Prediksi Kualitas Air Sungai Menggunakan Metode Pembelajaran Mesin: Studi Kasus Sungai Ciliwung Prediction of River Water Quality Using Machine Learning Methods: Ciliwung River Case Study

Abstract

The Ciliwung River is the largest river in Jakarta area, and its water quality tends to decline. To determine river water quality from time to time, real-monitoring is carried out in real time using Online Monitoring (ONLIMO) technology by installing a multiprobes sensor in the Ciliwung River. Eight parameters were monitored, including pH, Dissolved Oxygen, Nitrate, Turbidity, Total Dissolved Solids, Salinity, Electrical Conductivity, and Temperature. In this study, a data science approach using four Machine Learning models, namely Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest, and Naive Bayes, was used to process monitoring data for one year, from January 1 until December 31, 2018, to predict the Ciliwung River water quality. The AAN results showed that the 5-Hidden Layers model has the highest value of Confusion Matrix (Accuracy, Recall, Precision, Specificity, and F1 Score) compared with all tested Hidden Layer of architecture models. In addition, the Random Forest method has the highest confusion matrix value, followed by the ANN method with 5 Hidden Layers, the Naive Bayes method, and the SVM method. The accuracy value of the first three methods is very high, above 89%. Thus, the first three methods work well to predict the quality of Ciliwung River water.

ABSTRAK

Sungai Ciliwung merupakan sungai terbesar di wilayah Jakarta, kualitas airnya cenderung menurun. Untuk mengetahui kualitas air sungai dari waktu ke waktu dilakukan pemantauan secara real time menggunakan teknologi Online Monitoring (ONLIMO) dengan memasang multiprobes sensor di Sungai Ciliwung. Delapan parameter yang dipantau meliputi pH, Oksigen Terlarut, Nitrat, Kekeruhan, Total Padatan Terlarut, Salinitas, Konduktivitas Listrik, dan Suhu. Pada penelitian ini, pendekatan sains data menggunakan empat model Machine Learning yaitu Jaringan Syaraf Tiruan (JST), Support Vector Machine (SVM), Random Forest, dan Naive Bayes digunakan untuk mengolah data pemantauan selama satu tahun, dari 1 Januari hingga 31 Desember 2018 untuk memprediksi kualitas air sungai Ciliwung. Hasil metode JST menunjukkan bahwa model dengan 5 Hidden Layer memiliki nilai Confusion Matrix (Accuracy, Recall, Precision, Specificity, dan F1 Score) tertinggi dibandingkan dengan semua model arsitektur Hidden Layer yang diuji. Selain itu, metode Random Forest memiliki nilai Confusion Matrix tertinggi, diikuti metode JST dengan 5 Hidden Layer, metode Naive Bayes, dan terakhir metode SVM. Tingkat akurasi ketiga metode pertama sangat tinggi, yaitu di atas 89%. Dengan demikian, ketiga metode pertama bekerja dengan baik untuk memprediksi kualitas air Sungai Ciliwung.