Klasifikasi Jenis Laporan Masyarakat Dengan K-Nearest Neighbor Algorithm
DOI:
https://doi.org/10.33633/joins.v5i1.3355Abstract
Feedback masyarakat terhadap pelayanan pemerintah merupakan elemen penting dalam proses evaluasi dan peningkatan kinerja. Maka dari itu pemerintah perlu untuk memiliki metode pelaporan yang efektif, efisien dan sistematis. Feedback masyarakat dapat berupa pengaduan, permintaan informasi dan aspirasi. Salah satu cara penyampain feedback masyarakat adalah melalui media sosial. Klasifikasi jenis laporan/feedback masyarakat ini penting dilakukan untuk mempercepat proses penanggapan laporan. Algoritma K-Nearest neighbor pada metode text mining ini merupakan salah satu solusi untuk dapat membantu proses klasifikasi jenis laporan. Dengan 930 data latih dan 100 data uji laporan masyarakat tahun 2017 yang disampaikan melalui media sosial, menghasilkan nilai akurasi tertinggi k=11 sebesar 82%.References
R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructure Data. Cambridge: Cambridge University Press, 2007.
P. B. Dastanwala and V. Patel, “A review on social audience identification on twitter using text mining methods,†Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 1917–1920, 2016.
W. Gata and Purnomo, “Akurasi Text Mining Menggunakan Algoritma K-Nearest Neighbor pada Data Center Berita SMS,†J. Format, vol. 6, no. 1, 2017.
Okfalisa, I. Gazalba, Mustakim, and N. G. I. Reza, “Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification,†Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-Janua, pp. 294–298, 2018.
N. Anggraini and M. J. Tursina, “Sentiment Analysis of School Zoning System On Youtube Social Media Using The K-Nearest Neighbor With Levenshtein Distance Algorithm,†in The 7th International Conference on Cyber and IT Service Management (CITSM 2019), 2019.
K. Nyodu and K. Sambjo, “Automatic Identification of Arunachal language Using K-Nearest Neighbor Algorithm,†in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, pp. 213–216.
L. Yang, Q. Yang, Y. Li, and Y. Feng, “K-Nearest Neighbor Model based Short-Term Traffic Flow Prediction Method,†in 2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2019, pp. 27–30.
A. Hetami and B. Dwijawisnu, “Perancangan Information Retrieval (IR) Untuk Pencarian Ide Pokok Teks Artikel Berbahasa Inggris dengan Pembobotan Vector Space Model,†J. Ilm. Teknol. dan Inf. ASIA, vol. 9, no. 1, 2015.
M. Bramer, Principles of Data Mining. Springer, 2007.
X. Wu and V. Kumar, The Top Ten Algorithms in Data Mining. Minnesota: CRC Press, 2009.
C. Sammut and G. I. Webb, Encyclopedia of Machine Learning. New York: Springer, 2010.
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