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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED Jurnal Sistem Informasi Kaputama (JSIK) Journal Of Millennial Community Sustainable: Jurnal Kajian Mutu Pendidikan Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Indo-MathEdu Intellectuals Journal Paedagogi: Jurnal Kajian Ilmu Pendidikan (e-journal) Lencana: Jurnal Inovasi Ilmu Pendidikan Deli Sains Informatika International Journal of Educational Practice and Policy Journal on Education and Teacher Professionalism Jurnal Pendidikan Non formal Cendikia Pendidikan ADM : Jurnal Abdi Dosen dan Mahasiswa
Melly Br Bangun
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Deli Sains Informatika

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COMPARATION BETWEEN FEED FORWARD NEURAL NETWORK (FFNN) AND SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) IN FORECASTING SEASONAL TIME SERIES DATA Dian Septiana; Melly Br Bangun Melly Br Bangun
Deli Sains Informatika Vol. 2 No. 2 (2023): Artikel Riset Juni 2023
Publisher : LPPM Universitas Deli Sumatera

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Abstract

Seasonal patterns in time series data are periodic and recurring patterns caused by certain factors such as weather, holidays, repetition of promotions, or changes in the economic climate. Good data forecasting is very important for making decisions in the business sector, such as retail prices, marketing, production and other business sectors. There are several approaches that can be taken to analyze time series data that has a seasonal or trending pattern. Among them is the classical approach which decomposes seasonal and non-seasonal factors, then forecasts with certain assumptions. Then there is also an approach using artificial intelligence, in this case a more flexible feed-forward neural network is used as a tool for forecasting time series data. In this study the data used is data with a regular seasonal pattern 12. For data with a pattern like this SARIMA (1,1,1)(0,1,1)12 with a MAPE of 1.775% gives better results than FFNN 12-10-1 which produces a MAPE value of 7.5226%.
Co-Authors Adelina Irmayani Lubis Agustin, Angelica Ahmad Fariqi Amenobelia Sitepu Ayuning, Vista Cindy Nadya Damayanti, Nina Afria Dian Septiana Dita, Disa Martia Egner Bernard Pribadi Elis Nina Herliyana Elizon Nainggolan Firdaus, Lucky Marcelino Fitria Cahaya Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Ginting, Rehia Angelica Handayani, Silvia Mariah Harahap, Nazwa Malika Hasibuan, Lailan Sahrina Hasibuan, Maria Angel Imelda Sari Ira Sefi Andini Khairani, Dewi Lestania Simatupang Lidya Natasya Lilik Prayitno Lumbangaol, Hermawan Mahfuzi Irwan Marisa Nabila Muchtar, Chairul Omar Muhammad Adib Ahsani Muhammad Takwin Machmud Muliyani Natalia Silalahi Natalia Silalahi, Natalia Ndona, Margaretha Djanius Nehe, Gian Devina Gracia Nina Afria Damayanti Noer Risky Ramadhani Nurhudayah Manjani Oriza Salsabila Padmawati, Fitri Pakpahan, Fanny Grisella Pricilia, Yolanda Purba, Atikah Aulia Purba, Della Priskila Purba, Jon Felix Ramadhani, Noer Risky Reny Furnawati Sitanggang Ridoansih, Thomas Rini Juliani Sipahutar Rista Triwani Rodhiatam Mardiah Damanik Roif Syahli, Naufal Romi Anggun Zefanya Rosdiana Rosdiana Rossikha, Faiza Rossy Nurhasanah Rumahombar, Cindy Natalie Sagala, Angel C Sagala, Annisa Azmi SANI SUSANTI Sani Susanti Santi Nurhasanah Batubara Siagian, Mouces Ferdinan Sianturi, Mian Simangunsong, Yesica Natalia Simanjuntak, Septo Sinaga, Mariana Sipahutar, Rini Juliani Siti Maisaroh Sitorus, Julia Sintya Sondang Dioranta Suci, Delli Syahputra, Dika Dona Syahrial Syahrial SYAHRIAL SYAHRIAL Tessalonika, Jelita Vivaldy, Jeremy Akbar Winda Tarihoran Wulandari, Yunita Dwi Zainuddin Muchtar