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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics WIDYAKALA JOURNAL KURVATEK Indonesian Journal of Electrical Engineering and Computer Science JTEIN: Jurnal Teknik Elektro Indonesia JUDGE: Jurnal Hukum Jurnal INFOTEL JGGAG (Journal of Games, Game Art, and Gamification)

Wayan Suparta

Institut Teknologi Nasional Yogyakarta

Author-ID : 671094

Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 20 Documents Claim Missing Document

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Articles

Title

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Co-Authors Abda Abda Afan Galih Salman Agung Trisetyarso Alhasa, Kemal Maulana Anak Agung Ngurah Krisnanda Putra Aris Warsita Asnawi, M. Natsir Bachtiar Saleh Abbas Badawi, Achmad Budi Utama, Budi Dodick Zulaimi Sudirman Dulhadi, Dulhadi Edi Abdurahman Ford Lumban Gaol Ford Lumban Gaol Ircham Kemal Maulana Alhasa Maniah Maniah Melyani Melyani Putra, Iwahori Badia Yosafat Rohmat Taufiq Trie Handayani, Trie Wahyu Sasongko Putro Winanti Winanti Yaya Heryadi Yaya Heryadi

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