FIDIA UTAMI, NPM 2109100032 (2025) IMPLEMENTASI ALGORITMA NAIVE BAYES DALAM PREDIKSI KELULUSAN MAHASISWA BERBASIS KOMPETENSI AKADEMIK. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Prediksi kelulusan mahasiswa merupakan salah satu isu penting dalam dunia pendidikan tinggi, terutama untuk membantu institusi dalam meningkatkan kualitas akademik dan memberikan intervensi lebih dini bagi mahasiswa yang berisiko tidak lulus. Penelitian ini bertujuan untuk mengimplementasikan algoritma Naïve Bayes dalam memprediksi kelulusan mahasiswa berbasis kompetensi akademik dengan mempertimbangkan variabel Indeks Prestasi Kumulatif (IPK), tingkat kehadiran, dan jenis kelamin. Data penelitian diperoleh dari mahasiswa Universitas Labuhanbatu pada Program Studi Sistem Informasi, Agroteknologi, Manajemen Informatika, dan Teknologi Informasi. Proses analisis dilakukan menggunakan perangkat lunak RapidMiner dengan tahapan meliputi pra-pemrosesan data, pembentukan model, hingga evaluasi menggunakan confusion matrix untuk mengukur akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mampu memberikan tingkat akurasi prediksi yang tinggi, dengan nilai akurasi tertinggi pada Program Studi Manajemen Informatika sebesar 95,2%, diikuti oleh Agroteknologi 91,6%, Sistem Informasi 87,5%, dan Teknologi Informasi 85,7%. Faktor IPK dan kehadiran terbukti menjadi indikator utama dalam menentukan status kelulusan, sedangkan variabel jenis kelamin memiliki pengaruh minimal. Dengan demikian, model prediksi berbasis Naïve Bayes dapat dijadikan sebagai alat bantu evaluasi akademik untuk mengidentifikasi mahasiswa berisiko tidak lulus sejak dini, sehingga institusi pendidikan dapat memberikan pembinaan akademik yang lebih tepat sasaran. Penelitian ini juga membuka peluang untuk pengembangan lebih lanjut dengan menambahkan variabel non-akademik serta mengombinasikan dengan algoritma lain guna meningkatkan performa prediksi. Kata kunci: Prediksi kelulusan, Naïve Bayes, kompetensi akademik, RapidMiner ======================================================================================= Student graduation prediction is an essential issue in higher education, particularly to assist institutions in improving academic quality and providing early intervention for students at risk of not graduating. This study aims to implement the Naïve Bayes algorithm in predicting student graduation based on academic competencies, considering variables such as Grade Point Average (GPA), attendance rate, and gender. The research data were obtained from students of Universitas Labuhanbatu in the Information Systems, Agrotechnology, Informatics Management, and Information Technology study programs. The analysis process was carried out using RapidMiner software through several stages, including data preprocessing, model construction, and evaluation using a confusion matrix to measure accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes algorithm provides a high prediction accuracy, with the highest accuracy achieved in the Informatics Management program at 95.2%, followed by Agrotechnology at 91.6%, Information Systems at 87.5%, and Information Technology at 85.7%. GPA and attendance were found to be the main indicators in determining graduation status, while gender had minimal influence. Therefore, the Naïve Bayes-based prediction model can serve as a valuable academic evaluation tool to identify students at risk of not graduating early, enabling institutions to provide more targeted academic support. This research also opens opportunities for further development by integrating non-academic variables and combining with other algorithms to enhance prediction performance. Keywords: Graduation prediction, Naïve Bayes, academic competence, RapidMiner.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Prediksi kelulusan, Naïve Bayes, kompetensi akademik, RapidMiner=============Graduation prediction, Naïve Bayes, academic competence, RapidMiner. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 25 Nov 2025 04:27 |
| Last Modified: | 25 Nov 2025 04:27 |
| URI: | http://repository.ulb.ac.id/id/eprint/1998 |
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