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All Journal International Journal of Advances in Applied Sciences Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Patria : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Komunikasi Journal of Computer and Information Systems Ampera Advance Sustainable Science, Engineering and Technology (ASSET)
Alauddin Maulana Hirzan
Universitas Semarang
Aerospace Engineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Computer Science and Information Technologies

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Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things Hirzan, Alauddin Maulana; Daru, April Firman; Huizen, Lenny Margaretta
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p136-146

Abstract

Water quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture.
Co-Authors Agnes Saputri Agus Hartanto April Firman Daru Bahaman, Nazrulazhar Febrian Wahyu Christanto Galih Setiarso Handayani, Sri Hanif, Mohammad Burhan Huizen, Lenny Margaretta Nurtriana Hidayati Rastri Prathivi Saputri, Agnes Setiarso, Galih Suprayogi, Muhammad Sani Surono Surono Surono Surono Susanto Susanto Titin Winarti Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit