TIARA RIYANTO, NPM 2107100014 (2024) IMPLEMENTATION OF KNN METHOD TO DETERMINE PROSPECTIVE STUDENT INTERESTS PENERAPAN METODE KNN UNTUK MENENTUKAN MINAT CALON MAHASISWA. Tugas_Akhir(Artikel) INFORMATIKA, 12 (3). pp. 521-528. ISSN 2615-1855 (e-ISSN) 2303-2863 (p-ISSN)
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Abstract
This study focuses on the implementation of data mining to determine the interests of prospective male and female students in the Informatics Management Department using the K-Nearest Neighbors (KNN) method. The analysis process is carried out through the Knowledge Discovery in Databases (KDD) stages, which include data selection, pre-processing, transformation, data mining, and pattern evaluation. The KDD stage ensures that the data used has been prepared and processed properly to produce an accurate and relevant model. The KNN method is used to classify sample data consisting of 82 prospective male and female students. The results of this study indicate that 63 out of 82 prospective students are interested in the Informatics Management Department, while 19 other prospective students are not interested. This classification process shows that the KNN method is able to identify the interests of prospective students with a high level of accuracy, providing useful information for universities in understanding the preferences of their prospective students. Evaluation of the research results using two evaluation tools, namely Test and Score and Confusion Matrix, showed perfect results with an accuracy of 100%. Both of these evaluation tools are consistent in assessing the performance of the KNN model, confirming that this model works very well in classifying prospective student interests. In conclusion, the KNN method is proven to be effective and reliable in determining prospective students' interest in the Informatics Management Department, providing a strong foundation for similar applications in the future. Keywords: Data Mining, K-Nearest Neighbor (KNN) method, Knowledge Discovery in Databases (KDD), Confusion Matrix, Box Plots
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Data Mining, K-Nearest Neighbor (KNN) method, Knowledge Discovery in Databases (KDD), Confusion Matrix, Box Plots |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Informatika Komputer |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 09 Jun 2026 04:40 |
| Last Modified: | 09 Jun 2026 04:40 |
| URI: | http://repository.ulb.ac.id/id/eprint/2525 |
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