Sekar Hanun Faizah, . (2025) PREDIKSI DINAMIKA SUHU TIME SERIES INLET DAN OUTLET SISTEM PENDINGIN REAKTOR NUKLIR FISI MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
To prevent high-risk overheating, it is crucial to control the temperature of the fission nuclear reactor cooling system. The purpose of this research is to study how the RNN algorithm works and predicts temperature dynamics. The RNN model was chosen for its ability to handle historical data dependencies and time series. Model training and testing used TH-IN, TH-OUT and t(s) temperature data. The results show that the RNN model can provide a forecast with a low error rate. With RMSE 0.0000052 dan MSE 0.0022842, where window size 3 has the best performance. The accuracy of the model is strongly influenced by the selection of the ideal window size. It is expected that this model will contribute to the development of an efficient and customizable temperature prediction system to improve the safety and operational efficiency of fission nuclear reactors.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511134] [Pembimbing 1: Didit Widiyanto] [Pembimbing 2: Novi Trisman Hadi] [Penguji 1: Ridwan Raafi'udin] [Penguji 2: I Wayan Rangga Pinastawa] |
| Uncontrolled Keywords: | Temperature Prediction, Fission Nuclear Reactor, Recurrent Neural Network (RNN), Time Series |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | SEKAR HANUN FAIZAH |
| Date Deposited: | 06 Aug 2025 07:06 |
| Last Modified: | 06 Aug 2025 07:06 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37787 |
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