor paints), depo-proof (anti leaked). PT Indowarna Cemerlang Indonesia does not reuse sales transaction data resulting from its sales activities. This data is only used as a reference for making sales reports and as an archive only, causing accumulation of data and unknown paint brands that are often sold or those that are of interest to customers. Therefore, the author takes the title application of data mining analysis of customer purchase patterns in paint sales using a priori algorithm. By doing this research, it is expected to provide results in the form of information that can be useful for related parties and can design sales strategies to increase company turnover.

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Fey, Ferry Putrawansyah. "Application of the Apriori Algorithm to Purchase Patterns." Indonesian Journal of Computer Science 12, no. 2 (April 30, 2023): 553–61. http://dx.doi.org/10.33022/ijcs.v12i2.3105.

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The purpose of this research is to produce an Apriori Algorithm application system to increase sales turnover at Viona stores. The problem faced by the Viona store is that the Viona store has decreased turnover in the midst of business competition because it has not been able to optimally analyze the products that are often purchased and the combination of purchases by consumers so that sales seem monotonous and do not have a business strategy to attract customers. sales that can attract consumers. One way is to make sales with sales packages at lower prices. It must have a good pattern and analysis to be able to combine products into a sales package. However, with the limited ability of Information Technology, in this study a sales application was built that applies the a priori algorithm. This a priori algorithm is very effective in finding the relationship pattern of one or more itemsets in a large data set so that it is effective in calculating a sales transaction data and finding patterns of combinations of consumer habits and being able to quickly create product sales packages. increase sales turnover. The results of the process of applying the a priori algorithm to sales data at the Viona Store through the RapidMiner application are the same as the results applied to the system built and using sales transaction data for the month of May 2022 using a minimum support of 30% and minimum confidence of 30%. So from this study, information was obtained that the items that were often purchased together during this May period were lighters and cigarettes with 100% Confidence. And for the month of June Viona Stores can recommend packages in their store by looking at the results of a combination of 3 items, which are later expected to increase sales turnover at Viona Stores.

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 151. http://dx.doi.org/10.17933/jppi.2019.090206.

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<p class="JGI-AbstractIsi">In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.</p>

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 151–59. http://dx.doi.org/10.17933/jppi.v9i2.285.

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In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.

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Yuliana, Lingga. "Dampak Kondisi Pandemi di Indonesia Terhadap Trend Penjualan (Studi Kasus pada PD. Sumber Jaya Aluminium)." JRB-Jurnal Riset Bisnis 4, no. 1 (October 31, 2020): 27–38. http://dx.doi.org/10.35814/jrb.v4i1.1480.

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The purpose of this study was to analyze the sales conditions at PD. Sumber Jaya Alunimium, through the sales data pattern, identified the relationship between PSBB and the sales impact of PD. Sumber Jaya Aluminum, as well as factors from the decline in sales due to the Pandemic conditions. The research method used is descriptive quantitative analysis through purposive sampling technique with Analytic Network Process (ANP). The results showed that the sales data pattern of PD. Sumber Jaya Aluminum during the period December 2019 to April 2020 has a sales trend that tends to decline. Based on processing with the Analytic Network Process (ANP), Large-Scale Social Restrictions (PSBB) are a problem with the decline in company sales which causes the company's operations to stop. This is followed by the unavailability of raw materials, decreased demand and the effect of panic buying.

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Patil, Kirti S., and Sandip S. Patil. "Sequential Pattern Mining Using Algorithm." Asian Journal of Computer Science and Technology 2, no. 1 (May 5, 2013): 19–21. http://dx.doi.org/10.51983/ajcst-2013.2.1.1715.

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The concept of Sequential Pattern Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. Sequential Patterns are used to discover sequential sub-sequences among large amount of sequential data. In web usage mining, sequential patterns are exploited to find sequential navigation patterns that appear in users’ sessions sequentially. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. In this paper, a new algorithm is proposed; it combines the Apriori algorithm and FP-tree structure which proposed in FP-growth algorithm. The advantage of proposed algorithm is that it dosen’t need to generate conditional pattern bases and sub-conditional pattern tree recursively. And the results of the experiments show that it works faster than Apriori.

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Hayati Ifroh, Riza, Iwan M.Ramdan, Vivi Filia Elvira, Rahmi Susanti, Reny Noviasty, and Ika Wulan Sari. "Cigarette Sales Promotion Pattern and Smoking Behavior of Sellers in Mulawarman University, Samarinda." Jurnal Ilmu Kesehatan Masyarakat 10, no. 3 (November 20, 2019): 153–62. http://dx.doi.org/10.26553/jikm.2019.10.3.153-162.

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Mulawarman University have the largest number of students in Kalimantan (37,000). This amount has the potential to be smokers supported by the non-realization of non-smoking areas in all faculties and the high circulation of cigarettes through the mobilization of street vendors and retail franchises. The purpose of this study was to find out the dominant factors influencing cigarette sales in the environment of street vendors in Mulawarman University. The design of this study is quantitative research to analyze the correlation between cigarette sales figures, types or brands of cigarettes, attributes and types of cigarette promotion media, knowledge and smoking behavior of street vendors. Statistical analysis on ordinal data using the Spearman analysis test, nominal data using contingency coefficients and ratio data using Pearson analysis. The results of this study are the average cigarette sales per day is 311 cigarettes with the highest cigarette sales is 1760 cigarettes, the type of cigarette promotion media is (55%) banners. The variables that have correlation to cigarette sales are types of cigarette sales places (p = 0.047, R = -0.257); the use of media promotion display (p = 0.002, R = 0.390) and profits from selling cigarettes in rupiah nominal (p = 0.000, R = 0.557). Limiting cigarette promotions around the campus and increasing regulation of non-smoking areas at the maximum is needed. Keywords: Sales promotion, cigarettes, smoking behavior

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Chitre, Vidya. "Big Mart Sales Analysis." International Journal of Innovative Technology and Exploring Engineering 11, no. 5 (April 30, 2022): 8–11. http://dx.doi.org/10.35940/ijitee.c9833.0411522.

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In the modern era of reaching new lengths of advancement, every company and enterprise are working on their customer demands as well as their inventory management. The models used by them help them predict future demands by understanding the pattern from old sales records. Lately, everyone is abandoning the traditional prediction models for sales forecasting as it takes a prolonged amount of time to get the expected results. Therefore now the retailers keep track of their sales record in the form of a data set, which comprises price tag, outlet types, outlet location, item visibility, item outlet sales etc.

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Aryanti, Dessy, and Johan Setiawan. "Visualisasi Data Penjualan dan Produksi PT Nitto Alam Indonesia Periode 2014-2018." Ultima InfoSys 9, no. 2 (March 19, 2019): 86–91. http://dx.doi.org/10.31937/si.v9i2.991.

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PT Nitto Alam Indonesia is a Manufacturing company engaged in screw manufacturing services. The company has a total of 134,252 rows sales and production data, but the data has never been analyzed so that the information is still not fully explored. This research proposes to make a visualization in the form of a dashboard containing sales and production data at PT Nitto Alam Indonesia in 2014 – 2018. It will be shown by using visual data mining (VDM) method with Tableau Software tools. The purpose of this study was to assist PT Nitto Alam Indonesia in analyzing sales and production data to find information that had never been explored before. The results of this study are that the patterns of sales and production can be known for the last 5 years. The sales pattern is included in the type of cycle with the highest sales peak located in the fourth quarter in 2017 at 6.552%. In addition, the performance of sales and production from 2014 to 2018 increased consistently. This research has been validated by applying User Acceptance Tests at PT Nitto Alam Indonesia.

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Chae, Jin Mie, and Eun Hie Kim. "Sales Pattern and Related Product Attributes of T-shirts." Journal of the Korean Society of Clothing and Textiles 44, no. 06 (December 31, 2020): 1053–69. http://dx.doi.org/10.5850/jksct.2020.44.6.1053.

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Kwon, Gae Eun, Dong Woo Ko, and Sang-Uk Jung. "Usage pattern of sales promotion in the Korean market." Pressacademia 4, no. 3 (August 30, 2017): 296–302. http://dx.doi.org/10.17261/pressacademia.2017.707.

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Ahn, Jin Sook, and So Young Sohn. "Customer pattern search for after-sales service in manufacturing." Expert Systems with Applications 36, no. 3 (April 2009): 5371–75. http://dx.doi.org/10.1016/j.eswa.2008.06.061.

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Riikonen, Antti, Timo Smura, and Juuso Töyli. "Price and Sales Volume Patterns of Mobile Handsets and Technologies." International Journal of Business Data Communications and Networking 11, no. 2 (July 2015): 22–39. http://dx.doi.org/10.4018/ijbdcn.2015070102.

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This article provides empirical evidence on the price and unit sales volume patterns of mobile handsets and mobile technologies, using data on the Finnish market. The prices and sales are studied on product category, product model, and product feature levels. The results show how the dynamic of prices and sales changed after the proliferation of smartphones. Otherwise, the dynamics seem to be relatively systematic supporting the use of simple assumptions in practical estimations. The median price of handset models decreases linearly, from 89% of the introduction price at peak sales in the fifth sales month to 47% in in two years. For mobile handset features, a decreasing price pattern was also identified. After a 10% market share is reached, the decrease is on average 30 Euros per 10% market share change. The prices at feature takeoff were identified to be on average at 58% of introduction price.

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Purba, Tigor Novanda, and Diky Firdaus. "DETERMINATION FOR CONSUMER PATTERNS IN BEVERAGE PRODUCT SALES USING THE FREQUENT PATTERN GROWTH ALGORITHM." IJISCS (International Journal of Information System and Computer Science) 5, no. 2 (June 14, 2021): 84. http://dx.doi.org/10.56327/ijiscs.v5i2.982.

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The culinary business is now increasingly developing and competition is increasing, so it requires a strategy to market the products to be sold. In the business sector, the results of the implementation of FP-Growth algorithm data mining can help business people find opportunities from consumption trends so that culinary business people can find out what types of products currently have the highest rating in the community so that managers can provide menu recommendations so they can increase sales turnover. The data required is a certain period of transaction data which is analyzed to produce product recommendations by the association rules. The design of this application uses HTML as the base system used in making websites, PHP as a means to develop websites, and SQL as a medium for data storage and processing. The testing process begins with the login process, then determines the support and confidence parameters, and determines the transaction time period. From the conclusion, managers can determine marketing strategies by increasing the stock of raw materials in beverage products that have the highest itemset value. Then the product with the lowest itemset value can provide promos or discounts on the purchase of goods to attract consumer buying interest.

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Wang, Junyi, and Derek T. Robinson. "Assessing the Relative and Combined Effects of Network, Demographic, and Suitability Patterns on Retail Store Sales." Land 12, no. 2 (February 16, 2023): 489. http://dx.doi.org/10.3390/land12020489.

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Despite challenges associated with acquiring proprietary sales data, there exists a wealth of literature using different types of data (e.g., spending, demographic, geographic) to understand or represent different drivers of retail store sales. We contribute to the spatial analysis of drivers of retail store sales by analyzing the relative influence of road networks, demographic, and suitability variables on retail store sales within the home-improvement sector. Results demonstrate that the inclusion of variables describing the road network pattern is more influential in predicting store sales than demographic and suitability variables with linear models (e.g., ordinary- and partial-least squares regression) as well as with a non-linear mathematical model derived using artificial intelligence. The analysis builds on previous research estimating consumer spending and a big-data suitability analysis for site selection that incorporates spatial interaction models, location quotient, and other unique criteria that are typically used in isolation. The overarching contribution of our results is the demonstration that network patterns can play a critical role in retail store sales, especially when regressions, analogs, and other simple methods for site selection are used.

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Ismarmiaty, Ismarmiaty, and Ria Rismayati. "Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm." Sinkron 8, no. 1 (January 1, 2023): 202–11. http://dx.doi.org/10.33395/sinkron.v8i1.11898.

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The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distributes daily consumer goods. The algorithm used is Frequent Pattern – Growth which is one of the algorithms in data mining used to find relationships in large data based on the number of occurrences of these data relationships. The Association Rule Mining method can be used in the retail business field, known as Market Basket Analysis. The application used for testing is Rapidminer 9.10. The research stages include: data collection, data preparation, FP-Growth algorithm implementation, result analysis and conclusions. The results of the tests carried out resulted in 819 rules with a total of 85 rules. The results of grouping strong rules based on the combination and number of products that produce information that is expected to be used as recommendations to promote products with discount, cross-selling, up-selling, product bundling and other types of promotions to increase product sales.

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Clark, Stephen D., Becky Shute, Victoria Jenneson, Tim Rains, Mark Birkin, and Michelle A. Morris. "Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles." Nutrients 13, no. 5 (April 27, 2021): 1481. http://dx.doi.org/10.3390/nu13051481.

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Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

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Damanik, Florida Nirma Sanny, Andrew Sagita, Harianto -, and Andy Syaputra. "Aplikasi Pengenalan Pola Pembelian Konsumen Menggunakan Kombinasi Algoritma FP-Growth Dan ECLAT Method (FEM)." Jurnal SIFO Mikroskil 19, no. 2 (May 29, 2018): 1–12. http://dx.doi.org/10.55601/jsm.v19i2.553.

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Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The application designed can be used to analyze consumer purchase pattern by generating associative rules using FEM algorithm through the Analysis form and printing the consumer purchase pattern through the purchase pattern report.

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Ismarmiaty, Ismarmiaty, and Ria Rismayati. "Purchase Pattern Analysis with FP-Growth Algorithm for Product Sales Promotion Recommendation Strategies." Jurnal Teknologi Informasi dan Pendidikan 15, no. 1 (October 28, 2022): 132–42. http://dx.doi.org/10.24036/jtip.v15i1.499.

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The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distributes daily consumer goods. The algorithm used is Frequent Pattern – Growth which is one of the algorithms in data mining used to find relationships in large data based on the number of occurrences of these data relationships. The Association Rule Mining method can be used in the retail business field, known as Market Basket Analysis. The application used for testing is Rapidminer 9.10. The research stages include: data collection, data preparation, FP-Growth algorithm implementation, result analysis and conclusions. The results of the tests carried out resulted in 819 rules with a total of 85 rules. The results of grouping strong rules based on the combination and number of products that produce information that is expected to be used as recommendations to promote products with discount, cross-selling, up-selling, product bundling and other types of promotions to increase product sales.

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SUZUKI, Akinori, Kenichi WATANABE, Hitoshi SHIMOII, Osamu AKITA, and Yuichi AKIMOTO. "Sales Pattern of Alcoholic Beverages in each Prefecture of Japan." JOURNAL OF THE SOCIETY OF BREWING,JAPAN 80, no. 7 (1985): 485–89. http://dx.doi.org/10.6013/jbrewsocjapan1915.80.485.

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Shah, Maulik, Nirali Shah, Anviksha Shetty, Darshan Shah, and Pradnya Gotmare. "A Comparative Study of Pattern Recognition Algorithms on Sales Data." International Journal of Computer Applications 141, no. 1 (May 17, 2016): 38–41. http://dx.doi.org/10.5120/ijca2016909463.

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Yu, Li, and Zai Fang Zhang. "Trend Analysis of Product Function Using Sequential Pattern Mining." Applied Mechanics and Materials 519-520 (February 2014): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.736.

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During the early stage of product design, it is important for design engineers to decide the most appropriate functions for various customers. To facilitate this time consuming task, sequential pattern mining is applied to uncover the useful patterns in historical database. The mined sequential patterns can reflect the dynamic change of product functions, which can help design engineers find the most suitable product functions for customers. Based on the historical sales transactions of computer, a case study is conducted to illustrate the proposed method.

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Yue, Xiaoli, Yang Wang, Yabo Zhao, and Hong’ou Zhang. "Spatial Pattern of Housing Sales Vacancy in Guangzhou’s Urban District, China." Journal of World Architecture 5, no. 6 (November 29, 2021): 47–51. http://dx.doi.org/10.26689/jwa.v5i6.2774.

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Housing vacancy can reflect the destocking degree of the real estate market. Based on the data of 57 opened residential quarters (46,622 units) from 2015 to 2018, this paper constructs a calculation formula of the sales vacancy rate and then analyzes the spatial pattern in Guangzhou’s urban district. The results show that there is obvious differentiation in the spatial pattern of housing sales vacancy in Guangzhou’s urban district, showing a higher spatial pattern in the old area and urban district and a lower spatial pattern in the core area. Subdistricts with high vacancy rates are mainly located in the east of the old area, the south and east of the urban district and near Baiyun Mountain in the north.

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Utami, Fadhila Putri, and Arief Jananto. "Implementation of the Association Rule Method using Apriori Algorithm to Recognize The Purchase Pattern of Pharmacy Drugs “XYZ”." CESS (Journal of Computer Engineering, System and Science) 8, no. 1 (January 4, 2023): 34. http://dx.doi.org/10.24114/cess.v8i1.40377.

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XYZ Pharmacy is a Special Health Service Point for employees and retirees of the XYZ company. This pharmacy carries out the process of buying and selling drugs by providing various types of drugs. The number of sales transactions in each day, resulting in sales data will increase over time. If the data is left alone, the pile of data will only become archives that are not utilized. By carrying out the data mining process, this data can be used to produce information that can be used to increase sales transactions at XYZ Pharmacy. The method used in this study is the Association Rule which functions to analyze the most sold and purchased drugs simultaneously, this analysis will be reviewed from drug sales transaction data at the XYZ Pharmacy. The application of the a priori algorithm in this study succeeded in finding the most item combinations based on transaction data and then formed an association pattern from the item combinations. By knowing the types of drugs that are often purchased together through identification of purchasing patterns, it is very useful for the XYZ Pharmacy to maintain the availability of the drugs.

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Anwar, Badrul, Ambiyar Ambiyar, and Fadhilah Fadhilah. "Application of the FP-Growth Method to Determine Drug Sales Patterns." Sinkron 8, no. 1 (January 4, 2023): 405–14. http://dx.doi.org/10.33395/sinkron.v8i1.12004.

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Pharmacies are shops that sell and mix medicines based on doctors’ prescriptions and trade medical goods. Apart from being a business actor, the pharmacy also plays a role in providing health services that are easily accessible to the public. The problem that often occurs in pharmacies selling drugs is that they are less than optimal in service to consumers. The habit of consumers buying more than one type of drug makes pharmacy staff slow in providing the drug due to the inaccurate layout of the drug. The FP-Growth method in Data Mining is a method that can provide a solution in determining drug sales patterns at pharmacies. The FP-Growth method is a method used in determining the data set that occurs most frequently together (Frequent Itemset). The research objective was to determine drug sales patterns based on drug sales transaction data so that drug layouts could be determined. This research was conducted at the Pharmacy at the Pratama Sehati Husada Clinic in Medan with drug sales transaction data from November 2021 to December 2021. The results of applying the FP-Growth method with a value of ≥ 15% as a minimum support and a value of ≥ 15% as a minimum confidence, a pattern of drug sales in pairs. The application of Data Mining with the FP-Growth method has been implemented at the Pharmacy at the Pratama Sehati Husada Clinic with the desired goals with the final result in the form of a report on the results of determining drug sales patterns.

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Kusnadi, Yahdi, and Muhamad Auliya Ahsan. "Pemilihan Strategi Penjualan Obat Apotik Antar Menggunakan Algoritma A Priori." Jurnal Teknologi Informatika dan Komputer 6, no. 2 (September 30, 2020): 74–83. http://dx.doi.org/10.37012/jtik.v6i2.213.

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Business competitors are required to think of a sales strategy to attract the attention of buyers, especially the amount of business competition that can increase sales. There are many ways used by a company to boost sales, even similar ways have been followed by other companies as competitors. Business competitors, especially Inter Pharmacies, are required to think creatively to increase sales. Using a sales database and assisted with algorithm A Priori data mining companies will know the pattern of selling goods and can determine the determination of the provision of goods and the right stock addition.

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Ciszewski, Robert L., and Philip D. Harvey. "The effect of price increases on contraceptive sales in Bangladesh." Journal of Biosocial Science 26, no. 1 (January 1994): 25–35. http://dx.doi.org/10.1017/s0021932000021039.

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SummaryIn April 1990, the prices of five brands of contraceptives in the Bangladesh social marketing project were increased, by an average of 60%. The impact on condom sales was immediate and severe, with sales for the following 12 months dropping by 46% from the average during the preceding 12 months. The effect on oral contraceptive sales was less dramatic: average sales in the year following the increases dropped slightly despite a previously established pattern of rapidly rising sales. There appears no reasonable combination of events other than the price increase itself to explain most of the difference.

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Karnila, Sri, Akbar Rizkyandi, Rio Kurniawan, and Nurjoko Nurjoko. "MARKET BASKET ANALYSIS ON TRANSACTION DATA USING THE APRIORI ALGORITHM." Jurnal TAM (Technology Acceptance Model) 13, no. 1 (July 12, 2022): 34. http://dx.doi.org/10.56327/jurnaltam.v13i1.1200.

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This research aims to get information about the relationship between sales patterns carried out by CV. Dian Abadi Jaya workshop by using APRIORI algorithms through transaction data sets carried out by customers. The subject of research is a record of shopping cart transactions made by customers, namely vehicle parts sales transactions and vehicle repair service transactions. The data collection techniques used are interviews and documentation. The criteria used in this research are a minimum of frequent itemset of 20 transactions with support criteria of 1,7%, confidence value of 40% and lift ratio value above 1. The results of the research have produced 9 sales pattern relationships with the highest confidence of 100%. The results that have been obtained are expected to help the CV. Dian Abadi Jaya workshop in making a decision for the next sale.

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Das Roy, Monami, and Shib Sankar Sana. "Random sales price-sensitive stochastic demand." Journal of Advances in Management Research 14, no. 4 (October 2, 2017): 408–24. http://dx.doi.org/10.1108/jamr-10-2016-0086.

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Purpose This research work introduces an imperfect production system where the demand is assumed to be stochastic and it is influenced by random selling price. The shift time from an “in-control” state to an “out-of-control” state is exponentially distributed. The accumulated inventory contains both perfect and defective items which are all sold with a free repair warranty (FRW) offer. Complete back ordering of shortages are taken into account. The purpose of this paper is to determine the optimal selling price and hence the optimal production lot size such that the expected profit is maximized. Design/methodology/approach The general model is discussed separately for both types of uniformly distributed selling price-sensitive demand pattern: additive type and multiplicative type. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the models. Findings This paper helps the manager to manage future situations and it may be considered as a base work for the researchers to work in this direction. Research limitations/implications The main limitation of this model is to consider a single item for a single channel system. There are many correlated issues that need to be further investigated. The future study in this direction may include the consideration of multi-items, diverse demand pattern with different types of price distributions. Originality/value In the production inventory literature, plenty of articles are available considering imperfect production but none of them have considered selling price-sensitive stochastic demand where the sales price is random in character under an FRW offer.

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Williams, N. J., J. Sewel, and F. Twine. "Council House Sales and Residualization." Journal of Social Policy 15, no. 3 (July 1986): 273–92. http://dx.doi.org/10.1017/s0047279400015166.

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ABSTRACTIt has been argued that council house sales will contribute towards a more general process of residualization of public sector housing. Empirical evidence is presented in this context derived from surveys of purchasers and non-purchasers of council dwellings in the city of Aberdeen. This evidence confirms that purchasers and non-purchasers exhibit different socio-economic characteristics and after only four years of the Right to Buy legislation significant numbers of households in social classes I, II and III have left the public sector via the mechanism of sales. The small number of sales relative to the stock as a whole, however, has meant that the overall contribution of sales towards residualization has been small. This evidence from Aberdeen is compared to evidence from elsewhere and related to the varying pattern of sales across the country as a whole.

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Razvodovsky, Y. E. "Beverage-Specific Alcohol Sale and Suicide in Russia." Crisis 30, no. 4 (July 2009): 186–91. http://dx.doi.org/10.1027/0227-5910.30.4.186.

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Background: The high suicide rate in Russia and its profound fluctuation over the past decades have attracted considerable interest. There is growing evidence that beverage preference and binge-drinking patterns, i.e., excessive consumption of strong spirits, results in a quicker and deeper level of intoxication, which increases the propensity for the alcohol-related suicide. In line with this evidence, we assumed that higher levels of vodka consumption, in conjunction with binge-drinking patterns, would result in a close, aggregate-level association between vodka sales and suicide in Russia. Aims and Methods: To test this hypothesis, trends in beverage-specific alcohol sales per capita and suicide rates from 1970 to 2005 in Russia were analyzed employing ARIMA time-series analysis. Results: The results of the time-series analysis suggested that a 1 liter increase in overall alcohol sales would result in a 4% increase in the male suicide rate and a 2.8% increase in the female suicide rate; a 1 liter increase in vodka sales would increase the suicide rate by 9.3% for men and by 6% for women. Conclusions: This study replicates previous findings from other settings, which suggest that suicide rates tend to be more responsive to changes in distilled spirits consumption per capita than to the total level of alcohol consumption. Assuming that drinking spirits is usually associated with intoxication episodes, these findings provide additional evidence that the drinking pattern is an important determinant in the relationship between alcohol and suicide. The outcomes of this study also provide support for the hypothesis that suicide and alcohol are closely connected in cultures where an intoxication-oriented drinking pattern prevails and adds to the growing body of evidence that alcohol plays a crucial role in the fluctuation in suicide mortality rates in Russia during recent decades.

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East, Robert. "Online Grocery Sales after the Pandemic." International Journal of Market Research 64, no. 1 (November 3, 2021): 13–18. http://dx.doi.org/10.1177/14707853211055047.

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In this paper, the increase in online grocery ordering in the UK during the COVID-19 pandemic is examined, and a prediction is made that is opposed to the balance of opinion expressed online. In their online comments, most practitioners claim that the increased use of the Internet for ordering groceries for home delivery will be sustained and will continue to grow after the risk of disease has subsided. Given the pattern of consumer behaviour in another field, discount purchasing, it seems more likely that online grocery ordering will fall back and then continue to grow at a modest pace, as it did before the pandemic.

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Perdana, A. W., J. M. Affan, Q. Hasri, E. Miswar, C. M. N. ‘Akla, and A. S. Batubara. "Analysis of distribution patterns and marketing margins of capture fishery products at the Ujong Baroh fishing port, West Aceh district, Indonesia." IOP Conference Series: Earth and Environmental Science 869, no. 1 (November 1, 2021): 012047. http://dx.doi.org/10.1088/1755-1315/869/1/012047.

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Abstract The objectives to be achieved in this study are to determine the distribution pattern of capture fishery products at Ujong Baroh Fishing Port and determine an efficient distribution pattern in conducting a marketing system by knowing the margin received. The method used in this study is a survey method with purposive sampling data collection techniques. The data analysis used in this research is descriptive analysis and marketing margin analysis. The descriptive analysis used to describe the distribution pattern in Ujong Baroh Fishing Port. Marketing margin analysis is used to measure the profits of each actor involved in the catch distribution pattern. Results of this study obtained 2 distribution patterns that occur in Ujong Baroh Fishing Port. The first distribution pattern is fishermen to fish collectors to wholesalers to retailers to consumers; second, fishermen to fish collectors to traders to consumers. Both distribution patterns at Ujong Baroh Fishing Port are correct because these patterns get a marketing margin value of < 50% and a fisherman’s profit sharing value of > 50%, where the distribution pattern is classified as efficient. The party who benefits from both distribution patterns is fish collectors because they get a sales profit of 7%.

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Islam, T., C. van Weezenbeek, R. Vianzon, A. M. C. G. Garfin, T. Hiatt, W. J. Lew, and K. Tisocki. "Market size and sales pattern of tuberculosis drugs in the Philippines." Public Health Action 3, no. 4 (December 21, 2013): 337–41. http://dx.doi.org/10.5588/pha.13.0094.

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Sun, Lijun, Xiuwu Xing, Yaxian Zhou, and Xiangpei Hu. "Demand Forecasting for Petrol Products in Gas Stations Using Clustering and Decision Tree." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3 (May 20, 2018): 387–93. http://dx.doi.org/10.20965/jaciii.2018.p0387.

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Demand forecasting for petrol products in gas stations is crucial to the planning of initiative distribution of petrol products, especially to the stability of product supply in petroleum companies. In this paper, a novel scheme of demand forecasting based on clustering and a decision tree is proposed, which uses a decision tree and integrates the results of clustering validity indices. First, the proposed scheme uses a k-means algorithm to divide the sales data into multiple disjointed clusters, evaluates the clustering result of the daily sales curve of a product according to seven validity indices and determines the optimal number of clustering. Next, the relationship between the sales pattern and the relevant influence factors is described using a decision tree, which can categorize a future day’s sales pattern with these factors into the most suitable cluster to predict the quantity of the demand and the peak demand time windows for each gas station. Finally, three months’ worth of sales data is collected from a gas station in Dalian city, China, to illustrate the proposed forecasting scheme. Experimental results demonstrate that the scheme is an effective alternative for the demand forecasting for petrol products because it outperforms three other selected methods.

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Yu, Shidong, Dongsheng Yang, Ying Hao, Mengjia Lian, and Ying Zang. "Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 11 (February 25, 2020): 2059036. http://dx.doi.org/10.1142/s0218001420590363.

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Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

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Zein Vitadiar, Tanhella, Farikhin Farikhin, and Bayu Surarso. "Production Planning and Planting Pattern Scheduling Information System for Horticulture." E3S Web of Conferences 31 (2018): 10004. http://dx.doi.org/10.1051/e3sconf/20183110004.

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This paper present the production of planning and planting pattern scheduling faced by horticulture farmer using two methods. Fuzzy time series method use to predict demand on based on sales amount, while linear programming is used to assist horticulture farmers in making production planning decisions and determining the schedule of cropping patterns in accordance with demand predictions of the fuzzy time series method, variable use in this paper is size of areas, production advantage, amount of seeds and age of the plants. This research result production planning and planting patterns scheduling information system with the output is recommendations planting schedule, harvest schedule and the number of seeds will be plant.

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Cai, Man Yi, and Bing Pan. "Research on Data Aggregation Application Based on Mashup." Applied Mechanics and Materials 644-650 (September 2014): 3279–82. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3279.

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Recently, the development of electronic commerce has gotten better and better. But the sales tool of e-commerce platforms is not yet mature. In this paper, a sales tool is implemented by using mashup technology combined with MVC pattern to aggregate data from different data sources, such as gostats.com, k780.com. It can help vendors and develop reasonable sales strategy in the e-commerce, by tracking the IP addresses of the visitors on product pages and display the distribution by calling Google Map API.

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Bils, Mark, and James A. Kahn. "What Inventory Behavior Tells Us About Business Cycles." American Economic Review 90, no. 3 (June 1, 2000): 458–81. http://dx.doi.org/10.1257/aer.90.3.458.

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The countercyclical pattern of inventory-sales ratios is a striking feature of inventory behavior. In a model where inventories are productive for sales, both the markup of price over marginal cost and expected changes in marginal cost are key determinants of that ratio. This paper argues that costly variation in factor utilization gives rise to countercyclical markups in production-to-stock manufacturing industries. The markup turns out to be more important than intertemporal substitution in explaining the behavior of inventory-sales ratios. (JEL E22, E32)

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Akay, Ozzy, Mark D. Griffiths, and Drew B. Winters. "An Examination of Two Competing Hypotheses: For the Demand for Lottery Tickets." Journal of Gambling Business and Economics 2, no. 1 (January 2, 2013): 77–102. http://dx.doi.org/10.5750/jgbe.v2i1.526.

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We extend previous research on higher sales for end-of-the-week lottery drawings to a longer time series and to different lotteries. We find higher sales for end-of-the-week lotteries drawings with Wednesday/Saturday drawings and Tuesday/Friday drawings. Additionally, higher Friday sales from daily lotteries along with results from bonus play opportunities and intraday lottery sales provide evidence suggesting that the leisure time hypothesis is an incomplete explanation for the observed phenomenon. We offer an alternate explanation related to the preferred habitat for liquidity and suggest that an individual’s pool of discretionary funds is largest immediately following pay days. It is the fact that the most common pay pattern is weekly or biweekly with payment on Fridays which likely results in higher sales for end-of-the-week lottery drawings.

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Somya, Ramos, Edi Winarko, and Sigit Priyanta. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3422–32. http://dx.doi.org/10.11591/eei.v11i6.4021.

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.

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Setiawan, Dwi, Eko Sediyono, and Irwan Sembiring. "Pemanfaatan Metode Association Rules dan Holt-Winter Multiplicative untuk Meningkatkan Peluang Penjualan Obat Pertanian." JURNAL SISTEM INFORMASI BISNIS 10, no. 1 (March 25, 2020): 46–55. http://dx.doi.org/10.21456/vol10iss1pp46-55.

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The competition level between companies on executing product marketing is rapidly increasing, so the companies have to understand the importance of correlation between external environments of company with consumer’s needs. One of the efforts that can be done is by utilizing data warehouse and the application of infrastructure in information and technology field. This research combined Association Rules method to extracting pattern and finding every possibility that potential to increase sales and Holt-Winter Multiplicative method to estimate the alteration of trend on the seasonal data. After passed through data processing process by using RapidMiner tools, information that consists of correlation pattern between rule that describe the comparison of product and the sales working area and season that affects the product sale. The pattern used by company to know which product is often purchased by customer. Besides that, this research produces changing trend data of PT ABC’s product that generated by result of previous data comparison with forecast data. Based on value of error rate Mean Absolute Percentage Error (MAPE) in estimating forecast result on the PT ABC’s sales transaction data during 3 years, it shows good level of accuracy. Result of data test, by considering rule that formed and forecast result so the company can control and manage product in order to avoid incorrect sales. This thing will effect on repression of operational cost and PT ABC can identify available opportunities to increase sale of agricultural medicine.

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Vishwakarma, Sagar, and Dr S. C. Solanki. "Predicting sales during COVID using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2481–89. http://dx.doi.org/10.22214/ijraset.2022.41822.

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Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.

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Yuliana, Yuliana. "Jual Beli Bahan Bakar Premium Eceran." Al-Masharif: Jurnal Ilmu Ekonomi dan Keislaman 8, no. 1 (July 10, 2020): 68–85. http://dx.doi.org/10.24952/masharif.v8i1.2589.

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Abstract This research is motivated, because of the reduction in the quantity of retail premium perliter quantity. Phenomena based on field survey that there are some patterns of retail premium that become the background, namely retail premium sales pattern with the first pattern, retail premium sales with bottled patterns accompanied by prices, and retail premium sales with bottled patterns accompanied by a reduction in quantity. As for the formulation of the problem how to buy and sell retail premium fuel? This research aims to find out which muamalah is true or legitimate, in accordance with Islamic law based on the quran hadits, for the general public towards buying and selling retail premium fuel. This research is a qualitative research that is collecting data source from the subject under study. After conducting field research and getting conclusions using the qiyas method to explore syara’ law from what is concluded from the field. AbstrakPenelitian ini dilatarbelakangi, karena adanya pengurangan takaran kuantitas perliter premium eceran. Fenomena yang berdasarkan surve di lapangan bahwa ada beberapa pola premium eceran yang menjadi latar belakang yaitu pola penjualan premium eceran dengan pola pertamini, penjualan premium eceran dengan pola botolan disertai harga, dan penjualan premium eceran dengan pola botolan disertai pengurangan kuantitas. Adapun rumusan masalah bagaimana Jual Beli Bahan Bakar Premium Eceran? Penelitian ini bertujuan untuk mengetahui bermuamalah yang benar atau salah, sudah sesuai dengan syariat Islam berlandaskan al-Quran dan Hadis, bagi masyarakat umum terhadap jual beli bahan bakar minyak premium eceran. Penelitian ini merupakan penelitian kualitatif (field reseach) yaitu mengumpulkan data yang bersumber dari subjek yang diteliti. Setelah melakukan penelitian lapangan dan mendapatkan kesimpulan mengunakan metode qiyas untuk menggali hukum syara’ dari apa yang simpulkan dari lapangan.

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Journal articles on the topic 'SALES PATTERN'

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