Implementasi Clustering Data Mahasiswa Program Studi Teknik Informatika STMIK Widya Cipta Dharma Menggunakan Metode K-Means

Eksakta Ramang Kridho, Haryono (2025) Implementasi Clustering Data Mahasiswa Program Studi Teknik Informatika STMIK Widya Cipta Dharma Menggunakan Metode K-Means. S1 Teknik Informatika thesis, STMIK Widya Cipta Dharma.

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Abstract

Penelitian ini bertujuan untuk mengelompokkan data mahasiswa pada Program Studi Teknik Informatika STMIK Widya Cipta Dharma Samarinda menggunakan metode K-Means Clustering. Latar belakang penelitian ini adalah belum optimalnya pemanfaatan data akademik dan administratif yang tersedia dalam jumlah besar, sehingga belum mampu memberikan dukungan yang maksimal dalam proses pengambilan keputusan. Penelitian ini menerapkan algoritma K-Means untuk mengelompokkan mahasiswa berdasarkan kesamaan atribut akademik dan non-akademik. Data yang digunakan mencakup tahun ajaran 2019 hingga 2023, dengan variabel seperti Nomor Induk Mahasiswa, Semester, Jenis Kelamin, Agama, Status Perkawinan, Status Bekerja, Pekerjaan, Nama Asal Sekolah, Jurusan Sekolah, Provinsi Asal, Kota/Kabupaten, Kecamatan, Kelurahan/Desa, Kode Pos, Pekerjaan Ayah, Pekerjaan Ibu, Tahun Ajaran, IPK, Nama Kompetisi, Peringkat Juara, Sumber Informasi Pendaftaran, Alasan Memilih STMIK, Mata Kuliah Favorit, Mata Kuliah Kurang Favorit, dan Hobi. Penentuan jumlah cluster optimal dilakukan menggunakan metode Elbow. Implementasi dilakukan menggunakan Jupyter Notebook, dan hasilnya diintegrasikan ke dalam platform web interaktif. Hasil implementasi menunjukkan bahwa algoritma K-Means mampu mengelompokkan mahasiswa secara efektif berdasarkan karakteristik akademik dan aktivitas ekstrakurikuler. Hasil pengelompokan ini memberikan wawasan yang bermanfaat untuk merancang strategi pembelajaran yang sesuai, menyusun kebijakan akademik, mendukung strategi promosi, dan mengoptimalkan sumber daya pendidikan di STMIK Widya Cipta Dharma, sehingga dapat meningkatkan kualitas akademik dan perencanaan institusi secara keseluruhan. ============================================================= This research aims to cluster student data in the Informatics Engineering Study Program at STMIK Widya Cipta Dharma Samarinda using the K-Means Clustering method. The background of this study lies in the underutilization of the institution’s large volume of academic and administrative data, which has not been effectively analyzed to support decision-making processes. This study applies the K-Means algorithm to group students based on similarities in academic and personal attributes. The dataset consists of records from 2019 to 2023 and includes variables such as Student ID Number, Semester, Gender, Religion, Marital Status, Employment Status, Occupation, Name of School of Origin, School Major, Province, City, District, Village, Postal Code, Father’s Occupation, Mother’s Occupation, Academic Year, GPA, Competition Name, Champion Rank, Registration Source, Reason for Choosing STMIK, Favorite Courses, Less Favorite Courses, and Hobbies. The optimal number of clusters is determined using the Elbow Method. The implementation is carried out using Jupyter Notebook, and the results are deployed into a web-based interactive platform. The results of this implementation indicate that the K-Means algorithm can effectively identify student groupings based on both academic performance and extracurricular involvement. These groupings provide valuable insights for developing tailored learning strategies, refining academic policies, supporting promotional efforts, and optimizing educational resources at STMIK Widya Cipta Dharma, ultimately contributing to the improvement of academic quality and institutional planning..

Item Type: Thesis (S1 Teknik Informatika)
Additional Information: Siti lailiyah S.Kom., M.Kom Muhammad Ibnu Sa'ad S.Kom., M.Kom
Uncontrolled Keywords: Teknik Informatika, Clustering Mahasiswa, K-Means
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Teknik Informatika
Depositing User: Mr Eksakta Ramang Kridho Haryono
Date Deposited: 12 Aug 2025 01:54
Last Modified: 12 Aug 2025 01:54
URI: http://repository.wicida.ac.id/id/eprint/6243

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