ANALISIS TINGKAT BAKAT SISWA DALAM MENENTUKAN EKSKUL MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN)

APRYANI CUT MULYANA, NPM 2209500177 (2026) ANALISIS TINGKAT BAKAT SISWA DALAM MENENTUKAN EKSKUL MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN). Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Perkembangan teknologi informasi mendorong pemanfaatan sistem pendukung keputusan dalam bidang pendidikan untuk membantu proses pengambilan keputusan yang lebih objektif. Penentuan ekstrakurikuler di SMA Muhammadiyah 09 Kualuh Hulu masih dilakukan secara subjektif sehingga diperlukan metode yang mampu menganalisis bakat siswa secara lebih terukur. Metode K Nearest Neighbor (KNN) merupakan salah satu teknik klasifikasi yang dapat digunakan untuk menentukan keputusan berdasarkan kedekatan data. Beberapa penelitian sebelumnya menunjukkan bahwa KNN efektif dalam mengelompokkan data berdasarkan kemiripan atribut yang dimiliki. Penelitian ini menggunakan data siswa kelas XI dengan variabel nilai akademik, minat olahraga, dan minat seni sebagai dasar analisis. Proses penelitian dilakukan dengan tahapan pengumpulan data, seleksi data, pembagian data training dan testing, serta pengolahan menggunakan aplikasi Orange Data Mining. Hasil penelitian menunjukkan bahwa metode KNN mampu menghasilkan klasifikasi yang sesuai dengan karakteristik siswa. Rekomendasi ekstrakurikuler yang dihasilkan dapat membantu pihak sekolah dalam menentukan kegiatan yang sesuai dengan bakat siswa. Dengan demikian, penerapan metode KNN dapat meningkatkan objektivitas dalam pengambilan keputusan. Hasil penelitian ini diharapkan dapat menjadi referensi dalam pengembangan sistem pendukung keputusan di bidang pendidikan. Kata Kunci: K-Nearest Neighbor, Klasifikasi, Ekstrakurikuler, Bakat Siswa, Orange Data Mining ==================================================== The development of information technology encourages the use of decision support systems in education to assist in more objective decision-making processes. The determination of extracurricular activities at SMA Muhammadiyah 09 Kualuh Hulu is still done subjectively, so a method capable of analyzing student talents more measurably is needed. The K-Nearest Neighbor (KNN) method is a classification technique that can be used to determine decisions based on data proximity. Several previous studies have shown that KNN is effective in grouping data based on the similarity of their attributes. This study used data from 11th-grade students with variables such as academic grades, sports interests, and artistic interests as the basis for analysis. The research process was carried out through the stages of data collection, data selection, data distribution for training and testing, and processing using the Orange Data Mining application. The results showed that the KNN method was able to produce classifications that were in accordance with student characteristics. The resulting extracurricular recommendations can help schools in determining activities that are in accordance with student talents. Thus, the application of the KNN method can increase objectivity in decision-making. The results of this study are expected to serve as a reference in the development of decision support systems in education. Keywords: K-Nearest Neighbor, Classification, Extracurricular Activities, Student Talent, Orange Data Mining

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	K-Nearest Neighbor, Klasifikasi, Ekstrakurikuler, Bakat Siswa, Orange Data Mining===============K-Nearest Neighbor, Classification, Extracurricular Activities, Student Talent, Orange Data Mining
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software 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 > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	11 May 2026 02:54
Last Modified:	11 May 2026 02:54
URI:	http://repository.ulb.ac.id/id/eprint/2251

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