Timeliness Of Spp Payments At Smk Tritech Infomatika Using Naive Bayes Algorithm


  • Muhammad Fakhri Politeknik Negeri Medan
  • Rial Beimar Volado Sibuea Politeknik Negeri Medan
  • Tri Krisandi Silalahi Politeknik Negeri Medan




Rapid Miner, SMK Tritech Informatika, Naive Bayes Method


SMK Tritech Informatika is one of the vocational schools located on Jalan Bhayangkara Medan. Smk Tritech plays an important role in education. However, many schools are late in financing their operations. So there are many problems related to school operational payments such as tuition payments. SPP payment is an important problem at Smk Tritech Infomatika because many of the students are late in paying SPP even though the payment deadline has been set. Therefore, it is necessary to evaluate the SPP payment. To overcome this problem, it is necessary to detect the factors that cause late tuition payments using data mining. The data mining technique used is classification with the Naive Bayes algorithm method.


Download data is not yet available.


I. M. Talha, I. Salehin, S. C. Debnath, M. Saifuzzaman, N. N. Moon, and F. N. Nur, “Human behaviour impact to use of smartphones with the python implementation using naive Bayesian,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2020, pp. 1–6.

M. Tabash, M. Abd Allah, and B. Tawfik, “Intrusion detection model using naive bayes and deep learning technique.,” Int. Arab J. Inf. Technol., vol. 17, no. 2, pp. 215–224, 2020.

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.

A. R. Lubis, M. K. M. Nasution, O. S. Sitompul, and E. M. Zamzami, “The effect of the TF-IDF algorithm in times series in forecasting word on social media,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, p. 976, 2021, doi: 10.11591/ijeecs.v22.i2.pp976-984.

E. Aker, M. L. Othman, V. Veerasamy, I. bin Aris, N. I. A. Wahab, and H. Hizam, “Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and naive bayes classifier,” Energies, vol. 13, no. 1, p. 243, 2020.

S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Syst., vol. 192, p. 105361, 2020.

Sarwo and S. Aisyah, “Penerapan Data Mining Menggunakan Algoritma Naive Bayes Classifier Untuk Memberikan Rekomendasi Bermain Golf Pada PT. Asiamadya Selaras,” J. Teknol. Pelita Bangsa, vol. 6, no. 2, pp. 99–104, 2017.

P. Koukaras, C. Tjortjis, and D. Rousidis, “Mining association rules from COVID-19 related twitter data to discover word patterns, topics and inferences,” Inf. Syst., vol. 109, p. 102054, 2022.

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,” in 2021 3rd east Indonesia conference on computer and information technology (eiconcit), IEEE, 2021, pp. 41–44.

G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.

S. Theodoridis, “Chapter 12 - Bayesian Learning: Inference and the EM Algorithm,” S. B. T.-M. L. (Second E. Theodoridis, Ed., Academic Press, 2020, pp. 595–646. doi: https://doi.org/10.1016/B978-0-12-818803-3.00023-4.

T. M. Ma, K. Yamamori, and A. Thida, “A comparative approach to Naïve Bayes classifier and support vector machine for email spam classification,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), IEEE, 2020, pp. 324–326.

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,” in 2020 International Conference on Contemporary Computing and Applications (IC3A), IEEE, 2020, pp. 217–220.

S. Ramadani, I. Ambarita, and A. M. H. Pardede, “Metode K-Means Untuk Pengelompokan Masyarakat Miskin Dengan Menggunakan Jarak Kedekatan Manhattan City Dan Euclidean ( Studi Kasus Kota Binjai ),” vol. 04, no. 2, pp. 15–29, 2019.

U. Yabas, H. C. Cankaya, and T. Ince, “Customer churn prediction for telecom services,” Proc. - Int. Comput. Softw. Appl. Conf., pp. 358–359, 2012, doi: 10.1109/COMPSAC.2012.54.

B. Zhu, X. Wu, L. Yang, Y. Shen, and L. Wu, “Automatic detection of books based on Faster R-CNN,” in 2016 third international conference on digital information processing, data mining, and wireless communications (DIPDMWC), IEEE, 2016, pp. 8–12.

Z. Y. Shu, “The study and application of the technology of data warehouse and data mining in the library,” 2011 Int. Conf. Electr. Inf. Control Eng. ICEICE 2011 - Proc., pp. 4671–4673, 2011, doi: 10.1109/ICEICE.2011.5778341.

A. F. Firdaus, R. Saedudin, and R. Andeswari, “Implementasi Metode Klasifikasi Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa,” e-Proceeding Eng., vol. 8, no. 5, pp. 9274–9279, 2021.

L. M. Jose and K. Rahamathulla, “A semantic graph based approach on interest extraction from user generated texts in social media,” Proc. 2016 Int. Conf. Data Min. Adv. Comput. SAPIENCE 2016, pp. 101–104, 2016, doi: 10.1109/SAPIENCE.2016.7684118.

M. Abdel-Basset, M. Mohamed, F. Smarandache, and V. Chang, “Neutrosophic association rule mining algorithm for big data analysis,” Symmetry (Basel)., vol. 10, no. 4, p. 106, 2018.

H. Hassani, C. Beneki, S. Unger, M. T. Mazinani, and M. R. Yeganegi, “Text mining in big data analytics,” Big Data Cogn. Comput., vol. 4, no. 1, p. 1, 2020.

M. Bouazizi and T. Ohtsuki, “Opinion mining in Twitter: How to make use of sarcasm to enhance sentiment analysis,” Proc. 2015 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2015, pp. 1594–1597, 2015, doi: 10.1145/2808797.2809350.

S. Sagadevan, N. H. A. H. Malim, and M. H. Husin, “A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model,” Algorithms, vol. 15, no. 3, 2022, doi: 10.3390/a15030087.

I. I. Kholod, “Conditions for parallel execution of functions in data mining algorithm,” Proc. 2018 IEEE Conf. Russ. Young Res. Electr. Electron. Eng. ElConRus 2018, vol. 2018-Janua, pp. 308–312, 2018, doi: 10.1109/EIConRus.2018.8317094.

M. Watts and N. Kasabov, “Evolutionary optimisation of evolving connectionist systems,” Proc. 2002 Congr. Evol. Comput. CEC 2002, vol. 1, no. June 2014, pp. 606–610, 2002, doi: 10.1109/CEC.2002.1006995.

A. Karimi, L. Rossi, and A. Prati, “Aeda: An easier data augmentation technique for text classification,” arXiv Prepr. arXiv2108.13230, 2021.




How to Cite

Fakhri, M., Sibuea, R. B. V., & Silalahi, T. K. (2023). Timeliness Of Spp Payments At Smk Tritech Infomatika Using Naive Bayes Algorithm. Electronic Integrated Computer Algorithm Journal, 1(1), 9–15. https://doi.org/10.62123/enigma.v1i1.9