Penerapan Naïve Bayes untuk Klasifikasi Kriteria Air Layak Minum dengan Metode CRISP-DM - Ibnu Alfitra Salam; Katon Wahyudi Putra; Sisca Yuliatina; Betha Nurina Sari (LPPM Universitas Bina Sarana Informatika, 2023)
With water, living things can do various things easily. The adequacy of water is also important in maintaining human health. Water can be said to be feasible if its content is in accordance with the feasible criteria. From the dataset obtained regarding the feasibility of water for this study, it will calculate the accuracy value obtained using the Naive Bayes algorithm. To simplify the process of processing research data this time using the CRISP-DM methodology which is a stage for data mining. The study uses two tools, namely Rapidminer and Google Collab to compare their accuracy values. By using the two tools in implementing the Naive Bayes algorithm on a potable water quality dataset, an accuracy of 62.8% is obtained. This value is accurate enough to predict the quality of drinking water.