COMPARISON OF DECISION TREE AND NAÏVE BAYES ALGORITHMS IN CLASSIFICATION MODELS TO DETERMINE LECTURER PERFORMANCE USING K FOLD CROSS VALIDATION
(1) Informatics Study Program, Institut Sains & Teknologi Akprind, Yogyakarta
(2) Informatics Study Program, Institut Sains & Teknologi Akprind, Yogyakarta
(3) Informatics Study Program, Institut Sains & Teknologi Akprind, Yogyakarta
(4) Digital Business Study Program, Institut Sains & Teknologi Akprind, Yogyakarta
Corresponding Author
Abstract
Lecturer performance is very important to support the progress of higher education. Determination of lecturer performance is based on Tri Dharma activities, including: teaching, research and community service. This study aims to build a model that can predict the predicate of lecturers from the activities carried out. The best model is obtained by comparing the use of two algorithms, namely Decision Tree and Naive Bayes. Data mining methods use the CRISP-DM method, namely business understanding, data understanding, data preparation, modeling, evaluation, and development. Performance testing of training data using K Fold Cross Validation. The modeling results with this performance show that the Decision Tree algorithm has better performance with 94.70%, accuracy, 93.24% precision and 96.33% recall, while Naïve Bayes algorithm has performance with 92.95%, accuracy 90.08% and 96.33%. This shows that modeling using the Decision Tree algorithm can be used as a model in determining lecturer performance.
Keywords
References
J. Han, M. Kamber, and J. Pei, “Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems),” 2022.
D. Xhemali, C. J. Hinde, and R. G. Stone, “Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages,” IJCSI International Journal of Computer Science Issues, vol. 4, no. 1, 2009.
Government of Republic Indonesia, “PERATURAN PEMERINTAH REPUBLIK INDONESIA,” 2009. Accessed: Aug. 14, 2022. [Online]. Available: https://peraturan.bpk.go.id/Home/Details/4956/pp-no-37-tahun-2009
M. Bilal, H. Israr, M. Shahid, and A. Khan, “Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques,” Journal of King Saud University - Computer and Information Sciences, vol. 28, no. 3, pp. 330–344, Jul. 2016, doi: 10.1016/j.jksuci.2015.11.003.
V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” in Procedia Computer Science, 2019, vol. 161, pp. 765–772. doi: 10.1016/j.procs.2019.11.181.
U. Pujianto, A. L. Setiawan, H. A. Rosyid, and A. M. M. Salah, “Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement,” Knowledge Engineering and Data Science, vol. 2, no. 2, p. 58, Dec. 2019, doi: 10.17977/um018v2i22019p58-71.
E. M. M. van der Heide, R. F. Veerkamp, M. L. van Pelt, C. Kamphuis, I. Athanasiadis, and B. J. Ducro, “Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle,” Journal of Dairy Science, vol. 102, no. 10, pp. 9409–9421, Oct. 2019, doi: 10.3168/jds.2019-16295.
A. Ashari and A. M. Tjoa, “Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool,” 2013. [Online]. Available: www.ijacsa.thesai.org
S. Rahmadani, A. Dongoran, M. Zarlis, and Zakarias, “Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem,” in Journal of Physics: Conference Series, Mar. 2018, vol. 978, no. 1. doi: 10.1088/1742-6596/978/1/012087.
A. Setyanto dan Hanif Al Fattah, “Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas,” 2017.
K. Yadav and R. Thareja, “Comparing the Performance of Naive Bayes And Decision Tree Classification Using R,” International Journal of Intelligent Systems and Applications, vol. 11, no. 12, pp. 11–19, Dec. 2019, doi: 10.5815/ijisa.2019.12.02.
S. Farhana, “Classification of Academic Performance for University Research Evaluation by Implementing Modified Naive Bayes Algorithm,” in Procedia Computer Science, 2021, vol. 194, pp. 224–228. doi: 10.1016/j.procs.2021.10.077.
R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 4, p. 646, Dec. 2021, doi: 10.32493/informatika.v5i4.7622.
T. Rosandy, “Perbandingan Metode Naive Bayes Classifier Dengan Metode Decision Tree (C4.5) Untuk Menganalisa Kelancaran Pembiayaan (Study Kasus : Kspps / Bmt Al-Fadhila),” vol. 02, 2016.
C. C. Aggarwal, Data Mining. Cham: Springer International Publishing, 2015. doi: 10.1007/978-3-319-14142-8.
R. Adrian, M. A. J. S. Perdana, A. Asroni, and S. Riyadi, “Applying the Naive Bayes Algorithm to Predict the Student Final Grade,” Emerging Information Science and Technology, vol. 1, no. 2, 2020, doi: 10.18196/eist.127.
Y. Jung, “Multiple predicting K-fold cross-validation for model selection,” Journal of Nonparametric Statistics, vol. 30, no. 1, pp. 197–215, Jan. 2018, doi: 10.1080/10485252.2017.1404598.
A. S. Nejad and R. Tavoli, “A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining,” Journal of Electrical and Computer Engineering Innovations, vol. 6, no. 1, pp. 33–42, 2018, doi: 10.22061/JECEI.2018.811.
S. Riyadi, Y. Lestari, C. Damarjati, and K. H. Ghazali, “Performance Comparison of Deep Learning Models to Detect Covid-19 Based on X-Ray Images,” 2022.
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DOI: 10.56327/jurnaltam.v14i2.1604
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