PREDIKSI KUALITAS AIR KOLAM MENGGUNAKAN ALGORITMA MACHINE LEARNING BERDASARKAN DATA IOT

Antoni Nur Yahya; Martanto .; Denni Pratama; Umi Hayati; Saeful Anwar

Authors

Antoni Nur Yahya STMIK IKMI Cirebon, Indonesia
Martanto . STMIK IKMI Cirebon, Indonesia
Denni Pratama STMIK IKMI Cirebon, Indonesia
Umi Hayati STMIK IKMI Cirebon, Indonesia
Saeful Anwar STMIK IKMI Cirebon, Indonesia

Keywords:

Analisis Sentimen, Naïve Bayes, RupaRupa, Google Play Store, Text Mining

Abstract

Kualitas air kolam merupakan faktor krusial dalam keberhasilan akuakultur karena berpengaruh langsung terhadap kesehatan ikan, produktivitas, dan keberlanjutan ekosistem. Pemantauan kualitas air secara konvensional masih menghadapi keterbatasan dalam cakupan pengukuran, kontinuitas data, serta keterlambatan dalam pengambilan keputusan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem prediksi kualitas air kolam berbasis Internet of Things (IoT) dan algoritma machine learning guna mendukung deteksi dini degradasi kualitas air. Data kualitas air dikumpulkan secara kontinu menggunakan sensor IoT yang mengukur parameter fisika-kimia utama, meliputi suhu, pH, kekeruhan, dan oksigen terlarut. Data yang diperoleh kemudian melalui tahapan pra-pemrosesan, seperti pembersihan data, penanganan nilai hilang, dan rekayasa fitur, sebelum digunakan dalam pemodelan machine learning dengan pendekatan supervised learning. Beberapa algoritma machine learning diterapkan dan dievaluasi untuk memperoleh model dengan kinerja prediksi terbaik berdasarkan metrik akurasi yang relevan. Hasil penelitian menunjukkan bahwa penerapan machine learning pada data IoT mampu menghasilkan prediksi kualitas air yang akurat dan adaptif terhadap perubahan kondisi lingkungan kolam. Sistem yang dikembangkan berpotensi memberikan peringatan dini terhadap penurunan kualitas air, sehingga memungkinkan pengelola kolam melakukan tindakan korektif secara proaktif. Penelitian ini berkontribusi dalam pengembangan sistem pemantauan dan prediksi kualitas air kolam berbasis data yang efisien, adaptif, dan berkelanjutan, serta mendukung implementasi akuakultur cerdas di masa mendatang.

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