Prediksi Pasang Surut Air Laut Pelabuhan Tanjung Emas Semarang dengan Metode Single Exponential Smoothing dan Least Squares

AL HARIS, Miftakhul Farid and Ruswanti, Diyah and Khusnuliawati, Hardika (2020) Prediksi Pasang Surut Air Laut Pelabuhan Tanjung Emas Semarang dengan Metode Single Exponential Smoothing dan Least Squares. Other thesis, Universitas Sahid Surakarta.

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Abstract

Indonesia as an archipelago that has very wide waters, produces a tidal phenomenon, the point of severity occurs on the north coast of Central Java, especially in the Semarang region. The absence of tidal predictions for the following day resulted in the hampering of data issued to relevant agencies, which resulted in sailing permits being not issued, and data validation being inaccurate. The aim of sea tide research for the next day using the single exponential smoothing and least squares method, is expected to reduce the impact of risks that occur, reduce ship accident at sea and increase the vigilance of fishermen in sailing. This study uses variables X and Y where X is a fixed value while Y is a random value. The point is that the value of the variable X will predict the variable Y so that there is the possibility of several variables Y. One of the uses of linear data is to make predictions based on data that has been previously owned. The process of making sea tide predictions begins with data collection, then the object-based system design is done using UML, followed by the creation of a database, the design of the system interface for pre-researchers. Then implemented in the main programming language. Obtained the test results, namely the prediction of single exponential smoothing produces the smallest MAE value of 5.9. while the least squares method produces the smallest MAE (mean absolute error) value of 10.2 and from the TNI AL prediction data produces the smallest MAE value of 47.8. It can be compared that the single exponential MAE testing method produces a smaller error value compared to the smoothing least squares and TNI AL predictions.

Item Type: Thesis (Other)
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Sains, Teknologi dan Kesehatan > Informatika
Depositing User: Dwi Ratna Sari
Date Deposited: 15 Dec 2020 07:28
Last Modified: 01 Aug 2022 04:22
URI: https:///id/eprint/167

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