Analysis of Community Sentiments Regarding Plans to Relocate National Capital Using the Naïve Bayes Method Analisa Sentimen Masyarakat Tentang Rencana Pemindahan Ibukota Negara Dengan Metode Naïve Bayes
Article Sidebar
Main Article Content
Abstract
This study aims to analyze sentiment towards the transfer of new capitals derived from comments on the tweeter. The method used in this research is Naïve Bayes Classifier, a classic method that has a pretty good accuracy. Naive Bayes Classifier is a probabilistic classification based on the Bayes theorem, taking into account naïv independence assumptions. In addition to using the naïve bayes method, in this study the researchers also used word weighting. The weighting word used is TF-IDF, which is a combination of term frequency and inverse document frequency. By using 3 testing methods, namely Confusion matrix, Precission and Recall, and K-Fold Cross Validation. The results obtained in this study are 3 document classifications, namely Positive, Negative and Neutral. Testing is done by dividing the document into 2 subsets, namely training data and test data and the resulting accuracy of 64.6%.
Article Details
Copyright (c) 2020 Tomi Eko Hidayat, Mochamad Alfan Rosid, Ika Ratna Indra Astutik
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Jurnal:
[1] Penulis1 A, Penulis2 A. Judul Makalah. Nama jurnal atau singkatannya. tahun; Vol.(Issue): halaman.
[2] Minarno, Agus Eko, and Nanik Suciati. "Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12.3 (2014): 597-604.
Prosiding:
Jika prosiding terdiri dari beberapa volume :
[3] Penulis1 A, Penulis2 B. Judul Makalah. Nama conference atau seminar. Kota. Tahun; volume.
[4] Kusuma, Wahyu Andhyka, and Lailatul Husniah. "Skeletonization using thinning method for human motion system." Intelligent Technology and Its Applications (ISITIA), 2015 International Seminar on. IEEE, 2015; Vol 1.
Jika prosiding terdiri dari satu volume :
[5] Penulis1 A, Penulis2 B. Judul Makalah. Nama conference atau seminar. Kota. Tahun.
[6] Minarno, Agus Eko, et al. "Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification." Information and Communication Technology (ICoICT), 2014 2nd International Conference on. IEEE, 2014.
Buku:
Jika refrensi merujuk pada beberapa halaman pada buku
[7] Penulis1 A, Penulis2 B. Judul Buku. Edisi. Kota: Penerbit. tahun: halaman.
[8] RC. Gonzales, RE. Woods. Digital image processing. 3rd edition. Prentice Hall. 2007: 424:447.
Jika referensi mengacu pada sebagian halaman pada buku :
[9] Penulis1 A, penulis2 B. Judul Buku. Kota: Penerbit. Tahun.
[10] Ward J, Peppard J. Strategic planning for Information Systems. Fourth Edition. West Susse: John Willey & Sons Ltd. 2007.
Buku Terjemahan:
[11] Penulis Asli. Tahun. Judul buku yang diterjemahkan. Penerjemah. Kota: Penerbit yang menerjemahkan buku. Tahun buku di terjemahkan.
[12] Pabla. 2004. Sistem Distribusi Tenaga Listik. Abdul Hadi. Jakarta: Erlangga. 2007.
Thesis/Disertation:
[13] Penulis. Judul Thesis/Disertasi. Thesis/Disertasi. Kota & Nama Universitas; Tahun.
[14] Rusdi M. A Novel Fuzzy ARMA Model for Rain Prediction in Surabaya. PhD Thesis. Surabaya: Postgraduate ITS; 2009.
Paten:
[15] Penulis1 A, Penulis2 B.. Judul Patent. Nomer Paten (Paten). Tahun Publikasi.
[16] Ahmad LP, Hooper A. The Lower Switching Losses Method of Space Vector Modulation. CN103045489 (Patent). 2007.
Standar:
[17] Nama standar/Institusi. Nomer Standar. Judul Standar. Tempat publikasi. Penerbit. Tahun Publikasi.
[18] ISO/IEC. 9126-1:2001. Software engineering -- Product quality. New York: IEEE Press; 2001.
If your references are from Reports
[19] Penulis/Editor (jika ada editor letakkan ed. Didepan nama editor). Judul. Organisasi. Nomer Laporan:. Tahun Publikasi.
[20] James S, Whales D. The Framework of Electronic Goverment. U.S. Dept. of Information Technology. Report number: 63. 2005.