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Khazanah Informatika
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Khazanah Informatika
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Khazanah Informatika
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Pengelompokan Komentar Netizen pada Media Sosial Pemerintah Daerah Berdasarkan Frekuensi Kata Kunci

Authors: Nur Aini Rakhmawati; Nody Risky Pratomo;

Pengelompokan Komentar Netizen pada Media Sosial Pemerintah Daerah Berdasarkan Frekuensi Kata Kunci

Abstract

Menurut survei internetworldstats yang dirilis pada Januari 2018, jumlah pengguna internet di Indonesia telah mencapai 132,7 juta jiwa. Dari jumlah tersebut, 40% di antaranya merupakan pengguna aktif media sosial. Hal ini mengakibatkan peningkatan penerapan konsep e-government pada pemerintah dengan media sosial. Melalui media sosial, masyarakat dapat melakukan interaksi kepada akun media sosial pemerintah kota dalam memberikan informasi atau kritik dan saran terkait kotanya. Menindaklanjuti fenomena tersebut, diperlukan aplikasi untuk melakukan pengelompokan komentar masyarakat di media sosial berdasarkan Satuan Kerja Perangkat Daerah (SKPD). Proses kategorisasi dilakukan dengan melihat kesesuaian kata kunci SKPD dengan komentar pada media sosial Facebook, Twitter, dan Youtube akun resmi pemerintah kota dan influencer. Selain itu, aplikasi ini dapat memberikan rekomendasi kata kunci berdasarkan komentar yang masuk. Proses rekomendasi dilakukan dengan cara mengambil kata kunci yang memiliki frekuensi terendah.Dalam melakukan pengujian, digunakan data pada pemerintah kota Depok sebagai contoh. Hasil pengujian menunjukkan bahwa dari 4.325 komentar yang telah dilakukan preprocessing, 10,35% komentar berhasil dikategorikan sesuai SKPD. Pengujian rekomendasi kata turut dilakukan dengan mengambil dinas dalam pemerintahan kota yang sama. Dari 19 dinas, terdapat 3 dinas yang mendapatkan rekomendasi kata. Dari 3 dinas, 1 dinas memiliki kata kunci yang relevan dan 2 sisanya tidak relevan dengan SKPD. Hal ini disebabkan tidak tepatnya kata kunci SKPD untuk melakukan kategorisasi serta jumlah komentar terkategorisasi yang masih sedikit.

Related Organizations
Keywords

Electronic computers. Computer science, kategorisasi, regular expression, media sosial, QA75.5-76.95, e-government

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold