Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan

Genesis Sembiring Depari

Abstract


The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting.

Keyword: social media advertisement, random forest, support vector machine, data mining 


References


Bačík, R., Fedorko, R., Kakalejčík, L. and Pudło, P., 2015. The importance of Facebook ads in terms of online promotion. Journal of applied economic sciences, 10(5), p.35.

Ballings, M. and Van den Poel, D., 2015. CRM in social media: Predicting increases in Facebook usage frequency. European Journal of Operational Research, 244(1), pp.248-260.

Barbier, G. and Liu, H., 2011. Data mining in social media. In Social Networks data analytics (pp. 327-352). Springer, Boston, MA.

Barreto, A.M., 2013. Do users look at banner ads on Facebook?. Journal of Research in Interactive Marketing.

Cortes, C. and Vapnik, V., 1995. Support-vector networks.

Machine learning, 20(3), pp.273-297.

Curran, K., Graham, S. and Temple, C., 2011. Advertising on Facebook. International Journal of E-business Development, 1(1), pp.26-33.

Hanna, R., Rohm, A. and Crittenden, V.L., 2011. We’re all connected: The power of the social media ecosystem. Business Horizons, 54(3), pp.265-273.

Hastie, T., Tibshirani, R. and Friedman, J., 2009. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.

Hastie, T., Tibshirani, R. and Friedman, J., 2009. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.

Ho, T.K., 1998. The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 20(8), pp.832-844.

Hsu, C.C., Lee, Y.C., Lu, P.E., Lu, S.S., Lai, H.T., Huang, C.C., Wang, C., Lin, Y.J. and Su, W.T., 2017, October. Social media prediction based on residual learning and random forest. In Proceedings of the 25th ACM international conference on Multimedia (pp. 1865-1870).

Keen, P.G., 1980. Decision support systems: a research perspective. In Decision support systems: Issues and challenges: Proceedings of an international task force meeting (pp. 23-44).

Kietzmann, J.H., Hermkens, K., McCarthy, I.P. and Silvestre, B.S., 2011. Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), pp.241-251.

Mangasarian, O.L., 2001, July. Data mining via support vector machines. In IFIP Conference on System Modeling and Optimization (pp. 91-112). Springer, Boston, MA.

Silver, M.S., 1991. Decisional guidance for computer-based decision support. MIS quarterly, pp.105-122.

Stone, M., 1977. An asymptotic equivalence of choice of model by cross‐validation and Akaike's criterion. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), pp.44-47.

Turban, E., Sharda, R., Delen, D., & Efraim, T. (2011). Decision support and business intelligence systems (9th ed.). Pearson.


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