<![CDATA[Abstract: This study aims to analyze the distribution of teaching load of teachers at SMKN 1 Stabat using the K-Means Clustering method. The main problem faced is the imbalance in the number of teaching hours between teachers which can affect the effectiveness of teaching and learning activities. The data used is the total weekly teaching hours of each teacher, which is taken from the document on the distribution of teaching tasks for the even semester of the 2024/2025 academic year. The K-Means method is used to group teachers into three categories of teaching load: light, medium, and heavy. The grouping process is carried out by determining the number of clusters (K = 3), initializing the centroid, calculating the distance of each data to the centroid, and updating the position of the centroid until the results are stable. The final results show that most teachers are included in the medium load cluster, while a small number are in the light and heavy categories. This shows that the distribution of the teaching load is not yet completely even. The application of K-Means has been proven to be able to provide an analytical picture of the distribution of teacher work, as well as support data-based decision making in education management. Keywords: K-Means Clustering, Teaching Load, Teacher Schedule, Data Clustering, SMKN 1 Stabat Abstrak: Penelitian ini bertujuan untuk menganalisis pembagian beban mengajar guru di SMKN 1 Stabat dengan menggunakan metode K-Means Clustering. Masalah utama yang dihadapi adalah ketidakseimbangan jumlah jam mengajar antar guru yang dapat memengaruhi efektivitas kegiatan belajar-mengajar. Data yang digunakan berupa total jam mengajar mingguan dari setiap guru, yang diambil dari dokumen pembagian tugas mengajar semester genap tahun ajaran 2024/2025. Metode K-Means digunakan untuk mengelompokkan guru ke dalam tiga kategori beban mengajar: ringan, sedang, dan berat. Proses pengelompokan dilakukan dengan menentukan jumlah klaster (K=3), menginisialisasi centroid, menghitung jarak masing-masing data ke centroid, serta memperbarui posisi centroid hingga hasil stabil. Hasil akhir menunjukkan bahwa sebagian besar guru tergolong dalam klaster beban sedang, sementara sebagian kecil masuk kategori ringan dan berat. Hal ini menunjukkan bahwa distribusi beban mengajar belum sepenuhnya merata. Penerapan K-Means terbukti mampu memberikan gambaran analitis terhadap distribusi kerja guru, serta mendukung pengambilan keputusan berbasis data dalam manajemen pendidikan. Kata kunci: K-Means Clustering, beban mengajar, jadwal guru, pengelompokan data, SMKN 1 Stabat]]>
