Klasifikasi Kualitas Air dengan Metode Indeks Pencemaran Menggunakan Machine Learning - Dalam bentuk pengganti sidang - Artikel Jurnal

FIRNA FIRDIANI

Informasi Dasar

24.04.1171
006.31
Karya Ilmiah - Skripsi (S1) - Reference

Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed thatWaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality.

Keywords: Internet of Things; machine learning; water quality prediction; Random Forest; random oversampling

Subjek

Machine Learning
 

Katalog

Klasifikasi Kualitas Air dengan Metode Indeks Pencemaran Menggunakan Machine Learning - Dalam bentuk pengganti sidang - Artikel Jurnal
 
 
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

FIRNA FIRDIANI
Perorangan
Satria Mandala
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2024

Koleksi

Kompetensi

  • CII4E4 - TUGAS AKHIR

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