A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students
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Published:
Apr 11, 2020
Keywords:
Data Mining, Naïve Bayes, K-Means Clustering, New Student
Main Article Content
Abstract
The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. so that there is an accumulation of student data continuously. The purpose of this study is to compare the K-Means Clustering Algorithm and Naïve Bayes on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is Knowledge Discovery in Database (KDD). The tools used are Rapid Miner. The attributes used are national examination score, school origin, and study programs. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used the K-Means Clustering Algorithm to produce 3 clusters, while the Naïve Bayes results resulted in an accuracy value of 9.08%.
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[1]
“A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students”, intelektualita, vol. 9, no. 1, pp. 51–62, Apr. 2020, doi: 10.19109/intelektualita.v9i1.5574.
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How to Cite
[1]
“A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students”, intelektualita, vol. 9, no. 1, pp. 51–62, Apr. 2020, doi: 10.19109/intelektualita.v9i1.5574.
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Joko Suntoro. Data Mining Algoritma Dan Implementasi Dengan Pemrograman PHP. Jakarta, 2019.
Khotimah, Tutik. ‘PENGELOMPOKAN SURAT DALAM AL QUR�AN MENGGUNAKAN ALGORITMA K-MEANS’. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer 5, no. 1 (2014): 83–88. https://doi.org/10.24176/simet.v5i1.141.
Mujawar, Sairabi, and H. P. R. Devale. ‘Prediction of Hear t Disease Using Modified K-Means and by Using Naive Bayes’. International Journal of Innovative Research in Computer and Communication Engineering 3, no. 11 (2015): 0396–0400. https://doi.org/10.15680/IJIRCCE.2015.
Rahayu, Flourensia Sapty, Rangga Deputra Ginantaka, and Y Sigit Purnomo Wp. ‘Analisis Manfaat Sistem Informasi Penerimaan Mahasiswa Baru Dengan Metode IT Balanced Scorecard’, no. January 2019 (2017). https://doi.org/10.21460/jutei.2017.12.21.
Soni, Jyoti, Ujma Ansari, Dipesh Sharma, and Sunita Soni. ‘Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction’. International Journal of Computer Applications 17, no. 8 (2011): 43–48. https://doi.org/10.5120/2237-2860.
Sundar, P.V.Praveen. ‘A COMPARATIVE STUDY FOR PREDICTING STUDENT’S ACADEMIC PERFORMANCE USING BAYESIAN NETWORK CLASSIFIERS’. IOSR Journal of Engineering 03, no. 02 (2013): 37–42. https://doi.org/10.9790/3021-03213742.
Sutabri, Tata. ‘Improving Naïve Bayes in Sentiment Analysis For Hotel Industry in Indonesia’. 2018 Third International Conference on Informatics and Computing (ICIC), 2016, 1–6.
Suyanto. Data Mining Untuk Klasifikasi Dan Klasterisasi Data. SpringerReference, 2017. https://doi.org/10.1007/SpringerReference_5414.
Vulandari, Tri Retno. ‘Pengertian Data Mining’. In Data Mining, Teori Dan Aplikasi Rapirminer, 1, 2017.
Yathongchai, Wilairat, Chusak Yathongchai, Kittisak Kerdprasop, and Nittaya Kerdprasop. ‘Factor Analysis with Data Mining Technique in Higher Educational Student Drop Out’. Latest Advances in Educational Technologies, 2012, 111–16.