Analisis dan Visualisasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means Clustering dan Silhoutte Score Berdasarkan Asal Daerah
Abstract
Academic achievement of graduates serves as a key indicator of higher education quality and graduates’ readiness to enter the workforce. This study examines the Christian Religious Education Study Program at the State Christian Institute of Ambon (IAKN Ambon) during the 2010–2020 period, focusing on the influence of students’ district of origin on graduation rates and Grade Point Average (GPA). Employing a descriptive quantitative approach based on secondary data, the analysis was conducted using descriptive statistics and the K-Means clustering method, with the optimal number of clusters evaluated through the silhouette score. The findings reveal a concentration of students from several major districts, including Central Maluku, Ambon City, and West Seram Regency. Four performance clusters were identified: high-performing, moderate-performing, challenging, and low-performing/no graduates. An Average Silhouette Score of 0.77 indicates good cluster quality and strong separation among groups. The study highlights disparities in academic achievement based on students’ geographical origins, suggesting the need for more targeted interventions and stronger collaboration with students’ home districts to improve academic success and graduation outcomes.
Keywords: Academic Performance; District of Origin; Graduation Rate; K-Means Clustering; Silhouette Score
Abstrak
Prestasi akademik lulusan menjadi indikator utama mutu pendidikan tinggi dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini mengkaji Program Studi Pendidikan Agama Kristen (PAK) Institut Agama Kristen Negeri Ambon periode 2010–2020, dengan fokus pada pengaruh asal kabupaten terhadap tingkat kelulusan dan IPK. Menggunakan pendekatan kuantitatif deskriptif berbasis data sekunder, analisis dilakukan melalui statistik deskriptif dan metode K-Means clustering, dengan evaluasi jumlah klaster menggunakan silhouette score. Hasil menunjukkan konsentrasi mahasiswa pada beberapa kabupaten utama seperti Maluku Tengah, Kota Ambon, dan Seram Bagian Barat. Ditemukan empat klaster kinerja, yaitu tinggi, moderat, menantang, dan rendah/tanpa lulusan. Nilai Average Silhouette Score sebesar 0,77 menunjukkan kualitas klaster yang baik. Studi ini mengungkap adanya perbedaan capaian akademik berdasarkan asal daerah, sehingga diperlukan intervensi yang lebih terarah serta kerja sama dengan daerah asal mahasiswa untuk meningkatkan keberhasilan studi.
Kata kunci: Asal Kabupaten; Clustering: K-Means; Silhoutte Score; Tingkat Kelulusan
References
E. Alyahyan and D. Düştegör, “Predicting academic success in higher education: literature review and best practices,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, pp. 57-77, Dec. 2020, doi: 10.1186/s41239-020-0177-7.
S. Rizky Rahayu and D. Murni, “Analisis Faktor-Faktor Yang Mempengaruhi Indeks Prestasi Kumulatif Mahasiswa Dapartemen Matematika FMIPA UNP Dengan Pendekatan Metode CHAID,” Jurnal Pendidikan Tambusai, vol. 8, no. 2, pp. 29858–29868, Aug. 2024.
Hadi Putra, Khairunnisa Nasution, and Elkin Rilvani, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Decision Tree: Studi Perbandingan Algoritma ID3 DAN C4.5,” JURNAL MEDIA AKADEMIK, vol. 3, no. 7, pp.49-63, Jul. 2025.
Destu Satya Widyaningsih, “Hubungan Antara Motivasi Belajar Dan Asal Daerah Dengan Prestasi Belajar Mahasiswa Di Akademi Manajemen Administrasi Yogyakarta,” JURNAL AMA YOGYAKARTA, vol. 8, pp.29-38, 2017.
H. Ermewaningsih, F. E. Renni Susanti, and Y. Yus, “Klasterisasi Kinerja Akademik Mahasiswa Berdasarkan IPK Menggunakan K-Means dan Metode Elbow,” Jurnal Pendidikan dan Teknologi Indonesia, vol. 6, no. 3, pp. 619–625, Apr. 2026.
R. A. H. Annafi, “Pengaruh tingkat prokrastinasi akademik pada mahasiswa ditinjau dari asal daerah,” Skripsi, Prodi Psikologi, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, 2025.
M. W. Agustina and Putri Deastuti Pracintya Wahyu, “Hardiness Dan Stress Akademik Pada Mahasiswa Rantau,” IDEA: Jurnal Psikologi, vol. 7, no. 1, pp. 34–45, Apr. 2023.
C. P. Lawrence and M. Mudzakkir, “Penyesuaian Diri Mahasiswa Rantau Indonesia Timur di Universitas Negeri Surabaya,” The Sociology Journal Paradigma, vol. 13, no. 1, pp. 11–20, Jan. 2024.
W. Atika, “Analisis Efisiensi Dan Produktivitas Program Studi S – 1 Di Fakultas Teknik Universitas Negeri Yogyakarta,” Skripsi, Prodi Pendidikan Teknik Elektronika, Universitas Negeri Yogyakarta, Yogyakarta, 2017.
H. A. Madni, Z. Anwar, and M. A. Shah, “Data mining techniques and applications — A decade review,” in 23rd International Conference on Automation and Computing (ICAC), IEEE, Sep. 2017, pp. 1–7. doi: 10.23919/IConAC.2017.8082090.
S. Harikumar and P. v. Surya, “K-Medoid Clustering for Heterogeneous DataSets,” in 4th International Conference on Eco-friendly Computing and Communication Systems (ICECCS), Kurukshetra, pp. 226–237, 7-8 Dec. 2015, doi: 10.1016/j.procs.2015.10.077.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM), vol. 3, no. 1, pp. 46–56, Mar. 2024, doi: 10.58602/jima-ilkom.v3i1.26.
Bakhtiar Robi Tegar, Herlambang Agus Bambang, and Anam Khoirul Ahmad, “Penggunaan SIG untuk Pemetaan dan Analisis Jumlah Sebaran mahasiswa dan Perguruan Tinggi di Kabupaten Brebes,” Jurnal Ilmiah Research Student, vol. 1, no. 3, pp. 641–650, Jan. 2024.
F. Rolansa, “Pengembangan interaktif dashboard kemahasiswaan di program studi teknik informatika dengan teknologi big data,” Jurnal Pendidikan Informatika dan Sains, vol. 10, no. 2, pp. 110–118, Dec. 2021, doi: 10.31571/saintek.v10i2.2190.
A. Ramadhan, F. Achmad, I. Zulkarnain, and M. Aritsugi, “Evaluation of K-Means, DBSCAN, and Hierarchical Clustering for Strategic Segmentation of Tourism SMEs in Rembang, Indonesia,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1605–1630, Jul. 2025, doi: 10.52436/1.jutif.2025.6.3.4602.
M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means Algorithm: A Comprehensive Survey and Performance Evaluation,” Electronics (Basel)., vol. 9, no. 8, p. 1295, Aug. 2020, doi: 10.3390/electronics9081295.
B. Syaeful Hadi, “Urgensi Dimensi Faktor Geografis Dalam Penentuan Kebijakan Komunikasi Dan Informasi,” Geomedia: Majalah Ilmiah dan Informasi Kegeografian, vol. 5, no. 1, pp.129-136, May 2017, doi: 10.21831/gm.v5i1.14209.
T. T. D. Susanto, L. Syafruddin, and S. F. Abdullah, “Peningkatan Kualitas Pengambilan Keputusan Melalui Manajemen Berbasis Data Di Sekolah,” JURNAL MADINASIKA Manajemen Pendidikan dan Keguruan, vol. 6, no. 2, pp. 235–250, Jun. 2025, doi: 10.31949/madinasika.v6i2.13723.
A. M. Bagirov, R. M. Aliguliyev, and N. Sultanova, “Finding compact and well-separated clusters: Clustering using silhouette coefficients,” Pattern Recognition, vol. 135, pp.109-144. 2023, doi: 10.1016/j.patcog.2022.109144.
Kusaeri and N. Wahyudi, “The Relative Influence of Age, Gender, Regional Differences and Their Relationship with Students’ Numeracy Levels on Madrasah Competency Assessment in Indonesia,” JPI (Jurnal Pendidikan Indonesia), vol. 13, no. 2, pp. 288–297, 2024, doi: 10.23887/jpiundiksha.v13i2.63570.
R. Lembani, A. Gunter, M. Breines, and M. T. B. Dalu, “The same course, different access: the digital divide between urban and rural distance education students in South Africa,” Journal of Geography in Higher Education, vol. 44, no. 1, pp. 70–84, 2020, doi: 10.1080/03098265.2019.1694876.
S.B.H. Sakur, M. Silangen, & D. Tuwohingide, “Penerapan Algoritma K-Means Cluster dan Metode TOPSIS pada Pemilihan Mahasiswa kunjungan Industri. Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 11, no. 3, pp. 851-860, 2022, doi:http://dx.doi.org/10.35889/jutisi.v11i3.1045.
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