Isu Penyelarasan Flight Information Region di atas Wilayah Natuna
DOI:
https://doi.org/10.54324/j.mtl.v5i3.273Kata Kunci:
sentiment analysis, text mining, aviation community, flight information region, NatunaAbstrak
Citizen sentiment is essential to evaluate the support toward government program. In 2015, Indonesian government proposed an acceleration program on re-alignment on Flight Information Region above Natuna area. Since then, primary of discussion is often be held as a formal or informal event. The data collected from 210 respondent, which consist of pilots, military staff, ATC staff, and academician. Furthermore, this study uses TF-IDW weighting technique to cluster the argument as positive, neutral, and negative sentiment. The result shows that most of Indonesia aviation community (75%) argue that FIR management should base on sovereignty and safety. Moreover, FIR issue under economic, national security and management shows significant positive respond (>90%) while FIR management under Singapore shows a negative response (100%). The result indicates that the aviation community supports the national program Natuna FIR re-alignment.Referensi
Akbar, A.S., Sediyono, E., & Nurhayati, O.D., (2016). Analisis Sentimen Berbasis Ontologi di Level Kalimat untuk Mengukur Persepsi Produk. Jurnal Sistem Informasi Bisnis, 5(2),84-97.
Ghag, K., & Shah, K., (2014). SentiTFIDF–Sentiment Classification using Relative Term Frequency Inverse Document Frequency. International Journal of Advanced Computer Science & Applications, 5(2).
Li, G., & Liu, F., 2010, November. A clustering-based approach on sentiment analysis. In Intelligent Systems and Knowledge Engineering (ISKE), (2010). International Conference on (pp. 331-337). IEEE.
Monarizqa, N., Nugroho, L.E., & Hantono, B.S., (2014). Penerapan Analisis Sentimen Pada Twitter Berbahasa Indonesia Sebagai Pemberi Rating. Jurnal Penelitian Teknik Elektro dan Teknologi Informasi, 1(3).
Pang, B. & Lee, L., (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2),1-135.
Rianto, B., (2016). Implementasi & Perbandingan Metode Prapemprosesan Pada Analisis Sentimen Gubernur DKI Jakarta Menggunakan Metode Support Vector Machine dan Naive Bayes (Doctoral dissertation, Universitas Gadjah Mada).
Rokhim, A., (2018). Implementasi Metode Term Frequency Inversed Document Frequence (Tf-Idf) dan Vector Space Model pada Aplikasi Pemberkasan Skripsi Berbasis WEB. Jurnal SPIRIT, 9(1).
Saputra, N., Adji, T.B., & Permanasari, A.E. (2015). Analisis sentimen data presiden Jokowi dengan preprocessing normalisasi dan stemming menggunakan metode naive bayes dan SVM. Jurnal Dinamika Informatika, 5(1).
Sarwono, J., Arikunto, M., & Arikunto, M.S. (2006). Metode Penelitian. Kuantitatif Kualitatif.
Yun-tao, Z., Ling, G., & Yong-cheng, W.,(2005). An improved TF-IDF approach for text classification. Journal of Zhejiang University-Science A, 6 (1), pp.49-55.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

