Triwardana, Nogi (2022) Penerapan Algortima Long Short Term Memory untuk memprediksi harga cabai rawit. Sarjana thesis, Uin Sunan Gunung Djati Bandung.
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Abstract
Research for machine learning for now increasingly experiencing rapid development along with research conducted around the world. Including research to prediction the prices of Cayenne Pepper made by Sebastianus Reczy in 2020 with Long Short Term Memory Algorithm. With the resulting level of accuracy of 72.22% with value of k resulting highest value is k=13. Method is used to this research is Long Short Term Memory (LSTM) algorithm. The purpose of this research is to know how to applicate LSTM algorithm for predicate the prices of cayenne pepper and how to know of resulting evaluation from LSTM algorithm to predicate prices of cayenne pepper. On the measured evaluation results with a method of Root Mean Squared Error (RMSE) showing that model variant of Vanilla LSTM is resulting a training loss value of 0.03895063325762749 and testing loss value of 0.035548947751522064. Stack LSTM is resulting a training loss value of 0.04113268852233887 and testing loss value of 0.0364893302321434. Then for Bidirectional LSTM is resulting training loss value of 0.03670644015073776 and testing loss value of 0.03317515179514885. Between the third tested models, Bidirectional LSTM model is resulting the most optimal training loss value and testing loss among the other two LSTM models.
| Item Type: | Thesis (Sarjana) |
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| Uncontrolled Keywords: | Long Short Term Memory;Cabai Rawit; |
| Subjects: | Data Processing, Computer Science Data Processing, Computer Science > Computer Science Education Technology, Applied Sciences |
| Divisions: | Fakultas Sains dan Teknologi > Program Studi Teknik Informatika |
| Depositing User: | Nogi Ragil Triwardana |
| Date Deposited: | 10 May 2022 04:30 |
| Last Modified: | 10 May 2022 04:30 |
| URI: | https://digilib.uinsgd.ac.id/id/eprint/50820 |
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