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Journal : Journal of Robotics and Control (JRC)

Development of Euclidean Distance Algorithm for ANFIS Optimization in IoT-based Pond Water Quality Prediction Dahria, Muhammad; Defit, Sarjon; Yuhandri, Yuhandri
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26497

Abstract

Pond water quality is a pivotal factor that influences the productivity and health of biota in aquaculture systems. The monitoring and prediction of water quality parameters, including temperature, pH, and dissolved oxygen (DO) levels, are imperative for maintaining optimal environmental conditions. The objective of this research is to develop the Euclidean Distance algorithm as an optimization method in adaptive neuro-fuzzy inference system (ANFIS) modeling to enhance the accuracy of internet of things (IoT)-based pond water quality prediction. Water quality parameter data is collected in real-time using IoT sensors connected to an ESP32 microcontroller and transmitted to a cloud storage platform for analysis. Subsequently, the data undergoes a series of processing steps, including min-max normalization and feature selection based on Euclidean distance. This process aims to generate a more representative and relevant subset of data for the subsequent model training process. The ANFIS model was trained using the optimized data and evaluated using MSE, MAD, MRSE and MAPE metrics. The training process involving four data sharing scenarios demonstrated a reduction in error when compared to the model that lacked optimization, specifically: The following proportions were determined: 50% versus 50% (0.11824 versus 0.15536), 70% versus 30% (0.18666 versus 0.19454), 80% versus 20% (0.17843 versus 0.18833), and 90% versus 10% (0.22477 versus 0.22859). The findings indicate that the incorporation of the Weighted Euclidean Distance algorithm within the IoT-based prediction system can markedly enhance the efficiency and precision of the ANFIS model.
Co-Authors Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Aldi, Febri Andrean, Fajri Ilhami Anita Sindar Ardiyan, Destio Arif Budiman Aulia, Allans Prima Budayawan, Khairi Chandra, Mrs Montesna Dahria, Muhammad Devita, Retno Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Efori Buulolo Eka Praja Wiyata Mandala Esa Kurniawan Fauzan, Yuniko Febri Hadi Feri Irawan Finny Fitry Yani Firzada, Fahmi Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartomi, Zupri Henra Hendrick, H Idun Ariastuti Iftitah, Hasanatul Iskandar Fitri, Iskandar Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak Liga Mayola M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Malik, Rio Andika Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nuning Kurniasih Nurdiyanto, Heri Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putri, Stefani Rahayu, Rita Rahmad Dian Rakhmad Kuswandhie Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Sarjon Defit Sarjon Defit Septiana, Vina Tri Setiawan, Adil Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sovia, Rini Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Syafrika Deni Rizki, Syafrika Deni Syaiffullah, Afif Tajuddin, Muhammad Takyudin, Takyudin Tessa Y M Sihite Tukino, Tukino Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yanto, Musli Yendi Putra Yeni, Nasma Yenila, Firna Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif