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      "keywords": "pencemaran air, machine learning, klasifikasi, analisis performa,\r\nkualitas air, Water pollution, classification, performance\r\nanalysis.",
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      "date": "2026-02-19",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32043",
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      "abstract": "Penilaian pencemaran air merupakan tugas yang sangat penting dalam\r\npemantauan lingkungan karena dampaknya yang langsung terhadap kesehatan\r\nmanusia dan keberlanjutan ekosistem. Penelitian ini menganalisis kinerja metode\r\nklasifikasi berbasis machine learning dalam menentukan tingkat pencemaran air\r\nmenggunakan data kualitas air yang tersedia secara publik. Beberapa algoritma\r\nsupervised learning, antara lain Decision Tree, Random Forest, Support Vector\r\nMachine , K-Nearest Neighbor, dan Naïve Bayes, diterapkan untuk\r\nmengklasifikasikan tingkat pencemaran berdasarkan parameter fisikokimia air.\r\nDataset dievaluasi menggunakan skema pembagian data latih dan data uji dengan\r\nrasio 90:10, serta kinerja model dinilai menggunakan metrik accuracy, precision,\r\nRecall, F1-Score, dan ROC-AUC. Hasil eksperimen menunjukkan bahwa\r\npengklasifikasi berbasis ensemble menghasilkan kinerja klasifikasi yang lebih\r\nunggul dibandingkan pengklasifikasi tunggal, terutama dalam menangkap\r\nhubungan nonlinier antar indikator kualitas air. Temuan ini sejalan dengan\r\npenelitian-penelitian terkini yang menekankan efektivitas teknik machine learning\r\ndalam klasifikasi kualitas air dan penilaian pencemaran air."
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