Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method
Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method
DOI:
10.47709/cnapc.v2i1.353Keywords:
Sales, Data MiningDimension Badge Record
Abstract
The sale is part of the marketing that determine the survival of the company. With the sale, the company can achieve the goals or targets. To be a company that continues to grow in motorcycle sales, the company should be able to compete in increasing sales volume. Starting from the launch prodak the best in sophistication motorcycles, up to a very attractive price cuts the attention of consumers. Things like that already sanggat often do, so the company can still compete, Motorcycles is a two-wheeled transfortasi tool used more and more common people. From teenagers to old orag, not infrequently motorcycle including important sanggat needs. If we do not have it feels very hard in activity quickly. Make sales without any restriction of sales data accumulate, until finally overwhelmed the company in terms of taking care of customer files. To find the most sales required Apriori Algorithm. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules.
Downloads
Abstract viewed = 303 times
References
PM Hasugian, "Testing Algorithm Apriori By Application Weka In," J. Mantik Penusa, 2017.
TR Vulandari, "Stages of Data Mining," in Data Mining, Theory and Applications Rapirminer, 2017.
SF Rodiyansyah, "Apriori Algorithm to Shopping Cart Analysis on Sales Transaction Data," Infotech,, 2015.
J. Ipmawati, Kusrini, and E. Taufiq Lutfi, "Comparison of Text Mining Classification Techniques On Sentiment Analysis," Indones. J. Netw. Secur., 2017.
A. -, Marisa F., and D. Purnomo, "Application of Apriori Algorithm Against Data Warehouse Store Sales in BM," JOINTECS (Journal Inf. Technol. Comput. Sci., 2016.
I. Kurnawan, Marisa F., and D. Purnomo, "Implementation of Data Mining With Apriori Algorithm For," J. Teknol. and Manaj. Inform., 2018.
I. Mubarok, Marisa F., and D. Purnomo, "apriori algorithm implementation for determining the satisfaction of students of service online university SIM Widyagama Malang," J. Teknol. and Manaj. Inform., 2017.
A. Nursikuwagus and T. Hartono, "Apriori algorithm implementation for web-based sales analysis by," Symmetrical J. Tech. Mechanical, Electrical and Sciences Komput., 2016.
SZ Ninuk Wiliani, "cashier ticket design applications watch the ball bareng X X cashier IN A location with visual basic 2010 and MYSQL," etc. Nusant. PGRI Kediri, 2017.
A. Anthony, AR Tanaamah, and AF Wijaya, "Analysis and Design of Information Systems Warehouse Sales Based on Stock-Based Client Server (Case Study Grocery Store 'Restu you')," J. Teknol. Inf. and Science Komput., 2017.