Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads

Afandi Nur Aziz  Thohari; Rima Dias  Ramadhani

doi:10.47709/cnahpc.v4i2.1488

Authors

Afandi Nur Aziz Thohari Politeknik Negeri Semarang, Indonesia
Rima Dias Ramadhani Institut Teknologi Telkom Purwokerto, Indonesia

DOI:

10.47709/cnahpc.v4i2.1488

Keywords:

Customer Transaction, Machine Learning, Prediction, Performance Comparison

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Abstract

The application of machine learning has been used in various sectors, one of which is digital marketing. This research compares the performance of six machine learning algorithms to predict customer transaction decisions. The six algorithms used for comparison are Perceptron, Linear Regression, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. The dataset is obtained from Facebook ads transaction data in 2020. The goal is to get a model that has the best performance so that it can be deployed to the web. The method that is used to compare the results is a confusion matrix and also uses visualization of the model to get the prediction error that occurred. Based on the test results, the random forest algorithm has the highest accuracy, recall, and f1-score values, with scores of 96.35%, 95.45%, and 93.32%. The highest precision value was generated by the logistic regression algorithm, which was 94.44%. Based on the data visualization presented by the random forest algorithm, it has the least prediction errors, there are four data. Therefore, it can be concluded that the random forest algorithm has the best performance because it has the highest value in the three confusion matrix measurements and the smallest data prediction error. The model of the random forest algorithm is deployed to the web platform and can be accessed at the link iklan-sosmed.herokuapp.com.

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References

Berry, M. W., Mohamed, A., & Yap, B. W. (Eds.). (2019). Supervised and unsupervised learning for data science. Springer Nature.

Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20-28.

Dhankhad, S., Mohammed, E., & Far, B. (2018, July). Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In 2018 IEEE international conference on information reuse and integration (IRI) (pp. 122-125). IEEE.

Dokmanic, I., Parhizkar, R., Ranieri, J., & Vetterli, M. (2015). Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Processing Magazine, 32(6), 12-30.

Fitrianah, D., Dwiasnati, S., & Baihaqi, K. A. (2021). Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes. Faktor Exacta, 14(2), 92-99.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

Hindrayani, K. M., Anjani, A., & Nurlaili, A. L. (2021). Penerapan Machine Learning pada Penjualan Produk UMKM: Studi Literatur. SENADA, 1(01), 19-23.

Tamba, S. P. (2022). PREDIKSI PENYAKIT GAGAL JANTUNG DENGAN MENGGUNAKAN RANDOM FOREST. Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM PRIMA), 5(2), 176-181.

Kudryashov, N. A. (2015). Logistic function as solution of many nonlinear differential equations. Applied Mathematical Modelling, 39(18), 5733-5742.

Kullarni, V. Y., & Sinha, P. K. (2013). Random Forest classifier: a survey and future research directions. Int. J. Adv. Comput, 36(1), 1144-1156.

Leonardo, R., Pratama, J., & Chrisnatalis, C. (2020). Perbandingan Metode Random Forest Dan Naïve Bayes Dalam Prediksi Keberhasilan Klien Telemarketing. Jurnal Teknologi Dan Ilmu Komputer Prima (Jutikomp), 3(2), 455-459.

Lin, E., Lin, C. H., & Lane, H. Y. (2021). Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions. Scientific Reports, 11(1), 1-9.

Maguire, P., Moser, P., & Maguire, R. (2020). Are people smarter than machines?. Croatian Journal of Philosophy, 20(1 (58)), 103-124.

Paul, A., Mukherjee, D. P., Das, P., Gangopadhyay, A., Chintha, A. R., & Kundu, S. (2018). Improved random forest for classification. IEEE Transactions on Image Processing, 27(8), 4012-4024.

Saiful, A. (2021). Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 8(1), 41-50.

Sokolova, M., & Lapalme, G. (2009, September). Classification of opinions with non-affective adverbs and adjectives. In Proceedings of the International Conference RANLP-2009 (pp. 421-427).

Vembandasamy, K., Sasipriya, R., & Deepa, E. (2015). Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), 441-444.

Yudhistiro, K. (2017). Pemanfaatan Neural Network Perceptron pada Pengenalan Pola Karakter. SMATIKA JURNAL, 7(02), 21-25.