THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM

  • Asri Mulyani Teknik Informatika, Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Dede Kurniadi Teknik Informatika, Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Muhammad Rikza Nashrulloh Sistem Informasi, Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Indri Tri Julianto Sistem Informasi, Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Meta Regita Teknik Informatika, Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
Keywords: KIP-Kuliah, Naive Bayes, PPA, Prediction, Scholarship

Abstract

The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.

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References

D. Kurniadi, E. Abdurachman, H. L. H. S. Warnars, and W. Suparta, “The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, p. 012039, 2018, doi: 10.1088/1757-899X/434/1/012039.

M. N. Sutoyo, “Implementasi Metode MADM Model Yager untuk Seleksi Penerima Beasiswa PPA,” JUITA J. Inform., vol. 5, no. 2, p. 81, 2018, doi: 10.30595/juita.v5i2.1630.

M. B. Kurniawan, “Sistem Pendukung Pengambilan Keputusan Pemberian Beasiswa Dengan Menggunakan Metode AHP.” Universitas Sanata Dharma, Yogyakarta, pp. 1–67, 2019.

H. Sulistiani, “Penerapan Algoritma Klasifikasi Sebagai Pendukung Keputusan Pemberian Beasiswa Mahasiswa,” pp. 300–305, 2018, doi: 10.31227/osf.io/yuavj.

F. A. Harimurti and E. Riksakomara, “Klasifikasi Penerimaan Beasiswa Menggunakan Metode Naïve Bayes Classifier (Studi Kasus Universitas Trunojoyo Madura),” 2017.

A. Alwi and Munirah, “The Concept Of Naive Bayes And Its Simple Use For Prediction Final Score Of Student Examination Using R Language,” JUTIF, vol. 3, no. 1, pp. 133–140, 2022.

A. Merdekawati, “Sistem Pendukung Keputusan Penerimaan Beasiswa Menggunakan Algoritma C4.5 (Studi Kasus : Baitul Maal),” J. Pendidik. Teknol. dan Kejuru., vol. 15, no. 1, pp. 113–123, 2018, doi: 10.23887/jptk-undiksha.v15i1.13067.

H. S. A. K. Muhammad Fikri Khaid, Sujalwo, “Model Logika Fuzzy Sugeno Berbasis Web Untuk Seleksi Penerima Beasiswa,” J. Ilm. SINUS, vol. 16, no. 1, p. 25, 2018, doi: 10.30646/sinus.v16i1.329.

A. Pujianto, K. Kusrini, and A. Sunyoto, “Perancangan Sistem Pendukung Keputusan Untuk Prediksi Penerima Beasiswa Menggunakan Metode Neural Network Backpropagation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 2, p. 157, 2018, doi: 10.25126/jtiik.201852631.

D. Kurniadi, A. Mulyani, Y. Septiana, and I. M. Yusuf, “Prediction of courses score using Artificial Neural Network with Backpropagation algorithm,” in IOP Conference Series: Materials Science and Engineering, Mar. 2021, vol. 1098, no. 3, p. 032110, doi: 10.1088/1757-899X/1098/3/032110.

D. Kurniadi, H. Leslie, H. Spits, and W. Suparta, “Predicting Student Performance with Multi-Level Representation in an Intelligent Academic Recommender System using Backpropagation Neural Network,” ICIC Express Lett. Part B Appl., vol. 12, no. 10, pp. 883–890, 2021, doi: 10.24507/icicelb.12.10.883.

P. R. Sihombing and A. M. Arsani, “Comparison of Machine Learning Methods in Classifying Poverty in Indonesia in 2018,” J. Tek. Inform., vol. 2, no. 1, pp. 51–56, 2021, doi: 10.20884/1.jutif.2021.2.1.52.

Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.

E. Manalu, F. A. Sianturi, and M. R. Manalu, “Penerapan Algoritma Naive Bayes Untuk Memprediksi Jumlah Produksi Barang Berdasarkan Data Persediaan Dan Jumlah Pemesanan Pada Cv. Papadan Mama Pastries,” J. Mantik Penusa, vol. 1, no. 2, 2017.

T. H. Apandi and C. A. Sugianto, “Algoritma Naive Bayes untuk Prediksi Kepuasan Pelayanan Perekaman e-KTP ( Naive Bayes Algorithm for Satisfaction Prediction of e-ID,” JUITA (Jurnal Inform. UMP, vol. 7, no. November, pp. 125–128, 2019.

M. F. Fibrianda and A. Bhawiyuga, “Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 9, pp. 3112–3123, 2018.

S. Rizal, P. Studi, T. Informatika, and U. Yudharta, “Penerapan Algoritma Naïve Bayes Untuk Prediksi Penerimaan Siswa Baru Di Smk Al-Amien Wonorejo,” Explor. IT J. Keilmuan dan Apl. Tek. Inform., vol. 10, no. 1, pp. 14–21, 2018, doi: 10.35891/explorit.v10i1.1671.

S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, KNN Dan SVM,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

Published
2022-08-20
How to Cite
[1]
A. Mulyani, D. Kurniadi, M. R. Nashrulloh, I. T. Julianto, and M. Regita, “THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM”, J. Tek. Inform. (JUTIF), vol. 3, no. 4, pp. 821-827, Aug. 2022.