Pengelompokan Seleksi Siswa Baru di Lembaga Pendidikan Non Formal Kabupaten Gresik Menggunakan Clustering K-Medoids

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Nia Saurina
Lestari Retnawati
Firman Hadi Sukma Pratama
Udik Pudjianto

Abstract

Non-formal education has different programs according to the current and future needs of society. The course institutions in Gresik Regency are the unofficial educational institutions of the Gresik Regency Education Office which organize programs, namely the SMA Package C Equivalency Education Program, the SMP Package B Equality Education Program and the Sewing Course. The current grouping of new student selection is felt to be less effective because the grouping is based on certificates obtained more than two years ago. This is because most non-formal students drop out of school due to financial constraints. So that the value given to the certificate is not in accordance with the qualifications of students while taking informal education. This study uses the K-Medoids cluster using datasets, namely practical exam scores, final exam scores and interview scores. The data used are students enrolled in package C as high school equivalents as many as 28 students and 32 students enrolled in package B as high school equivalents. The results obtained in this study are educational institutions that can place prospective students at a more accurate value. The Medoid-K clustering procedure for new student clustering requires two iterations to get a total S-distance of 54.594255.

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References

H. Haerullah and E. Elihami, “Dimensi Perkembangan Pendidikan Formal Dan Non Formal,” J. Edukasi Nonform., vol. 1, no. 1, pp. 199–207, 2020.

M. A. Hidayat, A. Anwar, and N. Hidayah, “Pendidikan Non Formal Dalam Meningkatkan Keterampilan Anak Jalanan,” Edudeena J. Islam. Relig. Educ., vol. 1, no. 1, pp. 31–42, 2017, doi: https://doi.org/10.30762/ed.v1i1.445.

M. I. Chanafi, D. P. Hapsari, R. K. Hapsari, and T. Indriyani, “Implementasi Algoritma Clustering Untuk Pengelompokan Pelanggan Retail Berdasarkan Skor Recency, Frequency, Dan Monetary,” in Prosiding Seminar Nasional Sains dan Teknologi Terapan, 2019, pp. 797–810.

T. Suprawoto, “Klasifikasi Data Mahasiswa Menggunakan Metode Kmeans Untuk Menunjang Pemilihan Strategi Pemasaran,” JIKO (Jurnal Inform. Dan Komputer), vol. 1, no. 1, pp. 12–18, 2016, doi: http://dx.doi.org/10.26798/jiko.v1i1.9.

T. Velmurugan, “Efficiency of k-Means and K- Medoids Algorithms for Clustering Arbitrary Data Points,” Int.J.Computer Technol. Appl., vol. 3, no. 5, pp. 1758–1764, 2012.

Y. H. Chrisnanto and G. Abdillah, “Penerapan Algoritma Partitioning Around Medoids (PAM) Clustering untuk Melihat Gambaran Umum Kemampuan Akademik Mahasiswa,” in Seminar Nasional Teknologi Informasi dan Komunikasi 2015 (SENTIKA 2015), 2015, pp. 444–448.

D. Marlina, N. F. Putri, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT, vol. 4, no. 2, pp. 64–71, 2018.

Y. Religia, A. E. Intani, and A. Saputra, “Pengelompokan Menggunakan Algoritma K-Medoid Untuk Evaluasi Performa Siswa,” Pelita Teknol., vol. 15, no. 1, pp. 49–55, 2020, doi: https://doi.org/10.37366/pelitatekno.v15i1.281.

A. P. Fialine, D. A. Alodia, D. Endriani, and E. Widodo, “Implementasi Metode K-Medoids Clustering untuk Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pendidikan,” SEPREN J. Math. Educ. Appl., vol. 3, no. 1, pp. 1–13, 2021.

I. W. Budiarta, I. P. H. M. Martayana, and I. W. T. Mahardika, “Pelatihan Digitalisasi Materi Dan Media Pembelajaran Di PKBM Lestari Desa Pejarakan, Kecamatan Gerokgak Dalam Rangka Menunjang Proses Belajar Mengajar Di Era New Normal,” in Proceeding Senadimas Undiksha, 2021, pp. 1610–1615.

Presiden Republik Indonesia, Peraturan Pemerintah Republik Indonesia Nomor 13 Tahun 2015 Tentang Perubahan Kedua Atas Peraturan Pemerintah Nomor 19 Tahun 2005 Tentang Standar Nasional Pendidikan. Indonesia, 2015.

B. S. Praja, P. D. Kusuma, and C. Setianingsih, “Penerapan Metode K-Means Clustering Dalam Pengelompokan Data Penumpang Dan Kapal Angkutan Laut Di Indonesia,” eProceedings Eng., vol. 6, no. 1, pp. 1442–1449, 2019.

D. Nugroho, F. Nhita, and D. T. Murdiansyah, “Prediksi Penyakit Menggunakan Genetic Algorithm (GA) dan Naive Bayes Untuk Data Berdimensi Tinggi Prediction of Disease Using Genetic Algorithm (GA) and Naive Bayes For Data High Dimension,” e-Proceeding Eng., vol. 3, no. 2, pp. 3889–3899, 2016.

M. A. Nahdliyah, T. Widiharih, and A. Prahutama, “Metode K-Medoids Clustering Dengan Validasi Silhouette Index Dan C-Index (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018),” J. Gaussian, vol. 8, no. 2, pp. 161–170, 2019, doi: https://doi.org/10.14710/j.gauss.8.2.161-170.