DETERMINATION OF BANK INDONESIA SCHOLARSHIP RECIPIENTS USING NAÏVE BAYES CLASSIFIER
A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.
A. Hadianto, B. Setya Rintyarna, and L. Ali Muharom, “Klasifikasi Mahasiswa Penerima Beasiswa Dengan Metode Naive Bayes,” 2009.
Asnimar, A. Achmad, Yuyun, A. Iskandar, and Mansyur, “Classification Of Prospective Scholarship Recipients Kartu Indonesia Pintar (KIP) With Decision Tree Algorithm And Naïve Bayes,” Inspir. J. Teknol. Inf. dan Komun., vol. 12, no. 2, pp. 118–129, 2022, doi: 10.35585/inspir.v12i2.6.
H. Kurniawan, I. R. Munthe, and B. Bangun, “Analysis of Naive Bayes Methods on Scholarship Admissions at Smks Al-Wasliyah 2 Merbau,” J. Mantik, vol. 6, no. 1, pp. 364–373, 2022.
R. Hegde, V. Anusha G, S. Madival, S. Sowjanya H, and U. Sushma, “A Review on Data Mining and Machine Learning Methods for Student Scholarship Prediction,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. ICCMC, pp. 923–927, 2021, doi: 10.1109/ICCMC51019.2021.9418376.
Ismuato’illah, “Pengaruh Pemberian Beasiswa Bank Indonesia Terhadap Motivasi Berprestasi Mahasiswa Ditinjau Dari Perspektif Ekonomi Islam,” Skripsi, 2020.
M. I. A. Putera and M. G. L. Putra, “Rancang Bangun Sistem Pendukung Keputusan Penentuan Calon Penerima Beasiswa Menggunakan Metode Simple Additive Weighting Pada Kpw Bank Indonesia Balikpapan,” J. Ilm. Tek. Inf., vol. 14, no. 2, pp. 110–120, 2020, [Online]. Available: https://ejournal.unisbablitar.ac.id/index.php/antivirus/article/view/1213/896
S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Syst., vol. 192, p. 105361, 2020, doi: 10.1016/j.knosys.2019.105361.
S. Dey, S. Wasif, D. S. Tonmoy, S. Sultana, J. Sarkar, and M. Dey, “A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews,” 2020 Int. Conf. Contemp. Comput. Appl. IC3A 2020, no. May, pp. 217–220, 2020, doi: 10.1109/IC3A48958.2020.233300.
A. Harun and D. Putri Ananda, “Analysis of Public Opinion Sentiment About Covid-19 Vaccination in Indonesia Using Naïve Bayes and Decission Tree,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 58–64, 2021, doi: 10.57152/malcom.v1i1.63.
É. R. Santana, L. Lopes, and R. M. de Moraes, “Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis,” J. Voice, no. November, 2022, doi: 10.1016/j.jvoice.2022.10.001.
A. Ali, A. Khairan, F. Tempola, and A. Fuad, “Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City,” E3S Web Conf., vol. 328, p. 04011, 2021, doi: 10.1051/e3sconf/202132804011.
D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono, “Discretization methods for bayesian networks in the case of the earthquake,” Bull. Electr. Eng. Informatics, vol. 10, no. 1, pp. 299–307, 2021, doi: 10.11591/eei.v10i1.2007.
D. P. Sari, M. Rosha, and D. Rosadi, “Disaster Mitigation Efforts Using K-Medoids Algorithm and Bayesian Network,” EKSAKTA Berk. Ilm. Bid. MIPA, vol. 23, no. 03, pp. 231–241, 2022, doi: 10.24036/eksakta/vol23-iss03/304.
D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono, “Application of Bayesian Network Model in Determining the Risk of Building Damage Caused by Earthquakes,” Int. Conf. Inf. Commun. Technol. ICOIACT, pp. 131–135, 2018, doi: 10.1109/ICOIACT.2018.8350776.
P. M. Singh and M. E. Languages, Probability & Statistics. 2020.
D. P. Bertsekas and J. N. Tsitsiklis, An Introduction to Probability. 2011. doi: 10.1002/9781444390155.ch5.
B. E. Putro and T. Saepurohman, “A Classification Approach to Predicting Beef Knuckle Quality using the Decision Tree and Naïves Bayes Method: Case Study: Tiga Bersaudara Factory,” 2020 IEEE 7th Int. Conf. Ind. Eng. Appl. ICIEA 2020, pp. 779–783, 2020, doi: 10.1109/ICIEA49774.2020.9102019.
K. Komahan, “Naïve Bayes versus Support Vector Machines: A comparison of two algorithmic approaches for estimating cause of death from free-text,” 2019.
A. P. Dawid, “Conditional Independence in Statistical Theory,” J. R. Stat. Soc. Ser. B, vol. 41, no. 1, pp. 1–15, 1979, doi: 10.1111/j.2517-6161.1979.tb01052.x.
M. R. Romadhon and F. Kurniawan, “A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 41–44, 2021, doi: 10.1109/EIConCIT50028.2021.9431845.
N. Salmi and Z. Rustam, “Naïve Bayes Classifier Models for Predicting the Colon Cancer,” IOP Conf. Ser. Mater. Sci. Eng., vol. 546, no. 5, 2019, doi: 10.1088/1757-899X/546/5/052068.
M. J. Sánchez-Franco, A. Navarro-García, and F. J. Rondán-Cataluña, “A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services,” J. Bus. Res., vol. 101, no. December, pp. 499–506, 2019, doi: 10.1016/j.jbusres.2018.12.051.
T. A. Shaikh and R. Ali, “A CAD Tool for Breast Cancer Prediction using Naive Bayes Classifier,” 2020 Int. Conf. Emerg. Smart Comput. Informatics, ESCI 2020, pp. 351–356, 2020, doi: 10.1109/ESCI48226.2020.9167568.
M. Umar, “SENTIMENT ANALYSIS OF STUDENTS’ OPINION ON PROGRAMMING ASSESSMENT USING NAÏVE BAYES ALGORITHM ON SMALL DATA,” Prog. Retin. Eye Res., vol. 561, no. 3, pp. S2–S3, 2019.
K. Yadav and R. Thareja, “Comparing the Performance of Naive Bayes And Decision Tree Classification Using R,” Int. J. Intell. Syst. Appl., vol. 11, no. 12, pp. 11–19, 2019, doi: 10.5815/ijisa.2019.12.02.
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