Optimasi naive Bayes classifier untuk klasifikasi teks pada e-government menggunakan particle swarm optimization

Naive Bayes classifier optimization for text classification on e-government using particle swarm optimization

Kuncahyo Setyo Nugroho  -  Department of Informatics, Universitas Widyagama Malang, Indonesia
*Istiadi Istiadi scopus  -  Department of Informatics, Universitas Widyagama Malang, Indonesia
Fitri Marisa orcid scopus  -  Department of Informatics, Universitas Widyagama Malang, Indonesia
Received: 15 May 2019; Revised: 13 Oct 2019; Accepted: 17 Oct 2019; Published: 31 Jan 2020; Available online: 5 Nov 2019.
DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.21-26 View
Dataset Sambat Online
Subject
Type Data Set
  Download (38KB)    Indexing metadata
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Original Research Articles
Language: ID
Statistics: 755 272
Abstract
One of the public e-government services is a web-based online complaints portal. Text of complaint needs to be classified so that it can be forwarded to the responsible office quickly and accurately. The standard classification approach commonly used is the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), which still classifies one label and needs to be optimized. This research aims to classify the complaint text of more than one label at the same time with NBC, which is optimized using Particle Swarm Optimization (PSO). The data source comes from the Sambat Online portal and is divided into 70 % as training data and 30 % as testing data to be classified into seven labels. NBC and k-NN algorithms are used as a comparison method to find out the performance of PSO optimization. The 10-fold cross-validation shows that NBC optimization using PSO achieves an accuracy of 87.44 % better than k-NN of 75 % and NBC of 64.38 %. The optimization model can be used to increase the effectiveness of services to e-government in society.

Note: This article has supplementary file(s).

Keywords: online public services; web mining; complaint text classification optimization

Article Metrics:

  1. E. A. Sosiawan, “Tantangan dan hambatan dalam implementasi e-government di Indonesia,” Seminar Nasional Informatika (SEMNASIF), vol. 1, no. 5, pp. 99-108, 2008.
  2. R. Feldman and J. Sanger, The text mining handbook. Cambridge: Cambridge University Press, 2006.
  3. S. Defiyanti, “Integrasi metode klasifikasi dan clustering dalam data mining,” in the 9th National Conference on Information Technology and Electrical Engineering, Yogyakarta, Indonesia, Jul. 2017, pp. 39-44.
  4. F. Gorunescu, Data mining : concepts, models and techniques. Berlin-Heidelberg: Springer-Verlag, 2011.
  5. N. Nurajijah and D. Riana, “Algoritma naïve bayes, decision tree dan svm untuk klasifikasi persetujuan pembiayaan nasabah koperasi syariah,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 2, pp. 77-82, 2019. doi: 10.14710/jtsiskom.7.2.2019.77-82
  6. F. Handayani and F. S. Pribadi, “Implementasi algoritma naive bayes classifier dalam pengklasifikasian teks otomatis pengaduan dan pelaporan masyarakat melalui layanan call center 110,” Jurnal Teknik Elektro, vol. 7, no. 1, pp. 19-24, 2015.
  7. A. A. Prasanti, M. A. Fauzi, and M. T. Furqon, “Klasifikasi teks pengaduan pada sambat online menggunakan metode n-gram dan neighbor weighted k-nearest neighbor (NW-KNN),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 2, pp. 594-601, 2018.
  8. A. Nurhadi, “Implementasi algoritma naive bayes classifier berbasis particle swarm optimization (PSO) untuk klasifikasi konten berita digital bahasa Indonesia,” Speed - Sentra Penelitian Engineering dan Edukasi, vol. 8, no. 3, pp. 48-56, 2016.
  9. T. Hidayatulloh, A. Herliana, and T. Arifin, “Klasifikasi sel tunggal pap smear berdasarkan analisis fitur berbasis naive bayes classifier dan particle swarm optimization,” Swabumi, vol. 4, no. 2, pp. 186-193, 2016.
  10. A. Taufik, “Optimasi particle swarm optimization sebagai seleksi fitur pada analisis sentimen review hotel berbahasa Indonesia menggunakan algoritma naïve bayes,” Jurnal Teknik Komputer, vol. 3, no. 2, pp. 40-47, 2017.
  11. R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan kinerja metode naive bayes dan k-nearest neighbor untuk klasifikasi artikel berbahasa Indonesia,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 4, pp. 427-434, 2018. doi: 10.25126/jtiik.201854773
  12. H. Muhamad, C. A. Prasojo, N. A. Sugianto, L. Surtiningsih, and I. Cholissodin, “Optimasi naive bayes classifier menggunakan particle swarm optimization pada data iris,” Jurnal Teknologi Informasi Dan Ilmu Komputer, vol. 4, no. 3, pp. 180-184, 2017. doi: 10.25126/jtiik.201743251

  1. Discrimination of civet coffee using visible spectroscopy
    Graciella Mae L Adier, Charlene A Reyes, Edwin R Arboleda, Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 239, 2020. doi: 10.14710/jtsiskom.2020.13734