ANALISIS ALGORITMA K-MEANS DALAM CLUSTERING SISWA BERPRESTASI PADA PELAJARAN MATEMATIKA (STUDI KASUS : SMA SWASTA KEMALA BHAYANGKARI RANTAUPRAPAT)

AGATHA CHRISTIORENFA BR HALOHO, NPM 2109100139 (2026) ANALISIS ALGORITMA K-MEANS DALAM CLUSTERING SISWA BERPRESTASI PADA PELAJARAN MATEMATIKA (STUDI KASUS : SMA SWASTA KEMALA BHAYANGKARI RANTAUPRAPAT). Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini bertujuan untuk menganalisis penerapan algoritma K-Means Clustering dalam mengelompokkan siswa berdasarkan prestasi belajar Matematika di SMA Swasta Kemala Bhayangkari Rantauprapat. Masalah utama yang dihadapi sekolah adalah belum adanya metode objektif untuk mengklasifikasikan siswa sesuai tingkat kemampuan akademik, sehingga diperlukan pendekatan berbasis data. Penelitian menggunakan data nilai harian, UTS, UAS, dan nilai akhir siswa kelas X, XI, dan XII tahun ajaran 2024/2025. Tahapan penelitian meliputi pengumpulan data, preprocessing (pembersihan, normalisasi, dan penanganan missing value), penerapan algoritma K-Means, evaluasi hasil klaster dengan Davies-Bouldin Index (DBI) dan Silhouette Coefficient, serta implementasi menggunakan perangkat lunak RapidMiner. Hasil analisis menunjukkan pembentukan tiga klaster: (1) siswa berprestasi tinggi dengan rata-rata nilai 88–95, (2) siswa berprestasi sedang dengan rata-rata nilai 80–85, dan (3) siswa berprestasi rendah dengan rata-rata nilai 70–76. Nilai DBI sebesar 0,182 menandakan kualitas klaster yang baik dengan tingkat pemisahan yang jelas antar kelompok. Temuan ini membuktikan bahwa algoritma K-Means efektif dalam memberikan segmentasi akademik yang akurat dan dapat menjadi dasar penyusunan strategi pembelajaran diferensiatif, seperti pemberian program pengayaan bagi siswa berprestasi tinggi serta pendampingan khusus bagi siswa berprestasi rendah. Penelitian ini juga menunjukkan potensi penerapan data mining dalam pendidikan sebagai alat bantu pengambilan keputusan berbasis analisis data yang sistematis. Kata Kunci: K-Means Clustering, Data Mining, RapidMiner, Prestasi Belajar Matematika, Evaluasi Klaster ================================================================================================== This study aims to analyze the application of the K-Means Clustering algorithm in classifying students based on their Mathematics achievement at SMA Swasta Kemala Bhayangkari Rantauprapat. The main issue faced by the school is the absence of an objective method to categorize students according to their academic performance, making a data-driven approach necessary. The research uses data from daily assignments, midterm exams, final exams, and final Mathematics scores of students in grades X, XI, and XII for the 2024/2025 academic year. The research stages include data collection, preprocessing (data cleaning, normalization, and handling of missing values), implementation of the K-Means algorithm, cluster evaluation using the Davies-Bouldin Index (DBI) and Silhouette Coefficient, and system implementation with RapidMiner software. The analysis results show three distinct clusters: (1) high-achieving students with an average score of 88–95, (2) moderate-achieving students with an average score of 80–85, and (3) low achieving students with an average score of 70–76. The DBI value of 0.182 indicates good cluster quality and clear separation among groups. These findings demonstrate that the K-Means algorithm effectively provides accurate academic segmentation and can serve as a basis for differentiated learning strategies, such as enrichment programs for high-achieving students and special assistance for low achieving students. This research also highlights the potential of applying data mining in education as a systematic data-driven decision-making tool. Keywords: K-Means Clustering, Data Mining, RapidMiner, Mathematics Achievement, Cluster Evaluation

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	K-Means Clustering, Data Mining, RapidMiner, Prestasi Belajar Matematika, Evaluasi Klaster==============K-Means Clustering, Data Mining, RapidMiner, Mathematics Achievement, Cluster Evaluation
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:	12 Jan 2026 02:43
Last Modified:	12 Jan 2026 02:43
URI:	http://repository.ulb.ac.id/id/eprint/2052

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