Main Article Content

Abstract

Text mining is the process of discovering new, previously unknown information from several text documents. Text mining can be applied to the fields of information extraction, topic tracking, document summarization, document categorization or grouping, concept linking or question answering systems. One thing that is often done in implementing text mining is information extraction. Information extraction aims to extract information from unstructured documents into structured data, with the aim of making it easier to analyze the data. In this study, feature extraction will be used to extract features from the Community Service document, using the Frequent Itemset Mining (FIM) algorithm. The features taken are PKM Title, Abstract, Year of Service, Location, and research topic. After obtaining the features, the service topics will be grouped using the Naive Bayes algorithm. The results of this study were tested using a confusion matrix, with an accuracy of 70%. Factors that affect the accuracy results include the amount of training data, the distribution of training data, and the optimization of the algorithm used

Keywords

Text Mining, Ektraksi Fitur, Algoritma Naive Bayes Text Mining Ektraksi Fitur Algoritma Naive Bayes

Article Details

How to Cite
Nurmalasari, D., & Ribut Yuliantoro, H. (2022). Implementasi Ekstraksi Fitur untuk Pengelompokan Dokumen Proposal Menggunakan Algoritma Naïve Bayes. Jurnal Komputer Terapan , 8(1), 194–203. https://doi.org/10.35143/jkt.v8i1.5351

References

  1. Ardanu, F., Himawan, H., & P, D. B. (2013). Pemanfaatan Teknologi Data Mining Dalam Menentukan Efektifitas Penyebaran Brosur.
  2. Dharmayanti, D., Bachtiar, A. M., & Heryandi, A. (2013). Pemodelan Data Warehouse,
  3. (2), 151–168.
  4. Fadilah, U., Winarno, W. W., Amborowati, A., Fadilah, U., Winarno, W. W., & Amborowati, A. (2016). Perancangan Data Warehouse Untuk Sistem Akademik STMIK Kadiri Data Warehouse System Design For Academic STMIK Kadiri, 6(2), 217–228.
  5. Ilmiah, J., Komputa, I., Volume, E., Issn, F., Cv, D. I., Anugerah, K., … Bandung, U. (2016a). PEMBANGUNAN PERANGKAT LUNAK DATA WAREHOUSE Jurnal Ilmiah Komputer Dan Informatika ( KOMPUTA ), 1.
  6. Ilmiah, J., Komputa, I., Volume, E., Issn, F., Cv, D. I., Anugerah, K., … Bandung, U. (2016b). PEMBANGUNAN PERANGKAT LUNAK DATA WAREHOUSE Jurnal Ilmiah Komputer Dan Informatika ( KOMPUTA ). Ok
  7. Mulyati, S., Amini, S., & Juliasari, N. (2014). 104-279-1-PB.Pdf. Jurnal Telematika MKOM, 6 No.1.
  8. Ponniah, P. (2001). Data Warehouse Fundamentals: A Comprehensive Guide For IT Professional.J.Wiley. New York.
  9. M. Ainiyah, D. Nurmalasari, And W. Nengsih, “Visualisasi Data Teks Food Reviews Menggunakan Frequent Itemset Mining,” J. Aksara Komput. Terap., Vol. 6, No. 2, 2017.
  10. L. Tanjaya, A. Wibowo, And D. Nurmalasari, “Sistem Pengelompokan E-Journal Berdasarkan Abstrak Menggunakan Text Mining Dan K-Means Clustering,” J. Aksara Komput. Terap., Vol. 5, No. 1, 2016
  11. J. Han, J. Pei, And M. Kamber, Data Mining: Concepts And Techniques. Elsevier, 2011.
  12. R. Feldman And J. Sanger, The Text Mining Handbook: Advanced Approaches In Analyzing Unstructured Data. Cambridge University Press, 2007.
  13. E. Muningsih, H. M. Nur, F. F. D. Imaniawan, V. R. Handayani, And F. Endiarto, “Comparative Analysis On Dimension Reduction Algorithm Of Principal Component Analysis And Singular Value Decomposition For Clustering,” In Journal Of Physics: Conference Series, 2020, Vol. 1641, No. 1, P. 012101.
  14. A. Sukma, B. Zaman, And E. Purwanti, “Information Retrieval Document Classification With K-Nearest Neighbor,” Rec. Libr. J., Vol. 1, No. 2, Pp. 129–138, 2015.
  15. A. N. Asyfa, D. Nurmalasari, And R. P. Sari, “Identifikasi Kinerja Perusahaan Berdasarkan Laporan Keuangan Menggunakan Algoritma K-NN,” J. Aksara Komput. Terap., Vol. 5, No. 1, 2016.
  16. S. H. Myaeng, K. S. Han, And H. C. Rim, “Some Effective Techniques For Naive Bayes Text Classification,” IEEE Trans. Knowl. Data Eng., Vol. 18, No. 11, Pp. 1457–1466, 2006
  17. W. Zhang And F. Gao, “Performance Analysis And Improvement Of Naïve Bayes In Text Classification Application,” 2013 IEEE Conf. Anthol. Nthol. 2013, Pp. 1–4, 2013.
  18. M. A. Fauzi, S. Gosario, A. Z. Arifin, And I. S. Prabowo, “Klasifikasi Berita Berbahasa Indonesia Menggunakan Seleksi Fitur Dua Tahap Dan Naive Bayes,” SYSTEMIC, Vol. 03, No. 02, Pp. 7–12, 2017.
  19. Nurhuda, F., Widya Sihwi, S. Dan Doewes, A. (2016) “Analisis Sentimen Masyarakat Terhadap Calon Presiden Indonesia 2014 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Teknologi & Informasi Itsmart, 2(2), Hal. 35