Supriadi, Fajar
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Penerapan Metode Dbscan Dalam Penentuan Mahasiswa Yang Layak Memperoleh Bidikmisi Supriadi, Fajar
Management of Information System Journal Vol 3 No 2: Maret 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v3i2.1943

Abstract

- Bidikmisi is a government-funded educational assistance programme aimed at prospective students from economically disadvantaged families who demonstrate academic potential. This assistance is distributed through higher education institutions under the Ministry of Research, Technology, and Higher Education (Ristek Dikti). The selection of Bidikmisi applicants is conducted during the admission process for new students by higher education institutions designated by Ristek Dikti. The distribution of Bidikmisi must also consider specific criteria before being awarded to eligible students who have passed the administrative selection and met other evaluation components as requirements for receiving the scholarship. In making decisions to determine which students are eligible to receive Bidikmisi, the process sometimes takes a considerable amount of time, and at times, the Bidikmisi program provided is perceived as not being targeted appropriately. The objective of this study is to develop an information system based on data mining analysis that can be used by the academic department as a recommendation for making quick decisions so that the Bidikmisi program can be distributed on time and targeted appropriately in the Bidikmisi selection process. The clustering technique is used to group records in a database based on specific criteria. The clustering results are provided to end users to give an overview of what is happening in the database. One method that can be used in clustering is the DBSCAN method. The DBSCAN method works by growing areas with high density into clusters and finding clusters in random forms in a spatial database containing noise. DBSCAN defines a cluster as the maximum set of density-connected points.