Analisis Sentimen Aplikasi Identitas Kependudukan Digital (IKD) Menggunakan Metode Naïve Bayes

Rahmat Hidayat; Rohim Nur Rahman; Muhammad Reifin Perdana; Arbansyah Arbansyah

doi:10.59581/jusiik-widyakarya.v2i1.2320

Authors

Rahmat Hidayat Universitas Muhammadiyah Kalimantan Timur
Rohim Nur Rahman Universitas Muhammadiyah Kalimantan Timur
Muhammad Reifin Perdana Universitas Muhammadiyah Kalimantan Timur
Arbansyah Arbansyah Universitas Muhammadiyah Kalimantan Timur

DOI:

https://doi.org/10.59581/jusiik-widyakarya.v2i1.2320

Keywords:

Sentiment Analysis, Digital Population Identity, KTP, Naïve Bayes

Abstract

Digital Population Identity (IKD) is a digital-based location data innovation through a mobile application with a photo or QR Code. The government's objective is to reduce the physical prints of KTP as well as the use of blank KTP-el in the hope of administrative efficiency. ICT is integrated with health services, education, banking, and taxation, facilitating public access in the era of technological development. However, in remote areas, limited internet access and minimal socialization raise concerns about the security of digital identity data being considered. Social media, especially YouTube, is a channel platform used by the public to convey opinions, opinions and comments about ICTs. So that's why sentimental analysis is needed using the Naive Bayes algorithm to help understand public opinion. The tests were conducted using Orange on 1,561 data showing accuracy, precision, recall, and F1 above 90%. The results of this analysis can serve as a guide for staff in interacting with the community for the implementation of Digital KTP through IKD, as well as improving services regarding the applications provided.

References

Ardianto, Rian, Tri Rivanie, Yuris Alkhalifi, Fitra Septia Nugraha, and Windu Gata. 2020. “Sentiment Analysis on E-Sports for Education Curriculum Using Naive Bayes and Support Vector Machine.” Jurnal Ilmu Komputer Dan Informasi 13, no. 2: 109–22. https://doi.org/10.21609/jiki.v13i2.885.

Arvyantomo, M, and N Ratama. 2023. “Analisis Sentimen Masyarakat Indonesia Terhadap Invasi Russia Di Ukraina Menggunakan Metode Naive Bayes Pada Media Sosial Facebook.” LOGIC: Jurnal Ilmu Komputer … 1, no. 4: 705–17. https://www.journal.mediapublikasi.id/index.php/logic/article/view/2142.

Atmadja, Boby Rizki. 2022. “Analisis Sentimen Bahasa Indonesia Pada Tempat Wisata Di Kabupaten Sukabumi Dengan Naive Bayes Classifier.” Elkom : Jurnal Elektronika Dan Komputer 15, no. 2: 371–82. https://doi.org/10.51903/elkom.v15i2.872.

Cendana, Maya, and Silvester Dian Handy Permana. 2019. “Pra-Pemrosesan Teks Pada Grup Whatsapp Untuk Pemodelan Topik.” Junal Mantik Penusa 3, no. 3: 107–16.

Fanissa, Shima, M Ali Fauzi, and Sigit Adinugroho. 2018. “Analisis Sentimen Pariwisata Di Kota Malang Menggunakan Metode Naive Bayes Dan Seleksi Fitur Query Expansion Ranking.” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer 2, no. 8: 2766–70. http://j-ptiik.ub.ac.id.

Hakimi, Fajar Darwis Dzikril. 2018. “Sistem Analisis Sentimen Publik Tentang Opini Pemilihan Kepala Daerah Jawa Timur 2018 Pada Dokumen Twitter Menggunakan Naive Bayes Classifier.” Skripsi Universitas Islam Negeri Sunan Ampel Surabaya.

Indah Nurhafida, Selva, and Falentino Sembiring. 2021. “Analisis Text Clustering Masyarakat Di Twiter Mengenai Mcdonald’Sxbts Menggunakan Orange Data Mining.” SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika), 28–35.

Khotimah, Aristin Chusnul, and Ema Utami. 2022. “Perbandingan Algoritma Naïve Bayes Classifier, K-Nearest Neighbor Dan Support Vector Machine Dalam Klasifikasi Karakter Individu Pada Akun Twitter.” Jurnal Teknik Informatika (Jutif) 3, no. 3: 673–80. http://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/254.

Muzaki, Akhmad, and Arita Witanti. 2021. “Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-19 By Method Naive Bayes Classifier.” Jurnal Teknik Informatika (Jutif) 2, no. 2: 101–7. https://doi.org/10.20884/1.jutif.2021.2.2.51.

Prasetyo, Tri, Hadi Zakaria, and Pandu Wiliantoro. 2022. “Analisis Layanan Pelanggan PT PLN Berdasarkan Media Sosial Twitter Dengan Menggunakan Metode Naïve Bayes Classifier.” OKTAL : Jurnal Ilmu Komputer Dan Sains 1, no. 6: 573–82. https://journal.mediapublikasi.id/index.php/oktal.

Putu, Ni Luh Putu Merawati, Ahmad Zuli Amrullah, and Ismarmiaty. 2021. “Analisis Sentimen Dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes Dan Latent Dirichlet Allocation.” Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi) 5, no. 1: 123–31. https://doi.org/10.29207/resti.v5i1.2587.

Septian, Jeremy Andre, Tresna Maulana Fachrudin, and Aryo Nugroho. 2019. “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF Dan K-Nearest Neighbor.” Journal of Intelligent System and Computation 1, no. 1: 43–49. https://doi.org/10.52985/insyst.v1i1.36.

Somantri, Oman, and Dairoh Dairoh. 2019. “Analisis Sentimen Penilaian Tempat Tujuan Wisata Kota Tegal Berbasis Text Mining.” Jurnal Edukasi Dan Penelitian Informatika (JEPIN) 5, no. 2: 191. https://doi.org/10.26418/jp.v5i2.32661.

Suryani, Putu Sri Merta, Linawati Linawati, and Komang Oka Saputra. 2019. “Penggunaan Metode Naïve Bayes Classifier Pada Analisis Sentimen Facebook Berbahasa Indonesia.” Majalah Ilmiah Teknologi Elektro 18, no. 1: 145. https://doi.org/10.24843/mite.2019.v18i01.p22.

Ulfa, Maria Arista, Budi Irmawati, and Ario Yudo Husodo. 2018. “Twitter Sentiment Analysis Using Na¨ive Bayes Classifier with Mutual Information Feature Selection.” Journal of Computer Science and Informatics Engineering (J-Cosine) 2, no. 2: 106–11. https://doi.org/10.29303/jcosine.v2i2.120.

Wahyuningsih, N. 2023. “Perbandingan Metode Klasifikasi Dalam Analisis Sentimen Masyarakat Terhadap Identitas Kependudukan Digital (IKD)” 8, no. 4: 1218–27. https://repository.uksw.edu/handle/123456789/30436%0Ahttps://repository.uksw.edu/bitstream/123456789/30436/8/T1_672019296_Daftar Pustaka.pdf.