Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Ramadhan, Annas Wahyu; Adytia, Didit; Saepudin, Deni; Husrin, Semeidi; Adiwijaya, Adiwijaya

doi:10.24843/lkjiti.2021.v12.i03.p01

Cited by 6 publications

(5 citation statements)

References 20 publications

(32 reference statements)

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“…To determine the model with the best forecasting accuracy, we utilize the coefficient of determination (𝑅 2 ) and Root Mean Squared Error (RMSE) as performance metrics. The coefficient of determination (𝑅 2 ), a commonly used quantitative metric for evaluating a model's predictive capability, ranges from 0 to 1 [10]. The formula for 𝑅 2 is as follows (2-4):…”

Section: Peformance Metricsmentioning

confidence: 99%

“…In Equation ( 2)-( 4), RSS is Residual Sum of Squares, while TSS represents Total Sum of Squares. Here, 𝑦 𝑖 denotes the individual observed value, 𝑁 represents the total number of data points, and 𝑦 ̂𝑖 represents the predicted value [10].…”

Section: Peformance Metricsmentioning

confidence: 99%

“…It involves decomposing water level data into harmonic components using the Least Squares Estimation (LSE) based on observed data. However, THA is limited to predicting only tidal components and relies on extensive historical data [10].…”

Section: Introductionmentioning

confidence: 99%

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Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Saepudin,

Egi Shidqi Rabbani,

Dio Navialdy

et al. 2024

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Climate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), tidal flooding occurs annually in Surabaya as a result of rising water levels, highlighting the urgent need for water level forecasting models to mitigate these impacts. In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. We divided the training data into three scenarios: 3, 6, and 8 months to train TCN models for 14-day forecasts. The 8-month training scenario yielded the best results. Subsequently, we used the 8-month training data to forecast 1, 3, 7, and 14 days using TCN, Transformers, and the Recurrent Neural Network (RNN) models. TCN consistently outperformed other models, particularly excelling in 1-day forecasting with coefficient of determination () and RMSE values of 0.9950 and 0.0487, respectively.

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Section: Peformance Metricsmentioning

confidence: 99%

Section: Peformance Metricsmentioning

confidence: 99%

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Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Saepudin,

Egi Shidqi Rabbani,

Dio Navialdy

et al. 2024

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“…LSTM architecture tries to overcome the weaknesses in RNN in terms of vanishing gradient [41]. LSTM is usually used in text processing and time series data [42] for predicting sea level. LSTM uses different gates in its architecture, consisting of an input gate, a forget gate and an output gate.…”

Section: Implementation Of the Deep Learning Algorithmmentioning

confidence: 99%

Spam Comments Detection on Instagram Using Machine Learning and Deep Learning Methods

Chrismanto

Afiahayati

Sari

et al. 2022

LKJITI

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The more popular a public figure on Instagram (IG), the number of followers also increase. When a public figure posts something, there are many comments from other users. In fact, from all the comments, not all of them are relevant to the post, such as advertising, links, or clickbait comments. The type of comments that are irrelevant to the post is usually called spam comments. Spam comments will interfere with information flow and may lead to misleading information. This research compares machine learning (ML) and deep learning (DL) classification methods based on our collected Indonesian IG spam comment dataset. This research was conducted in the following steps: dataset preparation, pre-processing, simple normalization, features generation using TF-IDF and word embedding, application of ML and DL classification methods, performance evaluation, and comparison. The authors compare accuracy, F-1, precision, and recall from ML and DL results. This research shows that ML and DL methods do not significantly differ. The Linear SVM, Extreme Tree (ET), Regression, and Stochastics Gradient Descent algorithms can reach the accuracy of 0.93. At the same time, the DL method has the highest accuracy of 0.94 using the SimpleTransformer BERT architecture. The difference between ML and DL methods is not significantly different.

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“…We used two measurements to evaluate model performance i.e., Root Mean Square Error (RMSE) and Correlation Coefficient (CC). The formula for calculating the RMSE value is explained in equation ( 5) [28].…”

Section: Performance Measurementmentioning

confidence: 99%

Boosting Methods For Dengue Incidence Rate Prediction in Bandung District

Nhita

Adytia²,

Rohmawati³

2022

LKJITI

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Dengue infections are among the top 10 diseases that cause the most deaths worldwide. Dengue is a severe global threat and problem, especially in tropical countries like Indonesia. The Indonesian Ministry of Health also stated that dengue is as dangerous as COVID-19. One of the preventive actions that can be taken is by controlling vectors (the Aedes aegypti mosquito) where weather factors influence their breeding. In this study, the prediction of dengue incidence rate is carried out using three boosting methods i.e., Extreme Gradient Boosting, Adaptive Boosting, and Gradient Boosting. The data used are monthly data of dengue incidence rate and weather data. The case study used is Bandung district, West Java Province, Indonesia. The important issues that is investigated in this study is to find the weather parameters that have the most influence on IR and gradually improve the prediction model through three test scenarios. From the test results, the weather parameter that has the most influence on the next month's IR is temperature. Meanwhile, the best training data length is five years (2016-2020). Finally, the best prediction model achieved by AdaBoost method with value of Root Mean Square Error and Correlation Coefficient for testing data (January-December 2021) are 0.55 and 0.95, respectively.

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Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Cited by 6 publications

References 20 publications

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Spam Comments Detection on Instagram Using Machine Learning and Deep Learning Methods

Boosting Methods For Dengue Incidence Rate Prediction in Bandung District

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