Penerapan Algoritma LightGBM untuk Penentuan Kondisi Air Akuarium Berdasarkan Parameter Kualitas Air: Implementation of LightGBM Algorithm for Determining Aquarium Water Conditions Based on Water Quality Parameters

Mutiara Qolbu; Ahmad Taqwa; Mohammad Fadhli

doi:10.57152/malcom.v6i4.2933

Authors

Mutiara Qolbu Politeknik Negeri Sriwijaya
Ahmad Taqwa Politeknik Negeri Sriwijaya
Mohammad Fadhli Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.57152/malcom.v6i4.2933

Keywords:

Amonia, Hyperparameter, Klasifikasi, Kualitas Air, LightGBM, Machine Learning, pH

Abstract

Kualitas air sangat penting bagi kesehatan dan kelangsungan hidup ikan di akuarium. Jika parameter seperti pH, suhu, total dissolved solids (TDS), dan amonia berubah, kondisi air dapat menjadi tidak cocok bagi ikan. Masalahnya, metode pemantauan biasa masih terbatas dalam menganalisis hubungan antara parameter-parameter tersebut secara simultan. Penelitian ini bertujuan untuk menggunakan algoritma LightGBM untuk mengklasifikasikan kualitas air akuarium berdasarkan pH, suhu, TDS, dan amonia. Dataset yang digunakan terdiri dari 710 data pengukuran sensor kualitas air. Data-data ini melalui beberapa tahap, yaitu preprocessing, pelabelan data, pembagian data, penyesuaian hyperparameter, dan pelatihan model. Hasil pengujian menunjukkan bahwa konfigurasi LightGBM terbaik memiliki accuracy 99,30%, precision 98,61%, recall 99,28%, dan F1-score 98,92%. Evaluasi menggunakan confusion matrix menunjukkan bahwa dari 142 data pengujian hanya terdapat satu kesalahan klasifikasi, dengan seluruh data kategori baik dan buruk berhasil diprediksi dengan tepat. Hasil tersebut menunjukkan bahwa LightGBM dapat mengklasifikasikan kualitas air akuarium dengan akurasi yang tinggi.

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