Prediksi klik tayang adalah salah satu kriteria yang paling sering digunakan untuk menentukan efektivitas suatu iklan. Dalam produksi periklanan, prediksi klik tayang sangat berpengaruh bagi perusahaan yang memasang iklan tersebut. Perusahaan juga harus tau, apakah iklan tersebut benar-benar dilihat oleh pengguna, atau hanya sekedar meng klik nya saja. Selain memprediksi klik tayang suatu iklan, pemakaian model atau algoritma yang digunakan juga sangat penting dalam menganalisa klik tayang yang terjadi. Dalam penelitian ini menggunakan beberapa model yang di uji coba. Adapun model yang digunakan dalam pelatihan antara lain
Bagged Decision Trees, Extra Trees, Random Forest, AdaBoost, Stochastic Gradient Boosting dan Voting Ensemble. Adapun model yang menghasil akurasi optimal yaitu menggunakan AdaBoost, setelah itu di Hyperparameter Tuning kan dengan Grid Search dengan nilai accuracy 96,50%, AUC 96,51%, recall 94,05% dan precision 97,876%.
Tesis
[1] T. Çakmak, A. T. Tekin, Ç. Şenel, T. Çoban, Z. E. Uran, and C. Okan Sakar, “Accurate prediction of advertisement clicks based on impression and clickthrough rate using extreme gradient boosting,” ICPRAM 2019 - Proc. 8th Int. Conf. Pattern Recognit. Appl. Methods, no. Icpram, pp. 621–629, 2019, doi: 10.5220/0007394306210629.
[2] B. Xia, X. Wang, T. Yamasaki, K. Aizawa, and H. Seshime, “Deep neural network-based click-through rate prediction using multimodal features of online banners,” Proc. - 2019 IEEE 5th Int. Conf. Multimed. Big Data, BigMM 2019, pp. 162–170, 2019, doi: 10.1109/BigMM.2019.00-29.
[3] A. Lozano-Diez, R. Zazo, D. T. Toledano, and J. Gonzalez Rodriguez, “An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition,” PLoS One, vol. 12, no. 8, pp. 1–22, 2017, doi: 10.1371/journal.pone.0182580.
[4] A. S. Kwekha-Rashid, H. N. Abduljabbar, and B. Alhayani, “Coronavirus disease (COVID-19) cases analysis using machine-learning applications,” Appl. Nanosci., no. 0123456789, 2021, doi: 10.1007/s13204-021-01868-
7. [5] K. Gao, G. Mei, F. Piccialli, S. Cuomo, J. Tu, and Z. Huo, “Julia language in machine learning: Algorithms, applications, and open issues,” Comput. Sci. Rev., vol. 37, p. 100254, 2020, doi: 10.1016/j.cosrev.2020.100254.
[6] Y. Pristyanto, “Penerapan Metode Ensemble Untuk Meningkatkan Kinerja Algoritme Klasifikasi Pada Imbalanced Dataset,” J. Teknoinfo, vol. 13, no. 1, p. 11, 2019, doi: 10.33365/jti.v13i1.184.
[7] S. Saraswathi, V. Krishnamurthy, D. Venkata Vara Prasad, R. K. Tarun, S. Abhinav, and D. Rushitaa, “Machine learning based ad-click prediction system,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 3646–3648, 2019, doi: 10.35940/ijeat.F9366.088619.
[8] W. tri Retno, Data Mining : Teori dan Aplikasi Rapidminer, 1st ed. YOGYAKARTA: Gava Media, 2017.
[9] B. Harahap, E.H., Muflikhah, L., & Rahayudi, “Implementasi Algoritma Support Vector Machine(SVM) Untuk Penentuan Seleksi Atlet Pencak Silat,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. Vol. 2, No, pp. 3843–3848, 2018.
[10] N. Siti, Y. Lestari, Hermanto, N. Elah, and S. Mahmud, “Penerapan algoritma decision tree c4.5 dalam penerimaan guru pada smk sirajul falah parung,” STIKOM Cipta Karya Inform., vol. 11, no. 2, pp. 192–198, 2018.
[11] H. Ding, G. Li, X. Dong, and Y. Lin, “Prediction of Pillar Stability for Underground Mines Using the Stochastic Gradient Boosting Technique,” 4444 IEEE Access, vol. 6, no. c, pp. 69253–69264, 2018, doi: 10.1109/ACCESS.2018.2880466.
[12] L. Abhishek, “Optical character recognition using ensemble of SVM, MLP and extra trees classifier,” 2020 Int. Conf. Emerg. Technol. INCET 2020, pp. 7–10, 2020, doi: 10.1109/INCET49848.2020.9154050.
[13] A. Primajaya and B. N. Sari, “Random Forest Algorithm for Prediction of Precipitation,” Indones. J. Artif. Intell. Data Min., vol. 1, no. 1, p. 27, 2018, doi: 10.24014/ijaidm.v1i1.4903.
[14] J. Fiaidhi*, T. Wadiwala, and V. Trikha, “Analyzing Brain Signals to Predict Seizure Events using Machine Learning Techniques,” Int. J. Bio-Science Bio-Technology, vol. 12, no. 1, pp. 35–46, 2020, doi:
10.21742/ijbsbt.2020.12.1.05.
[15] E. K. Ampomah, Z. Qin, and G. Nyame, “Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement,” Inf., vol. 11, no. 6, 2020, doi: 10.3390/info11060332.
[16] S. Bhanu Koduri, L. Gunisetti, C. Raja Ramesh, K. V. Mutyalu, and D. Ganesh, “Prediction of crop production using adaboost regression method,” J. Phys. Conf. Ser., vol. 1228, no. 1, 2019, doi: 10.1088/1742-6596/1228/1/012005.
[17] A. Lawi and F. Aziz, “Classification of credit card default clients using LSSVM ensemble,” Proc. 3rd Int. Conf. Informatics Comput. ICIC 2018, pp.1–4, 2018, doi: 10.1109/IAC.2018.8780427.
[18] R. Atallah and A. Al-Mousa, “Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method,” 2019 2nd Int. Conf. New Trends Comput. Sci. ICTCS 2019 - Proc., 2019, doi:
10.1109/ICTCS.2019.8923053.
[19] M. Saw, T. Saxena, S. Kaithwas, R. Yadav, and N. Lal, “Estimation of prediction for getting heart disease using logistic regression model of machine learning,” 2020 Int. Conf. Comput. Commun. Informatics, ICCCI 2020, pp. 20–25, 2020, doi: 10.1109/ICCCI48352.2020.9104210.
[20] H. Li, Z. Li, H. Hou, G. Sheng, X. Jiang, and J. Hu, “An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree,” 2018 Cond. Monit. Diagnosis, C. 2018 - Proc., pp. 1–5, 2018, doi: 10.1109/CMD.2018.8535665.
[21] T. T. H. LE, H. KANG, and A. H. KIM, “Household Appliance Classification Using LowerOdd-Numbered Harmonics and the BaggingDecision Tree,” IEEE Access, vol. VOLUME 8, pp. 55937-55952, 2020.
[22] W. Guohua, Y. Diping, Y. Jiyao, Z. Wenhua, D. Peng, and X. Yiqing,4545 “Research on Non-Intrusive Load Monitoring Based on Random Forest Algorithm,” 4th Int. Conf. Smart Grid Smart Cities, ICSGSC 2020, pp. 1–5, 2020, doi: 10.1109/ICSGSC50906.2020.9248565.
[23] J. Pan et al., “Field-weighted factorization machines for click-through rate prediction in display advertising,” Web Conf. 2018 - Proc. World Wide Web Conf. WWW 2018, pp. 1349–1357, 2018, doi: 10.1145/3178876.3186040.
[24] S. Zhang, Z. Liu, and W. Xiao, “A Hierarchical Extreme Learning Machine Algorithm for Advertisement Click-Through Rate Prediction,” IEEE Access, vol. 6, no. c, pp. 50641–50647, 2018, doi: 10.1109/ACCESS.2018.2868998.