ANALYSIS OF PUBLIC INTEREST IN AUTOMATIC MOTORCYCLES USING KNN AND NEURAL NETWORK METHODS

NURUL FATMA, NPM 2009100069 (2024) ANALYSIS OF PUBLIC INTEREST IN AUTOMATIC MOTORCYCLES USING KNN AND NEURAL NETWORK METHODS. Tugas_Akhir (Artikel) Sinkron : Jurnal dan Penelitian Teknik Informatika, 8 (2). pp. 1178-1187. ISSN 2541-2019 (e-ISSN) / 2541-044X (p-ISSN)

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Abstract

Research on public interest in automatic motorbikes was carried out with the aim of understanding the factors that influence the decision to purchase an automatic motorbike. Using data mining methods, this research applies the K Nearest Neighbor (KNN) and Neural Network techniques to identify and analyze people's interest patterns. The data used amounted to 139 samples, of which 127 showed interest in automatic motorbikes, while 12 others showed no interest. The research process begins with data analysis, the next stage is preprocessing, which includes data cleaning, in the model design stage in data mining, two models are built: one using KNN and the other using Neural Network. These two models are designed to classify sample data based on interest in automatic motorbikes. The next stage is model testing. Test results show that both models can classify interests accurately, with most of the sample data being classified correctly. Model evaluation was carried out to measure the effectiveness and accuracy of the two methods. The evaluation results show that both models provide very good performance, with results that almost reach a perfect score. This shows that both methods, KNN and Neural Network, are very effective in classifying and predicting people's interest in automatic motorbikes based on available data. In conclusion, this research not only shows the effectiveness of KNN and Neural Network in data mining for analyzing people's interests, but also provides valuable insights for automatic motorbike manufacturers and sellers about consumer preferences. Keywords : Classification; Confusion Matrix; Data Mining; K-Nearest Neighbor (KNN); Neural Network

Item Type: Article
Uncontrolled Keywords: Classification; Confusion Matrix; Data Mining; K-Nearest Neighbor (KNN); Neural Network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User: Unnamed user with email repository@ulb.ac.id
Date Deposited: 03 Jun 2024 04:56
Last Modified: 03 Jun 2024 04:56
URI: http://repository.ulb.ac.id/id/eprint/723

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