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All Journal Foristek Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Lembaga Pengabdian Kepada Masyarakat Undana ABDIMAS KAUNIAH JBIMA: Jurnal Pengabdian kepada Masyarakat International Journal of Electrical Engineering and Intelligent Computing
Andi Muhammad Ilyas
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Khairun
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health Social Sciences Other

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PREDIKSI KUALITAS AIR DANAU LAGUNA DI TERNATE MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Ilyas, And Muhammad
Foristek Vol. 15 No. 2 (2025): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v15i2.821

Abstract

Predicting the water quality of Laguna Lake in Ternate City is crucial to support ecosystem management and the needs of surrounding communities. This study aims to predict the parameters pH, temperature, nitrate, and phosphate for the year 2026 using four methods: Multiple Linear Regression (MLR), Random Forest Regression (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN). The data used consisted of measurements from 2017, 2023, and 2024. The prediction results show differences in performance among the methods. For pH, the 2026 predictions range from 6.54 to 8.15, with RF providing the highest value and ANN the lowest. Temperature predictions range from 31.03 to 31.86 °C, nitrate from 0.0888 to 0.1140 mg/L, and phosphate from 0.0433 to 0.0752 mg/L. ANN demonstrates the best accuracy for pH, while RF performs better for temperature. These differences indicate the presence of non-linear relationships among water quality parameters that are more complex than simple linear models. The 2026 water quality predictions offer an early assessment of Laguna Lake’s condition, providing a basis for informed management planning, conservation, and water quality decision-making. The results also highlight the effectiveness of artificial intelligence methods, particularly ANN, in modeling and predicting water quality conditions in tropical aquatic ecosystems.
Co-Authors Achmad Prajudin Sardju Darmawan Darmawaty, Darmawaty Darmiyati Muksin Eueung Mulyana Fahrizal Djohar Fahrizal Djohar Kaidati, Bahar M. Natsir Rahman Martini Djamhur, Martini Md Hasanuzzaman Mochammad Apriyadi Hadi Sirad Muhammad Natsir Rahman Muhammad Natsir Rahman Mutmainnah Mutmainnah Putri, Saptariani Rahman, Muhammad Natsir Ridwan, Mutmainnah Suciana Suciana Suparman Syamsuddin, Faris Yuliana Yuliana Yuliana Yuliana Zulaeha Mabud