Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia

Research Article

Application for Monitoring the Prices of Basic Foods in Traditional Markets by the Department of Industry and Trade Using the Machine Learning Algorithm

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  • @INPROCEEDINGS{10.4108/eai.21-9-2023.2342904,
        author={Lidya  Wati and Niky  Hardinata and Muhammad Ridho Nosa and Khairus  Suhada},
        title={Application for Monitoring the Prices of Basic Foods in Traditional Markets by the Department of Industry and Trade Using the Machine Learning Algorithm},
        proceedings={Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia},
        publisher={EAI},
        proceedings_a={ABEC},
        year={2024},
        month={2},
        keywords={sembako machine learning k-mean cluster},
        doi={10.4108/eai.21-9-2023.2342904}
    }
    
  • Lidya Wati
    Niky Hardinata
    Muhammad Ridho Nosa
    Khairus Suhada
    Year: 2024
    Application for Monitoring the Prices of Basic Foods in Traditional Markets by the Department of Industry and Trade Using the Machine Learning Algorithm
    ABEC
    EAI
    DOI: 10.4108/eai.21-9-2023.2342904
Lidya Wati1,*, Niky Hardinata1, Muhammad Ridho Nosa1, Khairus Suhada1
  • 1: Politeknik Negeri Bengkalis
*Contact email: lidyawati@polbeng.ac.id

Abstract

The trade sector at the Bengkalis Regency Trade and Industry Service and also the general public need reference information on basic food prices that are reliable and easily accessible via the web because so far the public has often speculated about food prices due to a lack of accurate information about developments in prices prevailing at that time. This problem is often exploited by unscrupulous traders, especially traders in traditional markets, by raising prices inappropriately. This study aims to build a basic food price data system application to support the work of supervising and monitoring prices in traditional markets in 11 sub-districts in the Bengkalis Regency. This study applies the machine learning method with clustering or grouping techniques using the K-Means algorithm on food price data. The results of the grouping of basic food prices, it is divided into 3 clusters, namely rising prices, falling prices, and fixed prices. Prices increased in 3 sub-districts (Bengkalis, Mandau, Bathin Solapan), prices remained constant in 7 sub-districts (Siak Kecil, Rupat Utara, Bukit Batu, Laksmana, Talang Mandau) and prices fell in 1 sub-district (Pinggir). Cluster evaluation uses the Silhouette Coefficient and Dunn Index, with the results of optimal k = 2 with a value of 0.55 for silhouette and optimal k = 4 with a value of 0.67 for Dunn Index.