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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Moneter : Jurnal Akuntansi dan Keuangan Paradigma Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Mantik Penusa Jurnal Riset Teknologi dan Inovasi Pendidikan (Jartika) VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Jurnal Riset Informatika Building of Informatics, Technology and Science BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of System and Computer Engineering Jurnal Sains Indonesia Akasia: Artikel Ilmiah Sistem Informasi Akuntansi JUSTIAN - Jurnal Sistem Informasi Akuntansi Indonesian Journal of Networking and Security - IJNS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Ilmu Komputer untuk Masyarakat Jurnal Ilmiah AgriSains PENA ABDIMAS : Jurnal Pengabdian Masyarakat Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Journal of Accounting Information System
Nurfia Oktaviani Syamsiah
Universitas Bina Sarana Informatika
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Journal : Jurnal Riset Informatika

 1 
SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING Nurfia Oktaviani Syamsiah; Indah Purwandani
Jurnal Riset Informatika Vol. 3 No. 1 (2020): December 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i1.44

Abstract

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.
Co-Authors Abdul Gani Agustiana, Irma Anna Anna Apriani Mustika Burhanudin Burhanudin Chandra, Hari Dedi Saputra Dominikus Riki Irawan Duta Setiawan Eni Heni Hermaliani Eva Meilinda Fadillah, Sunia Maharani Fara Dina Fara Dina - AMIK BSI Pontianak Febri Wulandari firmansyah, Yoki Fitriani, Nadiya Hamdatun Nupus Imam Muzakki Imroatus Sholikhah Indah Purwandani Ismawan Ismawan Jubaidah Jubaidah Kela Tulvina Lestari, Melda Zulika Lisnawanty Mahmud Sairan Maulidia Maulidia Muhammad Sony Maulana Nanda Diaz Arizona Nila Hardi Nurwahyuni Panny Agustia Rahayuningsih Purwandani, Indah Putri Amalia Raja Sabaruddin Reza Maulana Ria Ulandari Riski Annisa Rokhimin - AMIK BSI Bekasi Romi Satria Wahono Rosmiati, Mia Sarwani Sarwani Siti Nurwahyuni Sri Murni Sumanto, Sumanto Tia Rumanah Tri Haryati Umi Faddillah Wahyu Nugraha Wanty Eka Jayanti Weishky Steven Dharmawan Windi Irmayani Yeni Mustika Yundari, Yundari