| dc.contributor.advisor | Annisa | |
| dc.contributor.author | Soesetiodiadi, Nadira Arbanisa | |
| dc.date.accessioned | 2025-01-09T23:50:14Z | |
| dc.date.available | 2025-01-09T23:50:14Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/160631 | |
| dc.description.abstract | Pengelolaan data mahasiswa berbasis talent mapping, biodata, dan nilai akademik menjadi penting untuk memahami potensi dan karakteristik individu. Dalam studi ini, metode association rule mining diterapkan untuk menganalisis hubungan antar atribut pada data mahasiswa Departemen Ilmu Komputer IPB University Tahun Ajaran 2020 dan 2021. Data yang digunakan mencakup biodata, hasil talent mapping, dan nilai mata kuliah. Algoritma Apriori diterapkan untuk mengidentifikasi pola yang relevan, menghasilkan aturan-aturan (rules) sebagai dasar pembentukan profil mahasiswa. Profil ini dirancang untuk membantu pengelompokan mahasiswa baru ke dalam divisi seperti Computational Intelligence and Optimization (CIO), Computer Systems and Network (CSN), dan Software Engineering and Information Systems (SEIS). Penelitian ini menghasilkan aturan asosiasi yang dapat mencocokkan potensi mahasiswa dengan divisi yang sesuai, meskipun pengembangan lanjutan diperlukan untuk meningkatkan kualitas hasil. | |
| dc.description.abstract | The management of student data based on talent mapping, biodata, and academic scores is essential for understanding individual potential and characteristics. In this study, the association rule mining method was applied to analyze the relationships between attributes in the student data of the Department of Computer Science at IPB University for the 2020 and 2021 academic years. The data used includes biodata, talent mapping results, and course grades. The Apriori algorithm was employed to identify relevant patterns, generating rules as the foundation for creating student profiles. These profiles are designed to assist in grouping new students into divisions such as Computational Intelligence and Optimization (CIO), Computer Systems and Network (CSN), and Software Engineering and Information Systems (SEIS). This research produced association rules that can match students' potential with appropriate divisions, although further development is needed to improve the quality of the results. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title |  Pencarian Aturan Asosiasi Profil Mahasiswa menggunakan Data Akademik dan Talent Mapping | id |
| dc.title.alternative |  Association Rule Mining for Student Profiling Using Academic Data and Talent Mapping | |
| dc.type | Skripsi | |
| dc.subject.keyword | association rule mining | id |
| dc.subject.keyword | biodata | id |
| dc.subject.keyword | divisi | id |
| dc.subject.keyword | nilai akademik | id |
| dc.subject.keyword | profil mahasiswa | id |
| dc.subject.keyword | talent mapping | id |
