PENERAPAN ALGORITMA K – MEANS CLUSTERING UNTUK MENGETAHUI PRESTASI NILAI AKADEMIK SISWA DI SMKN 3 RANTAU UTARA

AIDA AMELIA, NPM 2109100004 (2025) PENERAPAN ALGORITMA K – MEANS CLUSTERING UNTUK MENGETAHUI PRESTASI NILAI AKADEMIK SISWA DI SMKN 3 RANTAU UTARA. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini bertujuan untuk menerapkan algoritma K-Means Clustering dalam mengelompokkan prestasi nilai akademik siswa di SMKN 3 Rantau Utara. Data yang digunakan adalah nilai rapor semester ganjil siswa kelas X angkatan 2024/2025, yang mencakup berbagai mata pelajaran. Metodologi yang digunakan mencakup analisa masalah, pengumpulan data, studi literatur, penerapan algoritma K-Means baik secara manual maupun melalui perangkat lunak RapidMiner, serta evaluasi hasil cluster. Hasil penelitian menunjukkan bahwa data dapat diklasifikasikan ke dalam tiga kategori, yaitu sangat berprestasi, rata-rata, dan kurang berprestasi. Dari total 142 siswa, terdapat 32 siswa dalam kategori kurang berprestasi, 56 siswa kategori rata-rata, dan 54 siswa sangat berprestasi. Hasil pengelompokan ini dapat digunakan pihak sekolah untuk menentukan strategi pembelajaran yang lebih efektif dan intervensi yang tepat sasaran, seperti pemberian bimbingan tambahan bagi siswa kurang berprestasi dan tantangan tambahan untuk siswa sangat berprestasi. Kesimpulan dari penelitian ini adalah penerapan algoritma K-Means terbukti efektif dalam mengelompokkan data akademik siswa yang selanjutnya dapat dijadikan dasar dalam penyusunan kebijakan pendidikan yang lebih optimal di lingkungan SMKN 3 Rantau Utara. Penelitian ini juga memberikan kontribusi teoretis berupa referensi bagi penerapan pengelolaan data akademik menggunakan algoritma data mining di bidang pendidikan. Kata Kunci: K-Means Clustering, Prestasi Akademik, RapidMiner, Data Mining, SMKN 3 Rantau Utara ================================================================================================= This research aims to apply the K-Means Clustering algorithm to classify the academic achievements of students at SMKN 3 Rantau Utara. The data utilized consists of the odd semester report card scores of Grade X students from the 2024/2025 academic year, covering various subjects. The methodology includes problem analysis, data collection, literature review, implementation of the K-Means algorithm—both manually and using RapidMiner software—and evaluation of the clustering results. The findings show that student data can be categorized into three groups: high achievers, average, and underachievers. Out of a total of 142 students, 32 students were classified as underachievers, 56 as average, and 54 as high achievers. These classifications provide valuable insights for the school to formulate more effective learning strategies and targeted interventions, such as offering additional guidance for underachieving students and further academic challenges for high achievers. The conclusion of this study is that the application of the K-Means algorithm has proven effective in categorizing student academic performance, which can subsequently be used as a basis for developing more optimal educational policies within SMKN 3 Rantau Utara. This research also makes a theoretical contribution by serving as a reference for the implementation of data mining algorithms in academic data management within the educational sector. Keywords: K-Means Clustering, Academic Achievement, RapidMiner, Data Mining, SMKN 3 Rantau Utara

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	K-Means Clustering, Prestasi Akademik, RapidMiner, Data Mining, SMKN 3 Rantau Utara================K-Means Clustering, Academic Achievement, RapidMiner, Data Mining, SMKN 3 Rantau Utara
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 > ZA4050 Electronic information resources 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:	27 Oct 2025 02:12
Last Modified:	27 Oct 2025 02:14
URI:	http://repository.ulb.ac.id/id/eprint/1851

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