Forecasts Marine Weather on Java Sea Using Hybrid Methods: TS-ANFIS

Adyanti, Deasy Alfiah; Asyhar, Ahmad Hanif; Novitasari, Ian Candra Rini; Lubab, Ahmad; Hafiyusholeh, Moh.

doi:10.11591/eecsi.v4.1114

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…Comparison of training data and test data is 80%:20%. The second process is training or forecasting the measurement formulation model using Equation (5). The third process is model testing to determine the level of accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, to maximize marine resources, it is necessary to refer to information on oceanographic parameters. Oceanographic parameters include waves [2], tides [3], currents [4], and others that are influenced by weather in the sea area [5]. Tidal information itself is considered crucial because, generally, tides can affect the speed of ocean currents [6].…”

Section: Introductionmentioning

confidence: 99%

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Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Utami

Novitasari

Setiawan³

et al. 2021

SJI

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Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions.Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error.Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Utami

Novitasari

Setiawan³

et al. 2021

SJI

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“…Algoritma ANFIS seperti penurunan gradien dan perambatan mundur yang digunakan untuk melatih data berdasarkan artificial neural network (Adyanti et al, 2017). Tahap pertama yang digunakan pada ANFIS adalah "if then" rules dari fuzzy inference system (Novitasari et al, 2020).…”

Section: Pendahuluanunclassified

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

Khariri

Nurfadila

Novitasari

2022

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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Cedera panggul dan acetabular merupakan cedera yang jarang terjadi, terhitung sekitar 3% hingga 8% dari semua cedera. Meskipun angka kematian cedera panggul hanya terbatas pada 1-2%, apabila disertai dengan perdarahan intra-abdominal atau pada intracranial menimbulkan kematian tertinggi yaitu 50%. Kematian akibat cedera panggul terbilang tinggi ketika penanganan awal dan akurat tidak diperhatikan. Pada penelitian ini dilakukan bertujuan mendeteksi tingkat keparahan penderita cedera panggul menggunakan adaptive neuro-fuzzy inference system (ANFIS). Bertujuan membantu medis dalam memberikan penanganan sesuai dengan tingkat keparahan cedera panggul. Penelitian dengan metode ANFIS untuk mendeteksi keparahan cedera panggul mendapatkan nilai error dengan confusion matrix yang bernilai kecil yaitu 0.3362, sehingga menghasilkan akurasi sebesar 98,1%, presisi sebesar 97.7%, serta nilai dari sensitifitas dan F-skor sebesar 100% dan pada penelitian lain yang menggunakan ANN (Artificial Neural Network) menghasilkan keakuratan deteksi sebesar 87% dan area under the curve (AUC) sebesar 0.94.

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“…The buoys also help to forecast natural phenomena such as earthquakes and storms by using sensors to gather different parameters such as air temperature, air density, relative humidity, wind speed, rainfall, solar radiation, water temperature and salinity profile [1].…”

Section: *Author For Correspondencementioning

confidence: 99%

Utilization of huffman coding for weather buoy system using long-range communication

Velasco¹,

Credo²,

Salazar³

et al. 2021

IJATEE

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Weather defines the short-term condition of the atmosphere in a certain region, while climate is the statistical weather condition at a given place during a specified period of time. Climate change describes the change of weather patterns over time within the range of decades to millions of years. According to studies, earth is experiencing climate change due to human activities. This past decade, predicting the weather condition has become a tough problem that humans encounter everywhere around the world. This sometimes results in an inaccurate weather prediction. But in order to predict and lessen the possibilities that risk and harm people's lives, developing marine weather information like buoys is done. Deploying weather buoys in the ocean helps predict weather conditions in a specific location in real-time. It is used to monitor the conditions of the ocean's surface and deep below it. *Author for correspondenceThe buoys also help to forecast natural phenomena such as earthquakes and storms by using sensors to gather different parameters such as air temperature, air density, relative humidity, wind speed, rainfall, solar radiation, water temperature and salinity profile [1].In predicting weather conditions, deploying weather buoy can now be deployed together with Wireless Sensor Networks (WSNs). WSNs are used in different areas of technology involving environment monitoring, medicine fields, logistics, etc. New Internet of Things (IoT) innovations, such as Long Range (LoRa), is evolving, allowing for very long wavelengths of power-efficient wireless connectivityLoRa is a technology is rapidly turning up and is used because of its technical advantages such as the lifetime of battery, long range, security and more [2]. LoRa has a good resistance to interference and provides

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Forecasts Marine Weather on Java Sea Using Hybrid Methods: TS-ANFIS

Cited by 5 publications

References 3 publications

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

Utilization of huffman coding for weather buoy system using long-range communication

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