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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK The Journal for Technology and Science Proceedings of KNASTIK JUTI: Jurnal Ilmiah Teknologi Informasi PROCEEDING IC-ITECHS 2014 JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Conference SENATIK STT Adisutjipto Yogyakarta SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati
Ahmad Saikhu
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Conference SENATIK STT Adisutjipto Yogyakarta

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Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data Hariadi, Victor; Saikhu, Ahmad; Zakiya, Nurotuz; Wijaya, Arya Yudhi; Baskoro, Fajar
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN XXX-XXX-XXXXX-
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.365

Abstract

Rainfall is related to a number of factors that are interdependent and influenced by dynamic global time, region and climate factors. Determination of relevant predictors is important for the efficiency of the rainfall estimator model. Although some climate modeling studies in one region/country have high accuracy, this model is not necessarily suitable for other regions. Determination of predictor variables by considering spatio-temporal factors and local / global features results in a very large number of inputs. Feature selection produces minimal input so that it gets relevant predictor variables and minimizes variable redundancy. Recurrent Neural Networks is one of the artificial neural networks that can be used to predict time series data. This study aims to predict rainfall by combining the SVM classification method and the RNN method. Tests on the Perak 1 daily and monthly weather data (WMO ID: 96933) and Perak 2 Station daily and monthly data(WMO ID: 96937), showed high accuracy results with an R2 are 92.1%; 94.1%; 90.9% and 89.6%.
Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data Victor Hariadi; Ahmad Saikhu; Nurotuz Zakiya; Arya Yudhi Wijaya; Fajar Baskoro
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.365

Abstract

Rainfall is related to a number of factors that are interdependent and influenced by dynamic global time, region and climate factors. Determination of relevant predictors is important for the efficiency of the rainfall estimator model. Although some climate modeling studies in one region/country have high accuracy, this model is not necessarily suitable for other regions. Determination of predictor variables by considering spatio-temporal factors and local / global features results in a very large number of inputs. Feature selection produces minimal input so that it gets relevant predictor variables and minimizes variable redundancy. Recurrent Neural Networks is one of the artificial neural networks that can be used to predict time series data. This study aims to predict rainfall by combining the SVM classification method and the RNN method. Tests on the Perak 1 daily and monthly weather data (WMO ID: 96933) and Perak 2 Station daily and monthly data(WMO ID: 96937), showed high accuracy results with an R2 are 92.1%; 94.1%; 90.9% and 89.6%.
Co-Authors Adi Setyo Nugroho Agung Teguh Setyadi Akbar, Afrizal Laksita Anak Agung Istri Sri Wiadnyani Anggraini, Ratih Nur Esti Arya Yudhi Wijaya Arya Yudhi Wijaya Arya Yudi Wijaya Arya Yudi Wijaya Baskoro, Fajar Bilqis Amaliah Bryan Alfadhori Buliali, Joko Lianto Chastine Fatichah Daniel Oranova Siahaan Dedy D. Prastyo Deneng Eka Putra Djoko Kuswanto F.X. Arunanto Fajar Baskoro Fajar Baskoro FX Arunanto Ginardi, R.V. Hari Gracius Cagar Gunawan Hari Ginardi Imam Hambali Ivanda Zevi Amalia Joko Lianto Joko Lianto Laili Rochmah Muhammad Muharrom Al Haromainy Muhammaf Sjahid Akbar Neni Alya Firdausanti Noor Fitria Azzahra Noor Fitria Azzahra NUR LAILA Nurotuz Zakiya Qolby, Lailly Syifa'ul Rakhmat Arianto Rully Soelaiman Rully Soelaiman Santi Puteri Rahayu Sarwosri Sarwosri Setiawan Setiawan Suwadi Suwadi Victor Hariadi Yudhi Purwananto Zakiya, Nurotuz