Analisis Metode Random Forest untuk Memprediksi Kualitas Air Berbasis Data Lingkungan
DOI:
https://doi.org/10.26905/jisad.v4i2.17068Keywords:
Water Quality;, Machine Learning;, Random Forest;, Decision Tree;, Iterative Imputer;Abstract
Water quality is an important factor that affects human health and the environment. In Indonesia, access to consumable water is still limited, so a fast and accurate water quality classification method is needed. This study aims to develop a classification model based on Machine Learning using the Random Forest algorithm to predict the feasibility of drinking water. The dataset used is Water Quality from Kaggle with 3,276 data and 9 parameters. The preprocessing stage includes data cleaning, handling missing values using IterativeImputers, data sharing, and normalization using StandardScaler. The three algorithms were compared, namely Random Forest, Decision Tree, and Naïve Bayes. The results showed that Random Forest had the highest accuracy of 69%, higher than Decision Tree (64%) and Naïve Bayes (63%). However, the model had a performance imbalance with high Recall in the non-drinkable class (89%) and low in the drinkable class (38%). The feature importance analysis shows that Sulfate, pH, and Hardness are the most influential parameters. This study contributes through the use of a more optimal imputation method in improving classification performance.
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[1] V. B. Silahooy et al., Alam dan Perkembangannya. TOHAR MEDIA, 2024.
[2] A. R. Alfian, Krisis Air di Indonesia: Fenomena, Dampak & Solusi. Suluah Kato Khatulistiwa, 2023.
[3] A. P. Ekowati and M. F. D. Lusno, "Analisis Capaian dan Tantangan Akses Air Minum Aman di Indonesia Menuju SDGS 6.1.1," J. Penelit. Inov., vol. 5, no. 2, pp. 1707–1714, Apr. 2025, doi: 10.54082/jupin.1538.
[4] I. Dewata and Y. H. Danhas, Pencemaran Lingkungan. PT. RajaGrafindo Persada - Rajawali Pers, 2023.
[5] C. Ihsan, Dasar-Dasar Machine Learning: Teori, Algoritma, dan Implementasi. Greenbook Publisher, 2025.
[6] D. K. Phasinam, D. A. K. Singh, M. T. Singh, and M. K. Sharma, Fundamental of Machine Learning. Blue Rose Publishers, 2022.
[7] T. V. Geetha and S. Sendhilkumar, Machine Learning: Concepts, Techniques and Applications. CRC Press, 2023.
[8] A. B. Adetunji, O. N. Akande, F. A. Ajala, O. Oyewo, Y. F. Akande, and G. Oluwadara, "House Price Prediction using Random Forest Machine Learning Technique," Procedia Comput. Sci., vol. 199, pp. 806–813, Jan. 2022, doi: 10.1016/j.procs.2022.01.100.
[9] D. Safitri, "Prediksi Tingkat Stres Mahasiswa Menggunakan Algoritma Decision Tree," J. Inf. Syst. Appl. Dev., vol. 4, no. 1, pp. 188–194, Mar. 2026, doi: 10.26905/jisad.v4i1.16616.
[10] L. A. Supriyono et al., Buku Ajar Big Data dan Data Mining: Konsep, Metodologi, dan Aplikasi. PT. Sonpedia Publishing Indonesia, 2025.
[11] H. A. Salman, A. Kalakech, and A. Steiti, "Random Forest Algorithm Overview," Babylon. J. Mach. Learn., vol. 2024, pp. 69–79, Jun. 2024, doi: 10.58496/BJML/2024/007.
[12] M. W. Nugroho, "Analisis Performa Algoritma Random Forest dalam Mengatasi Overfitting pada Model Prediksi," J. Teknol. Inf. Dan Komun., vol. 9, no. 4, pp. 1562–1571, Dec. 2025, doi: 10.35870/jtik.v9i4.4236.
[13] O. Castro, P. Bruneau, J.-S. Sottet, and D. Torregrossa, "Landscape of High-Performance Python to Develop Data Science and Machine Learning Applications," ACM Comput. Surv., vol. 56, no. 3, pp. 65:1–65:30, Oct. 2023, doi: 10.1145/3617588.
[14] L. F. M. Horhoruw, A. Pratama, Y. Asri, and N. P., Eksplorasi Machine Learning dengan Scikit-Learn: Strategi Belajar Machine Learning. Uwais Inspirasi Indonesia, 2024.
[15] K. Maharana, S. Mondal, and B. Nemade, "A review: Data pre-processing and data augmentation techniques," Glob. Transit. Proc., vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
[16] S. Amaliah, M. Nusrang, and A. Aswi, "Penerapan Metode Random Forest untuk Klasifikasi Varian Minuman Kopi di Kedai Kopi Konijiwa Bantaeng," Variansi J. Stat. Its Appl. Teach. Res., vol. 4, no. 3, pp. 121–127, Dec. 2022, doi: 10.35580/variansiunm31.
[17] M. A. Ridla and A. A. Bawazin, "Klasifikasi Gangguan Tidur Berdasarkan Gaya Hidup Menggunakan Algoritma Decision Tree," J. Inf. Syst. Appl. Dev., vol. 3, no. 2, pp. 119–126, Sep. 2025, doi: 10.26905/jisad.v3i2.16038.
[18] J. R. Aryo Michael, "Klasifikasi Cacat Biji Kopi Menggunakan Metode Transfer Learning dengan Hyperparameter Tuning Gridsearch," J. Teknol. Dan Manaj. Inform., vol. 9, no. 1, pp. 37–45, 2023.
[19] N. Prayoga and M. A. Ridla, "Analisis Prediksi Diabetes Menggunakan Decision Tree pada Dataset Diabetes Pima Indians," J. Inf. Syst. Appl. Dev., vol. 4, no. 1, pp. 146–153, Mar. 2026, doi: 10.26905/jisad.v4i1.16494.
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