Analisis Sentimen Ulasan Pengguna Aplikasi Ajaib Menggunakan Metode Naïve Bayes
DOI:
https://doi.org/10.30865/klik.v4i3.1303Keywords:
Sentiment Analysis; Review; User; Naïve Bayes; AjaibAbstract
Developments occurring in the investment sector have made people interested in starting to invest. Currently, investments can be made easily using applications on smartphones, such as the Ajaib application. The Ajaib application offers comfort in making investments with the features, security and convenience provided to users, so that various reviews emerge from users in the form of negative or positive sentiments after using the Ajaib application. From the various reviews given, there are still some people who are still hesitant to use the Ajaib application because of the reviews given by people who have used it. The reviews given by users encouraged researchers to conduct research regarding user views after using the Ajaib application with the Naïve Bayes algorithm. The research was carried out to determine the number of negative and positive sentiments from existing reviews regarding the Ajaib application. Researchers collected 500 data through a scraping process on Google Playstore in the Ajaib application. The results obtained in research on sentiment analysis using the Naïve Bayes algorithm method were 44.2% or 221 positive sentiments and 55.8% or 279 negative sentiments from the 500 sentiment data that had been analyzed. The evaluation process was carried out using a confusion matrix to evaluate the Naïve Bayes algorithm method. The results of the evaluation process obtained an accuracy value of 74.44%.
Downloads
References
F. Ainiyah, I. Yuliana, and M. I. Prajawati, “Sikap Investasi dan Norma Subjektif Terhadap Minat Investasi dengan Literasi Keuangan Sebagai Variabel Moderasi,” Ekon. dan Bisnis, vol. 8, no. 8(1), pp. 39–45, 2021, doi: 10.35590/jeb.v10i1.6284.
M. A. Khakim, “Pengaruh Investasi Dalam Perekonomian,” J. AKSES, no. 2085, pp. 1–10, 2022, doi: 10.47329/jurnalakses.v14i2.892.
M. Rinestu, I. P. Made Indra, B. Marsanto, and S. Trisakti, “Classification Of Investment Decisions During Covid-19 Pandemic Using Naive Bayes,” Manag. Stud. Entrep. J., vol. 3, no. 4, pp. 1784–1796, 2022, [Online]. Available: http://journal.yrpipku.com/index.php/msej.
A. Voutama and E. Novalia, “Perancangan Aplikasi M-Magazine Berbasis Android Sebagai Sarana Mading Sekolah Menengah Atas,” J. Tekno Kompak, vol. 15, no. 1, p. 104, Feb. 2021, doi: 10.33365/jtk.v15i1.920.
Aditya Fajar Ramadhan, Ade Dwi Putra, and Ade Surahman, “Aplikasi Pengenalan Perangkat Keras Komputer Berbasis Android Menggunakanaugmented Reality (Ar),” J. Teknol. dan Sist. Inf., vol. 2, no. 2, pp. 24–31, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI.
EllyaHelmud, “Optimasi Basis Data Oracle Menggunakan Complex View Studi Kasus?: PT. Berkat Optimis Sejahtera (PT.BOS) Pangkalpinang,” J. Informanika, vol. 7, no. 1, pp. 80–86, 2021, doi: 10.52233/informanika.v7i01.204.
A. Wahab, A. Syahid, and J. Junaedi, “Penyajian Data Dalam Tabel Distribusi Frekuensi Dan Aplikasinya Pada Ilmu Pendidikan,” Educ. Learn. J., vol. 2, no. 1, p. 40, 2021, doi: 10.33096/eljour.v2i1.91.
F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier,” J. Linguist. Komputasional, vol. 5, no. 1, p. 34, Apr. 2022, doi: 10.26418/jlk.v5i1.79.
D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” J. Manaj. Inform., vol. 12, no. 2, pp. 89–99, 2022, doi: 10.34010/jamika.v12i2.7764.
M. A. Amrustian, W. Widayat, and A. M. Wirawan, “Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 535, 2022, doi: 10.30865/mib.v6i1.3527.
W. A. Prabowo and C. Wiguna, “Sistem Informasi UMKM Bengkel Berbasis Web Menggunakan Metode SCRUM,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 1, p. 149, Jan. 2021, doi: 10.30865/mib.v5i1.2604.
N. Ramdhani and R. H. Al-Fadillah, “Analisis Sentimen Pengguna Twitter Terhadap Belajar Daring Selama Pandemi Covid-19 Dengan Deep Learning,” J. Siliwangi, vol. 7, no. 2, p. 2021, 2021.
P. Arsi, R. Wahyudi, and R. Waluyo, “Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 231–237, 2021, doi: https://doi.org/10.29207/resti.v5i2.2698.
E. Fitriani, “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” Sistemasi, vol. 9, no. 1, p. 103, 2020, doi: 10.32520/stmsi.v9i1.596.
T. A. Sari, E. Sinduningrum, and F. N. Hasan, “Analisis Sentimen Ulasan Pelanggan Pada Aplikasi Fore Coffee Menggunakan Metode Naïve Bayes,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 6, pp. 773–779, 2023, doi: 10.30865/klik.v3i6.884.
A. Tangkelayuk, “The Klasifikasi Kualitas Air Menggunakan Metode KNN, Naïve Bayes, dan Decision Tree,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 1109–1119, Jun. 2022, doi: 10.35957/jatisi.v9i2.2048.
C. Nas, “Data Mining Prediksi Minat Calon Mahasiswa Memilih Perguruan Tinggi Menggunakan Algoritma C4.5,” J. Manaj. Inform., vol. 11, no. 2, pp. 131–145, 2021, doi: 10.34010/jamika.v11i2.5506.
K. F. Irnanda, A. P. Windarto, and I. S. Damanik, “Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 1, p. 122, 2022, doi: 10.30865/jurikom.v9i1.3836.
D. Pratmanto, F. Fandi, and D. Imaniawan, “Computer Science ( CO-SCIENCE ) Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors,” vol. 3, no. 2, pp. 110–117, 2023, doi: https://doi.org/10.31294/coscience.v3i2.1917.
K. D. Indarwati and H. Februariyanti, “Analisis Sentimen Terhadap Kualitas Pelayanan Aplikasi Gojek Menggunakan Metode Naive Bayes Classifier,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 10, no. 1, 2023, doi: 10.35957/jatisi.v10i1.2643.
J. Florensius Sianipar, Y. R. Ramadhan, and I. Jaelani, “Analisis Sentimen Pembangunan Kereta Cepat Jakarta-Bandung di Media Sosial Twitter Menggunakan Metode Naive Bayes,” Media Online), vol. 4, no. 1, pp. 360–367, 2023, doi: 10.30865/klik.v4i1.1033.
D. Oktavia, Y. R. Ramadahan, and M. Minarto, “Analisis Sentimen Terhadap Penerapan Sistem E-Tilang Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM),” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 1, pp. 407–417, 2023, doi: 10.30865/klik.v4i1.1040.
A. F. I. Muhammad Imam Ghozali, Wibowo Harry Sugiharto, “Analisis Sentimen Pinjaman Online Di Media Sosial Twitter Menggunakan Metode Naive Bayes,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 33, no. 1, pp. 1–12, 2022, doi: https://doi.org/10.30865/klik.v3i6.936.
E. W. Sholeha, S. Yunita, R. Hammad, V. C. Hardita, and K. Kaharuddin, “Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor,” JTIM J. Teknol. Inf. dan Multimed., vol. 3, no. 4, pp. 203–208, 2022, doi: 10.35746/jtim.v3i4.178.
M. Hudha, E. Supriyati, and T. Listyorini, “Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan Dengan Metode Naïve Bayes Classifier,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 1–6, 2022, doi: 10.33387/jiko.v5i1.3376.
L. L. Kaamilah, “Analisis Kelompok Lansia Berdasarkan Kategori Usia Dengan Metode K-Means Clustering,” J. Ris. Ilmu Akunt., vol. 2, no. 2, pp. 1–14, 2023.
M. W. A. Putra, Susanti, Erlin, and Herwin, “Analisis Sentimen Dompet Elektronik Pada Twitter Menggunakan Metode Naïve Bayes Classifier,” IT J. Res. Dev., vol. 5, no. 1, pp. 72–86, 2020, doi: 10.25299/itjrd.2020.vol5(1).5159.
A. Noviriandini, H. Hermanto, and D. Ayu Ambarsari, “Analisis Tingkat Kepuasan Pengguna Aplikasi JMO (Jamsostek Mobile) Menggunakan Algoritma Naive Bayes Classifier,” Reputasi J. Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 33–37, 2023, doi: 10.31294/reputasi.v4i1.1986.
A. Halim and Andri Safuwan, “Analisis Sentimen Opini Warganet Twitter Terhadap Tes Screening Genose Pendeteksi Virus Covid-19 Menggunakan Metode Naïve Bayes Berbasis Particle Swarm Optimization,” J. Inform. Teknol. dan Sains, vol. 5, no. 1, pp. 170–178, 2023, doi: 10.51401/jinteks.v5i1.2229.
M. F. El Firdaus, N. Nurfaizah, and S. Sarmini, “Analisis Sentimen Tokopedia Pada Ulasan di Google Playstore Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 5, p. 1329, 2022, doi: 10.30865/jurikom.v9i5.4774.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Ulasan Pengguna Aplikasi Ajaib Menggunakan Metode Naïve Bayes
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Hilmy Zhafran Muflih, Allif Rizki Abdillah, Firman Noor Hasan
This work is licensed under a Creative Commons Attribution 4.0 International License.
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 Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment 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 acknowledgment 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 (Refer to The Effect of Open Access).
