Sodiq, M. Ja'far (2020) PERBANDINGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI KUALITAS UDARA DI DKI JAKARTA. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
Air pollution is the entry or insertion of substances, energy, and / or other components into the air by human activities so that the air quality drops to a certain level that causes or influences human health. Air pollution can be caused by natural sources or from human activities such as factory activities to motor vehicle activities. The Air Pollution Standard Index (ISPU) is a number that does not have a unit that describes the condition of ambient air quality at a specific location and time issued by the Ministry of Environment and Forestry. The determination of this ISPU considers the level of air quality on the health of humans, animals, plants, buildings and aesthetic values. The Air Pollution Standard Index (ISPU) is set based on 5 pollutants, namely carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), surface ozone (O3) and dust particles (PM10). The Naive Bayes algorithm is a classification method using probability and statistical methods proposed by British scientist Thomas Bayes. The Naive Bayes algorithm predicts future opportunities based on past experience so it is known as the Bayes Theorem. The K-Nearest Neighbor (KNN) algorithm is a classification method for a set of data based on previously classified data learning. In this study the author compared the accuracy of the Naive Bayes algorithm and K-Nearest Neighbor in the classification of air quality based on the air pollution standard index (ISPU). This study resulted in an accuracy of naïve bayes of 91,862% and k-nearest neighbor accuracy of 97,3396%. Keywords: Air Pollution, ISPU, Naïve Bayes, KNN
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 26 Mar 2020 07:41 |
| Last Modified: | 26 Mar 2020 07:41 |
| URI: | http://eprints.uty.ac.id/id/eprint/4903 |
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