Implementation Of Text Mining And Pattern Discovery With Naive Bayes Algorithm For Classification Of Text Documents

Implementasi Text Mining Dan Pattern Discovery Dengan Algoritma Naive Bayes Untuk Klasifikasi Dokumen Teks

  • Novia Lestari Universitas Islam Negeri Imam Bonjol Padang
  • Ozzy Secio Riza Universitas Islam Negeri Imam Bonjol Padang
  • Reno Ardinal Universitas Islam Negeri Imam Bonjol Padang
Keywords: Text Mining, Pattern Discovery, Algoritma Naïve Baye, Klasifikasi

Abstract

Abstract

Classification of text documents can be managed manually by using human-made classification rules. However, as many text document files exist today, the application of machine learning can help to classify the documents more effectively and the structured. Data mining with the Naïve Bayes algorithm can help the process of searching for a set of patterns or characteristics that explain and separate a classification of data based on the aim that the model can used to predict and classify the the data that has been used. This study uses text mining and pattern discovery techniques with the naïve Bayes algorithm used in the Indonesian language online news classification process with an accuracy test result of 63.9 and a low error rate of 41.02%.

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References

Puspitasari, N., Pratama, F. et. all. ‘Quality Classification of Palm Oil Varieties Using Naive Bayes Classifier’, Digital Zone : Jurnal Teknologi Informasi & Komunikasi. 2022

Chen, P. and Yu, L. ‘Use of Data Mining Technologies in an English Online Test Results Management System’, International Journal of Emerging Technologies in Learning, 16(9), pp. 166–181. 2021 Available at: https://doi.org/10.3991/ijet.v16i09.22743.

Hasan, N.F. et al. ‘Sentiment Analysis of Public Opinion Regarding Papuan Local Languages Condition Using Data Science Approach’, Digital Zone : Jurnal Teknologi Informasi & Komunikasi, 13(02), pp. 125–139. 2022

Irmanita, R., Sri Suryani Prasetiyowati and Yuliant Sibaroni. ‘Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(1), pp. 10–16. 2021 Available at: https://doi.org/10.29207/resti.v5i1.2770.

Safitri, S.N., Haryono Setiadi and Suryani, E. (2022) ‘Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(3), pp. 448–456. Available at: https://doi.org/10.29207/resti.v6i3.3935.

Sari, R. and Hayuningtyas, R.Y. (2019) ‘Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website’, Indonesian Journal on Software Engineering (IJSE), 5(2), pp. 51–60. Available at: https://doi.org/10.31294/ijse.v5i2.6957.

Seemi, F. et al. ‘Browsing behaviour analysis using data mining’, International Journal of Advanced Computer Science and Applications, 10(2), pp. 490–498. 2019 Available at: https://doi.org/10.14569/ijacsa.2019.0100263.

Sravani, T., Madala, S.R. and HeenaKauser, S. ‘College students’ Network behavior Using data mining and feature analysis’, Journal of Physics: Conference Series, 2089(1). 2021 Available at: https://doi.org/10.1088/1742-6596/2089/1/012075.

Suharjo, B. ‘Application of K-Means Cluster and Spatial Statistics using Python to Analyze the Indicators of Indonesia Information Technology’, Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 12(1), pp. 11–18. 2021 Available at: https://doi.org/10.31849/digitalzone.v12i1.4310.

Sunday, K. et al. ‘Analyzing student performance in programming education using classification techniques’, International Journal of Emerging Technologies in Learning, 15(2), pp. 127–144. 2020 Available at: https://doi.org/10.3991/ijet.v15i02.11527.

Sutoyo, E. and Almaarif, A. ‘Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 4(1), pp. 95–101. 2020 Available at: https://doi.org/10.29207/resti.v4i1.1502.

Tope-Oke, A., Afolalu, C.A. and Omofade, O. ‘A Data Mining Based Approach to Customer Behaviour in an Electronic Settings’, Journal of Computer and Communications, 07(05), pp. 42–53. 2019 Available at: https://doi.org/10.4236/jcc.2019.75004.

Wang, C. ‘Analysis of Students’ Behavior in English Online Education Based on Data Mining’, Mobile Information Systems, 2021. Available at: https://doi.org/10.1155/2021/1856690.

Wiza, F. ‘Klasterisasi karakteristik kekerasan seksual terhadap anak dengan metode k-means cluster analysis’, Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 10(1), pp. 44–53. 2019 Available at: https://doi.org/10.31849/digitalzone.v10i1.2423.

A. Ridwan, “Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus,” J. SISKOM-KB, vol. 4, no. 1, pp. 15–21, 2020.

Prakoso, B.S. et al. ‘Klasifikasi Berita Menggunakan Algoritma Naive Bayes Classifer Dengan Seleksi Fitur Dan Boosting’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 3(2), pp. 227–232. 2022 Available at: https://doi.org/10.29207/resti.v3i2.1042.

Published
2023-05-28
How to Cite
Novia Lestari, Secio Riza, O., & Ardinal, R. (2023). Implementation Of Text Mining And Pattern Discovery With Naive Bayes Algorithm For Classification Of Text Documents: Implementasi Text Mining Dan Pattern Discovery Dengan Algoritma Naive Bayes Untuk Klasifikasi Dokumen Teks. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 14(1), 88-102. https://doi.org/10.31849/digitalzone.v14i1.13596
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