Data Mining Sales of Skin Care Products Using the K-Means Method
DOI:
10.33395/sinkron.v8i1.12007Keywords:
Data Mining, K-Means, Clustering, Skin Care, Rapid Miner.Abstract
Data mining is a form of method advancement in computerization that can dig past data into very valuable information. The problem in this study is that the sale of beauty and skincare carried out by Toko Hayati Store is still done manually so that it can cause it to not match the stock in the storage warehouse with changing market demand. Data mining with the K-Means method is one solution to this problem by grouping similar data, in this study grouping into two, namely best-selling and unsold products. The purpose of this study is that the store can provide stock of products in the warehouse according to market demand. Using a sample of 30 data resulted in 18 data as skin care products were not selling well and 12 data as skin care products were not selling well. With the results of a 100% similarity between manual calculations and using the rapid miner application, it can be concluded that the K-Means algorithm can be used as a solution to the problems that exist in the Toko Hayati Store.
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Adha, L. H., Asyhadie, Z., & Kusuma, R. (2020). DIGITALIZATION OF INDUSTRY AND ITS EFFECT ON EMPLOYMENT AND LABOR RELATIONS IN INDONESIA. Journal of Legal Compilation, 5(2).
Al-hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques : A comprehensive review from 2009 to 2019. Computer Science Review Journal, 40, 1–23. https://doi.org/10.1016/j.cosrev.2021.100402
Aldo, D., Lelisa Army, W., Lestari, W. J., Saputra, A. H., & Munir, Z. (2022). Development of Integrated Information System for Batam Tourism Industry Implements Website-Based User Centered Design. JOURNAL OF INFORMATICS MEDIA BUDIDARMA, 6(2). https://doi.org/10.30865/mib.v6i2.3849
Amalia, N., Rachman, O., & Surahman, R. (2020). Artificial Intelligence Based Agricultural Information System (E-Tandur). JAMIKA, 10(April), 1–10. https://doi.org/10.34010/jamika.v10i1
Arifah, D. M. Al, Ramadlana, A. N., Oktawandira, D., Pradana, M. A. Y., Syaifudin, A. I., & Aldo, D. (2022). INTERACTIVE MULTIMEDIA-BASED TOURIST ATTRACTION INFORMATION APPLICATION IN PURWOKERTO. JURSIMA (Journal of Information Systems and Management), 10(1).
Chen, J. B., Hu, W., & Li, K. Y. and G. N. (2021). A Log Analysis Technology Based on FP-growth Improved Algorithm. Nternational Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA), 219–223. https://doi.org/0.1109/CAIBDA53561.2021.00053
Cong, Y. (2022). Research on Data Association Rules Mining Method based on improved a priori algorithm. 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 373–376. https://doi.org/10.1109/ICBASE51474.2020.00085
Ependi, S., & Akbar, M. (2021). IMPLEMENTATION OF DATA MINING IN PRODUCT SALES USING A PRIORI ALGORITHM. Bina Darma Conference on Computer Science, 220–225.
Gong, J. (2021). In-depth Data Mining Method of Network Shared Resources Based on K-Means CLustering. ICMTMA, 13, 694–698. https://doi.org/10.1109/ICMTMA52658.2021.00160
Handoko, S., Fauziah, F., & Handayani, E. T. E. (2020). Data Mining implementation to determine the sales level of Telkomsel data packages using the K-means clustering method. Scientific Journal of Technology and Engineering, 25(1), 76–88. https://doi.org/10.35760/tr.2020.v25i1.2677
Harahap, B. (2019). Application of the K-Means algorithm to determine building materials is in demand (case study on UD. YD Indarung Building Shop). 394–403.
Hendri, & Oscar, D. (2021). Application of C4.5 Algorithm in Measuring Visitor Satisfaction with Facilities in Jakarta Wildlife Park. Infortech, 3(1).
Juansyah. (2018). E-MONITORING OF BASIC FOOD PRICES AT THE TRADE AND INDUSTRY OFFICE OF MUSI BANYUASIN REGENCY. Journal of Informanika, 4(2).
Karsito, & Sari, W. M. (2018). PREDICTION OF POTENTIAL SALES OF DELIFRANCE PRODUCTS USING THE NAIVE BAYES METHOD AT PT. SUSTAINABLE FOOD. SIGMA – Journal of Technology, 9(September), 67–78.
Narastri, M. (2020). FINANCIAL TECHNOLOGY (FINTECH) IN INDONESIA IS VIEWED FROM THE ISLAMIC PERSPECTIVE OF Maulidah. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 2(2), 155–170.
Noviyanto. (2020). Application of Data Mining in Grouping the Number of Deaths of COVID-19 Patients by Country on the Asian Continent. Paradigm, 22(2).
Pranata, B. S., & Utomo, D. P. (2020). Application of FP-Growth Algorithm Data Mining for Spare Parts Inventory in Motor Repair Shops (Case Study of Sinar Service Workshop). Bulletin of Information Technology (BIT), 1(2), 1–8.
Rachmatullah, N., & Purwani, F. (2022). Analysis of the Importance of Digitalization & Information Technology Infrastructure in Government Institutions: E-Government. FASILKOM, 12(1), 14–19.
Renhoran, B. S., Nurhandayani, N., & Septiana, L. (2018). APPLICATION OF THE C4.5 ALGORITHM TO DETERMINE STOCK DATA AND MATERIAL DEMAND TARGETS THAT ARE MOST NEEDED BY LOGISTICS WAREHOUSES AT PT PLN (Persero) KEBON JERUK AREA. 12(2), 13–20.
Risal, A. A. N., Yusuf, N. I., Kaswar, A. B., & Adiba, F. (2021). Application of Data Mining in Classifying Covid-19 Case Rates in South Sulawesi Using the Naive Bayes Algorithm. INDONESIAN JOURNAL OF FUNDAMENTAL SCIENCES, 7(1), 18–28.
Saputra, R., Yahya, S., & Malang, S. (2021). Application of Virtual Reality Technology in Residential Property. SISFOTEK, 5(1), 307–315.
Sitoto, Y., Allo, T., Sofica, V., Hasan, N., & Septiani, M. (2022). The use of the Naïve Bayes method in classifying unemployment in Bojong Kulur Village. Ferris wheel of informatics, 10(1), 30–35.
Suhartini, & Yuliani, R. (2021). Application of Data Mining to Cluster Data on Poor People Using K-Means Algorithm in Bagik Endep Sukamulia Timur Hamlet. Infotech : Journal of Informatics and Technology, 4(1), 39–50.
Syahril, M., Erwansyah, K., & Yetri, M. (2020). Application of data mining to determine the sales pattern of school equipment on the wigglo brand using a priori algorithm. J-SISKO TECH, 3(1), 118–136.
Yusuf, B., Qalbi, M., Dwitawati, I., & Ellyadi, M. (2020). IMPLEMENTATION OF THE NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN PREDICTING THE ACADEMIC PERFORMANCE OF AR-RANIRY STATE ISLAMIC UNIVERSITY STUDENTS. Cyberspace: Journal of Information Technology Education, 4(1), 50–58.
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