Wibisana Sudarto, . (2024) PREDIKSI RASIO KLIK TAYANG PADA LAYANAN IKLAN UNTUK PROMOSI PEMASARAN DENGAN MENGGUNAKAN METODE HYBRID LSTM-GRU. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The success of a company in running its business cannot be separated from the successful performance of the marketing team. In a company, the marketing team is the spearhead for the company's profitability and is the mainstay team for delivering products to the public. As time goes by, the marketing methods used have become sophisticated with internet-based marketing techniques or what is usually called digital marketing. One of the media for carrying out marketing promotions is through advertising, in its application one metric is used, namely the click-through rate. The click-through rate is not a metric that is a guarantee for users who see the ad and buy it, but rather to determine the interaction of user interest in the product displayed through the ad. Prediction of click-through rate on advertising services is carried out using a hybrid Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model. The data used is campaign data from company XYZ with a marketing promotion time span of one month. After carrying out the pre-processing stages, the data will be used to test the hybrid LSTM-GRU model which was formed using hidden layer parameters, units, SGD (Stochastic Gradient Descent) optimizer, and variations in epoch and batch size. By producing the best accuracy performance using a Root Mean Squared Error (RMSE) calculation of 0.062.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010511041] [Pembimbing 1: Neny Rosmawarni] [Pembimbing 2: Muhammad Panji Muslim] [Penguji 1: Musthofa Galih Pradana] [Penguji 2: I Wayan Widi Pradnyana] |
| Uncontrolled Keywords: | Click Through Rate, Long Short Term Memory, Gated Recurrent Unit |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Wibisana Sudarto |
| Date Deposited: | 19 Feb 2024 03:15 |
| Last Modified: | 19 Feb 2024 03:15 |
| URI: | http://repository.upnvj.ac.id/id/eprint/28341 |
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