WordPress Application Satisfaction Level Sentiment Analysis Using K-Nearest Neighbor and Naive Bayes Methods
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Abstract
User satisfaction reflects emotions when comparing services received with expectations, so understanding user satisfaction is important for app development. This research aims to evaluate user satisfaction with WordPress apps on the Google Play Store and identify areas for improvement. Sentiment analysis with KNN and Naïve bayes algorithms as the method used to extract information from 5,000 user reviews downloaded from Google Play Store,. The results showed the majority of reviews had positive sentiments, with Naïve Bayes providing better results than KNN, achieving 88% accuracy, 89.45% precision, 88% recall, and 83% F1-Score on a 90:10 data split. The word cloud of positive reviews featured words such as “great”, “good”, “helpful”, “app”, and “good”, reflecting user satisfaction with the ease and benefits of the app, while negative reviews featured words such as “difficult”, “try”, and “fail” indicating technical difficulties and user dissatisfaction. This study concludes that WordPress apps have provided a satisfactory experience for most users, but some technical areas need improvement. The results of this study will provide valuable information for app developers in efforts to improve service quality and the app's reputation
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References
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[32] D. P. Wijaya, L. D. Murti, M. R. Rachman, D. Arsip, and K. Bandung, “Recall dan Precision pada Online Public Access Catalog (OPAC) Dinas Arsip dan Perpustakaan Kota Bandung Didik,” vol. 24, no. 1, 2022.
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[2] N. C. Agustina, D. Herlina Citra, W. Purnama, C. Nisa, and A. Rozi Kurnia, “MALCOM: Indonesian Journal of Machine Learning and Computer Science The Implementation of Naïve Bayes Algorithm for Sentiment Analysis of Shopee Reviews on Google Play Store Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Goo,” vol. 2, pp. 47–54, 2022.
[3] S. Rahayu, Y. MZ, J. E. Bororing, and R. Hadiyat, “Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, pp. 98–106, Jun. 2022, doi: 10.29408/edumatic.v6i1.5433.
[4] T. A. Sari, E. Sinduningrum, and F. Noor Hasan, “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Sentimen Ulasan Pelanggan Pada Aplikasi Fore Coffee Menggunakan Metode Naïve Bayes,” Media Online), vol. 3, no. 6, pp. 773–779, 2023, doi: 10.30865/klik.v3i6.884.
[5] M. N. Muttaqin and I. Kharisudin, “Analisis Sentimen Pada Ulasan Aplikasi Gojek Menggunakan Metode Support Vector Machine dan K Nearest Neighbor,” UNNES Journal of Mathematics, vol. 10, no. 2, pp. 22–27, 2021.
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[7] P. Hou, L. Zhou, and Y. Yang, “Density clustering method based on k-nearest neighbor propagation,” J Phys Conf Ser, vol. 2858, no. 1, 2024, doi: 10.1088/1742-6596/2858/1/012041.
[8] R. A. Safitri and R. Hidayati, “Komparasi Metode K-Nearest Neighbor dan Naïve Bayes untuk Mengklasifikasi Resiko Diabetes Di Posbindu Desa Bulupitu,” SMATIKA JURNAL, vol. 14, no. 02, pp. 297–303, Dec. 2024, doi: 10.32664/smatika.v14i02.1350.
[9] A. Asro’i and H. Februariyanti, “Analisis Sentimen Pengguna Twitter Terhadap Perpanjangan Ppkm Menggunakan Metode K-Nearest Neighbor,” Jurnal Khatulistiwa Informatika, vol. 10, no. 1, pp. 17–24, 2022, doi: 10.31294/jki.v10i1.12624.
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[11] Y. Yuliska and K. U. Syaliman, “Peningkatan Akurasi K-Nearest Neighbor Pada Data Index Standar Pencemaran Udara Kota Pekanbaru,” IT Journal Research and Development, vol. 5, no. 1, pp. 11–18, Jul. 2020, doi: 10.25299/itjrd.2020.vol5(1).4680.
[12] V. P. Ramadhan and G. M. Namung, “Klasterisasi Komentar Cyberbullying Masyarakat di Instagram berdasarkan K-Means Clustering,” J-INTECH, vol. 11, no. 1, pp. 32–39, Jul. 2023, doi: 10.32664/j-intech.v11i1.846.
[13] A. F. N. Azizah and V. P. Ramadhan, “Komparasi Naïve Bayes dan K-NN Dalam Analisis Sentimen di Twitter Terhadap Kemenangan Paslon 02,” J-INTECH, vol. 12, no. 02, pp. 228–237, Dec. 2024, doi: 10.32664/j-intech.v12i02.1305.
[14] N. Nurfaizah and S. R. Hidayat, “Sentimen Analisis Pengguna Produk Ponsel Menggunakan Algoritma Naïve Bayes,” Journal of Information System Management (JOISM), vol. 6, no. 1, pp. 10–14, 2024, doi: 10.24076/joism.2024v6i1.1625.
[15] R. Merdiansah and A. Ali Ridha, “Sentiment Analysis of Indonesian X Users Regarding Electric Vehicles Using IndoBERT,” Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI, vol. 7, no. 1, pp. 221–228, 2024.
[16] I. S. H. Almaqbali, F. M. A. Al Khufairi, M. S. Khan, A. Z. Bhat, and I. Ahmed, “Web Scrapping: Data Extraction from Websites,” Journal of Student Research, pp. 1–4, 2020, doi: 10.47611/jsr.vi.942.
[17] N. Y. Pradipta and H. Soetanto, “Sentiment Classification of General Election 2024 News Titles on Detik. com Online Media Website Using Multinominal Naive Bayes Method,” Journal of Applied Science, Engineering …, vol. 6, no. 1, 2024.
[18] N. Cahyono and Anggista Oktavia Praneswara, “Analisis Sentimen Ulasan Aplikasi TikTok Shop Seller Center di Google Playstore Menggunakan Algoritma Naive Bayes,” Indonesian Journal of Computer Science, vol. 12, no. 6, pp. 3925–3940, 2023, doi: 10.33022/ijcs.v12i6.3473.
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[21] A. Özçift, K. Akarsu, F. Yumuk, and C. Söylemez, “Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish,” Automatika, vol. 62, no. 2, pp. 226–238, 2021, doi: 10.1080/00051144.2021.1922150.
[22] O. Manullang, C. Prianto, and N. H. Harani, “Analisis Sentimen Untuk Memprediksi Hasil Calon Pemilu Presiden Menggunakan Lexicon Based Dan Random Forest,” Jurnal Ilmiah Informatika, vol. 11, no. 02, pp. 159–169, 2023, doi: 10.33884/jif.v11i02.7987.
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[24] J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 247–256, 2024, doi: 10.57152/malcom.v4i1.1066.
[25] F. T. Admojo and Ahsanawati, “Klasifikasi Aroma Alkohol Menggunakan Metode KNN,” Indonesian Journal of Data and Science, vol. 1, no. 2, pp. 34–38, 2020, doi: 10.33096/ijodas.v1i2.12.
[26] Q. A. A’yuniyah and M. Reza, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Di Sma Negeri 15 Pekanbaru,” Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), vol. 3, no. 1, pp. 39–45, 2023, doi: 10.57152/ijirse.v3i1.484.
[27] S. A. Utiarahman and A. M. M. Pratama, “Analisis Perbandingan KNN, SVM, Decision Tree dan Regresi Logistik Untuk Klasifikasi Obesitas Multi Kelas,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 3137–3146, 2024, doi: 10.30865/klik.v4i6.1871.
[28] H. F. Putro, R. T. Vulandari, and W. L. Y. Saptomo, “Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan,” Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), vol. 8, no. 2, 2020, doi: 10.30646/tikomsin.v8i2.500.
[29] M. Yusuf, “Analisis Sentimen Data Twitter Terhadap Bakal Calon Presiden Republik Indonesia 2024 Dengan Metode Backpropagation,” 2022.
[30] F. Aziz, P. Ishak, and S. Abasa, “Klasifikasi Depresi Menggunakan Support Vector Machine: Pendekatan Berbasis Data Text Mining,” Journal Pharmacy and Application of Computer Sciences, vol. 2, no. 2, pp. 33–38, 2024, doi: 10.59823/jopacs.v2i2.53.
[31] S. A. Pratiwi, A. Fauzi, S. Arum, P. Lestari, and Y. Cahyana, “KLIK: Kajian Ilmiah Informatika dan Komputer Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree,” Media Online, vol. 4, no. 4, pp. 2381–2388, 2024, doi: 10.30865/klik.v4i4.1681.
[32] D. P. Wijaya, L. D. Murti, M. R. Rachman, D. Arsip, and K. Bandung, “Recall dan Precision pada Online Public Access Catalog (OPAC) Dinas Arsip dan Perpustakaan Kota Bandung Didik,” vol. 24, no. 1, 2022.
[33] M. Pirnau et al., “Content Analysis Using Specific Natural Language Processing Methods for Big Data,” Electronics (Switzerland), vol. 13, no. 3, pp. 1–22, 2024, doi: 10.3390/electronics13030584.
[34] M. Iqbal, A. Davy Wiranata, R. Suwito, and R. Faiz Ananda, “Perbandingan Algoritma Naïve Bayes, KNN, dan Decision Tree terhadap Ulasan Aplikasi Threads dan Twitter,” Media Online, vol. 4, no. 3, pp. 1799–1807, 2023, doi: 10.30865/klik.v4i3.1402.