Hardiana Said, . (2021) PERANCANGAN SISTEM DAN PENERAPAN ALGORITMA K-NEAREST NEIGHBOR (KNN) UNTUK MEMPREDIKSI KUALITAS AIR YANG DAPAT DIKONSUMSI. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The water quality that is safe for use is especially important for public health in every area, but water quality in various areas is decreasing for human needs in drinking water, the impact of water quality that is not safe for consumption can cause diseases such as cholera, diarrhea, hepatitis and others, this is due to water that has poor sanitation and substances that exceed standard levels. This research was conducted to create a prediction system about water and to support predictions by applying the application of data mining classification, namely the K-Nearest Neighbor algorithm. This algorithm is applied to calculate the probability that water quality is safe or not based on recorded data taken from the surrounding environment, especially in densely populated areas. A collection of data obtained from the kaggle website for experiments on the system to be created. The results of the modeling using the Confusion Matrix table to calculate accuracy. After being tested, this model has the highest accuracy rate of 85.52% with a value of k (nearest neighbor) = 3. Based on the modeling results, a web-based application is then made to predict the quality of water that can be consumed.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810512020] [Pembimbing 1: Nurhafifah Matondang] [Pembimbing 2: Helena Nurramdhani Irmanda] [Penguji 1: Iin Ernawati] [Penguji 2: Ika Nurlaili Isnainiyah] |
| Uncontrolled Keywords: | Water Quality, Prediction, Classification, System, K-Nearest Neighbor |
| Subjects: | T Technology > T Technology (General) T Technology > TP Chemical technology |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1) |
| Depositing User: | Hardiana Said |
| Date Deposited: | 04 Feb 2022 04:28 |
| Last Modified: | 21 Feb 2022 05:44 |
| URI: | http://repository.upnvj.ac.id/id/eprint/15756 |
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