Penentuan Penerima Beasiswa PPA Usulan dengan Menerapkan K-Means Clustering dan Multi Attribute Utility Theory Method
-
Mesran Mesran
*
Universitas Budi Darma, Medan,
Indonesia
https://orcid.org/0000-0002-8886-2013
(*) Corresponding Author
Abstract
The government launched the Proposed Academic Achievement Scholarship (PPA) Program to help outstanding students. This scholarship is open to diploma and undergraduate students throughout Indonesia, both at state and private universities. Recommendations for this scholarship can be submitted by various parties, such as universities, government, NGOs, or organizations/companies. Apart from that, this program still has several shortcomings in its distribution, such as the lack of accurate data regarding the economic conditions and academic achievements of students which hinders the process of determining scholarship recipients who are on target, the large number of applicants and the manual process used takes a long time and has the potential to cause errors. This causes delays in distributing scholarships to recipients, in addition to the lack of clear and measurable criteria in the assessment process opening up opportunities for nepotism and favoritism, so that scholarship recipients are not always the most capable. To overcome this problem, data mining techniques are used, namely K-Means Clustering and decision making using the MAUT method. The reason this research combines the two methods is because K-Means groups students with similar GPA values, so that less data is selected using MAUT. There are 5 criteria used to select scholarship recipients including GPA, parents' income, academic achievement, non-academic achievement and ethics. This research sample took 150 students. Data was collected using K-Means Clustering into two clusters with the final centroid value of Cluster_0: 3,636 (79 data) and Cluster_1: 2,897 (71 data). Cluster_0 was chosen for the next process because it has a higher centroid value. As a result of selection using the MAUT method, 15 students were declared entitled to receive scholarships. The student with the highest final score was Monalisa Marbun (0.583) and the lowest (15th place) was Jonathan Panca S P Gultom (0.386).
Downloads
References
Alfansyah, I., Sibagariang, J., Fadillah, R., & Assarani, D. (2023). Sistem Pendukung Keputusan Seleksi Dosen Non Komputer Terbaik Menerapkan Metode SAW. Journal of Decision Support System Research, 1(1), 30–36.
Ardinsah, A., Mesran, M., & Triayudi, A. (2023). Sistem Pendukung Keputusan Menentukan Aplikasi Chat Terbaik Menggunakan Metode OCRA Dengan Pembobotan ROC. Journal of Computer System and Informatics (JoSYC), 4(4), 891–898.
Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes. Jurnal Media Informatika Budidarma, 5(2), 640. https://doi.org/10.30865/mib.v5i2.2937
Bahruddin, M. H. (2019). Sistem Pendukung Pengambil Keputusan Penerima Beasiswa LAZISMU dengan Metode MAUT. 125–132.
Dewasasmita, E. Y., & Hendry, H. (2023). Perbandingan Metode SAW, MAUT, ORESTE, TOPSIS dalam Pendukung Keputusan Pembangunan Supermarket di Kabupaten Pati. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 7(2), 555–569.
Hafizah, A. S. L. T. T. H. (2022). Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), Vol 1, No 6 (2022): EDISI NOVEMBER 2022, 888–897.
Harli Trimulya Suandi As, B., & Zahrotun, L. (2021). PENERAPAN DATA MINING DALAM MENGELOMPOKKAN DATA RIWAYAT AKADEMIK SEBELUM KULIAH DAN DATA KELULUSAN MAHASISWA MENGGUNAKAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING (Implementation Of Data Mining In Grouping Academic History Data Before Students And Stud. Jurnal Teknologi Informasi, Komputer Dan Aplikasinya, 3(1), 62–71.
Lubis, M. Z., Fadillah, R., & Lubis, R. M. F. (2023). Decision Support System for Determining New Branch Locations Applying the Multi Attribute Utility Theory (MAUT) Method. International Journal of Informatics and Data Science, 1(1), 36–45.
Mareti, G. T., & Ayunda, A. T. (2023). Komparasi Metode Maut dan Moora dalam Pemilihan Sunscreen untuk Kulit Menggunakan Pembobotan ROC. Building of Informatics, Technology and Science (BITS), 5(2), 553–564.
Marpaung, P., Pebrian, I., & Putri, W. (2023). Penerapan Data Mining Untuk Pengelompokan Kepadatan Penduduk Kabupaten Deli Serdang Menggunakan Algoritma K-Means. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 6(2), 64–70.
Masjunedi, M., Suarna, N., & Wijaya, Y. A. (2023). Analisa Penerapan Metode Clustering K-Means Untuk Pengelompokan Data Transaksi Konsumen. JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1322–1328.
Maulidiya, H., & Jananto, A. (2020). Asosiasi Data Mining Menggunakan Algoritma Apriori dan FP-Growth sebagai Dasar Pertimbangan Penentuan Paket Sembako. Proceeding SENDIU 2020, 6, 36–42.
Mesran, M., Harahap, A., & Nugroho, F. (2023). Sistem Pendukung Keputusan Pemilihan Aplikasi Chat Terbaik Dalam Mendukung Pembelajaran Daring di Masa Pandemi Covid Menggunakan Metode Multi Attribute Utility Theory. Jurnal Sistem Komputer Dan Informatika (JSON), 4(3), 474–484.
Mesran, M., Triayudi, A., Nofrisa, D., & Fadillah, R. (2023). Penerapan Metode EXPROM II Dalam Menentukan Tempat Wisata Pantai Terbaik. Jurnal Sistem Komputer Dan Informatika (JSON), 5(2), 337–346.
Murti, W. K., Triayudi, A., & Mesran, M. (2023). Penentuan Mahasiswa Berprestasi dengan Menerapkan Metode Multi Attribute Utility Theory (MAUT). Jurnal Sistem Komputer Dan Informatika (JSON), 5(1), 122–130.
Nainel, Y. L., Buulolo, E., & Lubis, I. (2020). Penerapan Data Mining Untuk Estimasi Penjualan Obat Berdasarkan Pengaruh Brand Image Dengan Algoritma Expectation Maximization (Studi Kasus: PT. Pyridam Farma Tbk). JURIKOM (Jurnal Riset Komputer), 7(2), 214. https://doi.org/10.30865/jurikom.v7i2.2097
Nasution, A. A., Aldisa, R. T., Mesran, M., & Fadillah, R. (2024). Penerapan Metode Multi Objective Optimization on The Basis of Ratio Analysis (MOORA) dalam Penentuan Pembimbing Skripsi Terbaik. Journal of Information System Research (JOSH), 5(2), 614–620.
Nurzaman, M. Y. (2023). Implementasi K-Means Clustering Dalam Pengelompokkan Banyaknya Jumlah Petani Berdasarkan Kecamatan Di Provinsi Jawa Barat. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 10(3), 131–144.
Okmarizal, B., & Defit, S. (2023). Implementasi Metode AHP Dan Maut untuk Rekomendasi Produk Tupperware Terlaris. Jurnal KomtekInfo, 109–115.
Oktaria, I. (2023). Kombinasi Metode Multi-Attribute Utility Theory (MAUT) dan Rank Order Centroid (ROC) dalam Pemilihan Kegiatan Ekstrakulikuler. Jurnal Ilmiah Informatika Dan Ilmu Komputer (JIMA-ILKOM), 2(1), 1–11.
Rofiq, M. A., Qoiriah, A., Kom, S., & Kom, M. (2021). Pengelompokan Kategori Buku Berdasarkan Judul Menggunakan Algoritma Agglomerative Hierarchical Clustering Dan K-Medoids. Journal of Informatics and Computer Science (JINACS), 2(03), 220–227.
Sovia, R., Praja, E., Mandala, W., & Mardhiah, S. (2020). Algoritma K-Means dalam Pemilihan Siswa Berprestasi dan Metode SAW untuk Prediksi. 6(2), 181–187.
Sukamto, S., & Fitriansyah, A. (n.d.). Application of the MAUT Method to Determine Eligibility for Accredited School Libraries. CESS (Journal of Computer Engineering, System and Science), 8(2), 384–392.
Usanto Usanto. (2023). Penerapan Data Mining Dengan Mengimplementasikan Algoritma K-Means Dalam Proses Clustering Untuk Pengelompokan Mahasiswa Calon Penerima Beasiswa KIP. Building of Informatics, Technology and Science (BITS), 5(1). https://doi.org/10.47065/bits.v5i1.3411
Widaningsih, S. (2019). Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm. Jurnal Tekno Insentif, 13(1), 16–25. https://doi.org/10.36787/jti.v13i1.78
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penentuan Penerima Beasiswa PPA Usulan dengan Menerapkan K-Means Clustering dan Multi Attribute Utility Theory Method
Pages: 232-244
Copyright (c) 2024 Mesran Mesran
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
