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Integration of Fuzzy C-Means and SAW Methods on Education Fee Assistance Recipients
Corresponding Author(s) : Yana Mulyana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 2, May 2023
Abstract
Every year, UMTAS gets a quota for KIP tuition fee assistance provided by KEMDIKBUD. This program is intended for high school / vocational/equivalent graduates from poor and vulnerable families. The evaluation results of its implementation have problems, including the number of applicants exceeding the quota given by KEMDIKBUD and some applicants coming from well-off families. This research uses the fuzzy c-means method for data clustering and the SAW method for ranking. The results of data grouping using the fuzzy c-means method obtained the first cluster (C1) of 72 data and the second cluster (C2) of 119 data. Group C1 is closer to the provisions of aid recipients (eligible) compared to data group C2 (ineligible) because Data C1 consists of 100% DTKS recipients, 50% KIP and KKS card owners, 100% parental income <750,000, 40.28% parental dependents >=2 people and 29.17% applicants with achievements. 72 registrant data included in Data C1 are then ranked using the SAW technique to get weights, and 30 data with the highest weight will be used as a decision on recipients of KIP-Kuliah Education fee assistance according to the quota provided. The optimization of Fuzzy C-Means with SAW methods in selecting recipients of education fee assistance is objective and right on target.
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- F. Sabilah and S. Salahudin, “Public Policy Analysis on Education Budget Allocation: Case Study in Thirty-Eight Regencies/Municipalities in East Java, Indonesia,” J. Stud. Pemerintah., vol. 13, no. 1, pp. 59–85, 2022. https://doi.org/10.18196/jgp.v13i1.12529
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- W. Gunawan and B. S. P. Diwiryo, “Implementasi Algoritma Fuzzy C-Means Clustering Sistem Crowdfunding pada Sektor Industri Kreatif Berbasis Web,” J. Edukasi dan Penelit. Inform., vol. 6, no. 2, p. 193, 2020. http://dx.doi.org/10.26418/jp.v6i2.38018
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- N. Noviyanto, “Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 183–188, 2020. https://doi.org/10.31294/p.v22i2.8808
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References
F. Sabilah and S. Salahudin, “Public Policy Analysis on Education Budget Allocation: Case Study in Thirty-Eight Regencies/Municipalities in East Java, Indonesia,” J. Stud. Pemerintah., vol. 13, no. 1, pp. 59–85, 2022. https://doi.org/10.18196/jgp.v13i1.12529
Permendikbud No 10 Tahun 2020, “Peraturan Mentri Pendidikan dan Kebudayaan Republik Indonesia Nomor 10 Tahun 2020 Tentang Program Indonesia Pintar,” Menteri Pendidik. dan Kebud. RI, p. 82, 2020.
R. Trisudarmo, E. Sediyono, and J. E. Suseno, “Combination of Fuzzy C-Means Clustering Methods and Simple Additive Weighting in Scholarship of Decision Support Systems,” Proc. 1st Annu. Int. Conf. Nat. Soc. Sci. Educ. (ICNSSE 2020), vol. 547, no. Icnsse, pp. 161–169, 2021. https://doi.org/10.2991/assehr.k.210430.025
W. Gunawan and B. S. P. Diwiryo, “Implementasi Algoritma Fuzzy C-Means Clustering Sistem Crowdfunding pada Sektor Industri Kreatif Berbasis Web,” J. Edukasi dan Penelit. Inform., vol. 6, no. 2, p. 193, 2020. http://dx.doi.org/10.26418/jp.v6i2.38018
M. A. Kasri and H. Jati, “Combination of K-Means and Simple Additive Weighting in Deciding Locations and Strategies of University Marketing,” Khazanah, vol. 6, pp. 132–141, 2020. https://doi.org/10.23917/khif.v6i2.11281
N. D. Arianti and S. Moedjiono, “DSS for scholarship recipients using the fuzzy C-means clustering integrated with the simple additive weighting method,” Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 2048–2050, 2019.
E. V. Manoppo, N. A. Laoh, I. Pemerintahan, D. Negeri, P. Provinsi, and S. Utara, “Strategi Pemanfaatan Data Terpadu Kesejahteraan Sosial (DTKS) dalam Penyaluran Bantuan Sosial RS-RTLH oleh Dinas Sosial Provinsi Sulawesi Utara,” vol. 4, no. April. pp. 25–39, 2022. https://doi.org/10.33701/jk.v4i1.2598
A. Amin, R. N. Sasongko, and A. Yuneti, “Kebijakan Kartu Indonesia Pintar untuk Memerdekakan Mahasiswa Kurang Mampu,” J. Adm. Educ. Manag., vol. 5, no. 1, pp. 98–107, 2022. https://doi.org/10.31539/alignment.v5i1.3803
R. T. Allopa and T. Pabendon, “Program Kartu Keluarga Sejahtera: Efektifkah? (Bukti Empiris di Kampung Mulia Kencana Kabupaten Mimika),” ESEOSI J. Econ. Reg. Sci., vol. 2, no. 2, pp. 139–155, 2022. https://doi.org/10.52421/jurnal-esensi.v2i2.223
A. K. Wardhani, “Implementasi Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Kajen Pekalongan,” J. Transform., vol. 14, no. 1, pp. 30–37, 2016.
C. Ais, A. Hamid, D. Candra, and R. Novitasari, “Analysis of Livestock Meat Production in Indonesia Using Fuzzy C-Means Clustering,” J. Ilmu Komput. dan Inf. (Journal Comput. Sci. Information), vol. 15, no. 1, pp. 1–8, 2022. https://doi.org/10.21609/jiki.v15i1.993
F. R. Hariri, “Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents,” J. Online Inform., vol. 6, no. 1, p. 79, 2021. https://doi.org/10.15575/join.v6i1.591
D. L. Rahakbauw, V. Y. I. Ilwaru, and M. H. Hahury, “Implementasi Fuzzy C-Means Clustering Dalam Penentuan Beasiswa,” J. Ilmu Mat. dan Terap., vol. 11, pp. 1–12, 2017.
Z. Li, S. Chen, L. Pei, J. Chu, and J. Song, “Teacher Allocation and Evaluation Based on Fuzzy C-Means Clustering,” Mob. Inf. Syst., vol. 2022, no. 4, pp. 1–9, 2022. https://doi.org/10.1155/2022/8465713
L. Wang and Y. Jiang, “Collocating Recommendation Method for E-Commerce Based on Fuzzy C-Means Clustering Algorithm,” J. Math., vol. 2022, pp. 1–11, 2022. https://doi.org/10.1155/2022/7414419
Y. Li, J. Gou, and Z. Fan, “Educational data mining for students performance based on fuzzy C‐means clustering,” J. Eng., vol. 2019, no. 11, pp. 8245–8250, 2019. https://doi.org/10.1049/joe.2019.0938
M. Wulandariyaningsih, A. N. Hasmi, and S. Pancahayani, “Sistem Pendukung Keputusan Penentuan Uang Kuliah Tunggal Mahasiswa Dengan Metode Fuzzy C-Means (Studi Kasus Institut Teknologi Kalimantan),” Teorema Teor. dan Ris. Mat., vol. 7, no. 1, p. 117, 2022. http://dx.doi.org/10.25157/teorema.v7i1.6701
M. A. Pamungkas, H. Oktavianto, and R. Umilasari, “Perbandingan Fuzzy C-Means Dan K-Means Untuk Mengelompokkan Tingkat Buta Huruf Berdasarkan Provinsi Di Indonesia,” 2021.
G. Setiadi and W. Hadikurniawati, “Implementasi Metode Hybrid AHP-SAW-TOPSIS Untuk Pemilihan Taman TOGA,” J. Inform., vol. 9, no. 1, pp. 18–25, 2022. https://doi.org/10.31294/inf.v9i1.11901
R. Fauzan, Y. Indrasary, and N. Muthia, “Sistem Pendukung Keputusan Penerimaan Beasiswa Bidik Misi di POLIBAN dengan Metode SAW Berbasis Web,” J. Online Inform., vol. 2, no. 2, p. 79, 2018. https://doi.org/10.15575/join.v2i2.101
R. Sovia, E. P. W. Mandala, and S. Mardhiah, “Algoritma K-Means dalam Pemilihan Siswa Berprestasi dan Metode SAW untuk Prediksi Penerima Beasiswa Berprestasi,” J. Edukasi dan Penelit. Inform., vol. 6, no. 2, p. 181, 2020. http://dx.doi.org/10.26418/jp.v6i2.37759
I. Septiana, M. Irfan, A. R. Atmadja, and B. Subaeki, “Sistem Pendukung Keputusan Penentu Dosen Penguji Dan Pembimbing Tugas Akhir Menggunakan Fuzzy Multiple Attribute Decision Making dengan Simple Additive Weighting (Studi Kasus: Jurusan Teknik Informatika UIN SGD Bandung),” J. Online Inform., vol. 1, no. 1, p. 43, 2016. https://doi.org/10.15575/join.v1i1.10
A. Muhariya, I. Riadi, and Y. Prayudi, “Cyberbullying Analysis on Instagram Using K-Means Clustering,” JUITA J. Inform., vol. 10, no. 2, p. 261, 2022. http://dx.doi.org/10.30595/juita.v10i2.14490
A. M. Fadhilah, M. I. Wahyuddin, and D. Hidayatullah, “Analisis Faktor yang Mempengaruhi Perokok Beralih ke Produk Alternatif Tembakau (VAPE) menggunakan Metode K-Means Clustering,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 5, no. 2, p. 219, 2020. https://doi.org/10.35870/jtik.v5i2.182
R. Setiawan, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru ( Studi Kasus : Politeknik Lp3i Jakarta ),” J. Lentera Ict, vol. 3, no. 1, pp. 76–92, 2016.
D. P. M and A. Fadlil, “Penerapan Clustering K-Means untuk Pengelompokan Tingkat Kepuasan Pengguna Lulusan Perguruan Tinggi,” vol. 6, pp. 1693–1700, 2022. http://dx.doi.org/10.30865/mib.v6i3.4191
Y. S. Siregar and P. Harliana, “Algoritma Fuzzy C-Means Pada Aplikasi Matlab Dalam Menentukan Dosen Pembimbing Tugas Akhir,” Semin. Nas. Unisla, pp. 213–217, 2018.
N. Noviyanto, “Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 183–188, 2020. https://doi.org/10.31294/p.v22i2.8808
Pelsri Ramadar Noor Saputra and A. Chusyairi, “Perbandingan Metode Clustering dalam Pengelompokan Data Puskesmas pada Cakupan Imunisasi Dasar Lengkap,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 6, pp. 5–12, 2020. https://doi.org/10.29207/resti.v4i6.2556
A. M. Anter and M. Ali, “Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems,” Soft Comput., vol. 24, no. 3, pp. 1565–1584, 2020. https://doi.org/10.1007/s00500-019-03988-3