Adinda Amalia, . (2022) PREDIKSI KUALITAS UDARA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
In everyday life, air is used to breathe by living things. Clean air contains many benefits for life. However, the air found in nature is not always clean, so it can cause a decrease in air quality. Poor air quality can have an impact on human health and the surrounding environment. Air quality in DKI Jakarta can be known through the Indeks Standar Pencemar Udara (ISPU). This study aims to predict the air quality in DKI Jakarta based on ISPU data. Prediction is done using data mining techniques with classification methods. The algorithm used to make predictions in this study is the K-Nearest Neighbor (KNN) algorithm. This algorithm is an algorithm that classifies new object classes based on their closest neighbors. The amount of data used in this study was 445 data, then divided the data into 2, namely training data and test data. This study also includes performance measurements that include the values of accuracy, precision, recall, and f-measure for each tested K value. This measurement is carried out to determine the optimal parameters in the dataset used. The results obtained from testing the values of K = 3 to K = 9, it was found that the value of K = 7 had the best performance with the highest accuracy of 96%, precision of 92%, recall of 95%, and f-measurement of 93%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810512026] [Pembimbing 1: Ati Zaidiah] [Pembimbing 2: Ika Nurlaili Isnainiyah] [Penguji 1: Ermatita] [Penguji 2: Helena Nurramdhani Irmanda] |
| Uncontrolled Keywords: | classification, air quality, data mining, k-nearest neighbor |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1) |
| Depositing User: | Adinda Amalia |
| Date Deposited: | 04 Mar 2022 03:45 |
| Last Modified: | 04 Mar 2022 03:45 |
| URI: | http://repository.upnvj.ac.id/id/eprint/15561 |
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