Klasifikasi Metode Naïve Bayes pada Ulasan Pengguna Aplikasi Dazzcam untuk Pengeditan Foto Vintage di App Store
Article Sidebar
Main Article Content
Abstract
The rapid growth of mobile applications has increased the importance of user-generated reviews as a source of information for evaluating application quality and user satisfaction. Dazzcam, a photo editing application known for its vintage-style filters, has gained significant popularity among iOS users. This study aims to classify user reviews from the App Store into positive and negative sentiment categories using the Naïve Bayes algorithm and to evaluate the performance of the model. A total of 911 reviews were collected and divided into training and testing datasets with a ratio of 80:20. The research methodology includes data preprocessing, feature extraction using TF-IDF, and classification using Naïve Bayes, followed by evaluation with a confusion matrix. The results show that 712 reviews were classified as positive and 199 as negative, with an accuracy of 79.78%, precision of 79.89%, recall of 79.78%, and F1-score of 79.53%. These findings indicate that the Naïve Bayes algorithm demonstrates good performance and can be effectively utilized for sentiment analysis of application reviews.
Article Details
Section
How to Cite
References
Al Rasyid, R., & Ningsih, D. H. U. (2024). Penerapan Algoritma TF-IDF dan Cosine Similarity untuk Query Pencarian Pada Dataset Destinasi Wisata. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 170–178. https://doi.org/10.35870/jtik.v8i1.1416
Anisah, S., & Irwansyah, I. (2025). Analisis Data Mining untuk Klasifikasi Kafe Populer di Jakarta Menggunakan Decision Tree dan Visualisasi dengan Tableau. Jutisi : Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, 14(2), 890. https://doi.org/10.35889/jutisi.v14i2.2660
Ari Rama Novryadi, Irwansyah, & Moh Shidqon. (2026). Analisis Sentimen Masyarakat Terhadap Kinerja Presiden Indonesia Joko Widodo Periode Kedua Menggunakan Metode Naïve Bayes dan SVM. DIGINTEL-AI : DIGital INnovation and inTELligence – AI, 1(1), 11–24. https://doi.org/10.66217/digintel-ai.v1i1.2
Aushofi, M., Irwansyah, & Moh Shidqon. (2026). Implementasi Data Mining Untuk Menganalisis Pola Penimbangan Sampah Menggunakan Algoritma Apriori. DIGINTEL-AI : DIGital INnovation and inTELligence – AI, 1(1), 1–10. https://doi.org/10.66217/digintel-ai.v1i1.1
Bagaskara, W., Pusparini, N. N., & Irwansyah, I. (2024). Klasifikasi Penjadwalan Kerja Perawatan Air Conditioner (Ac) Menggunakan Algoritma Decision Tree (C4.5) Pada Pt Xyz. Infotech: Journal of Technology Information, 10(1), 11–20. https://doi.org/10.37365/jti.v10i1.240
Fadil Firmansyah, Irwansyah, & Agus Budiyantara. (2026). Analisis Pola Pembelian Konsumen Di Rumah Makan Tepi Laut Baubau Menggunakan Algoritma Apriori. DIGINTEL-AI : DIGital INnovation and inTELligence – AI, 1(1), 25–36. https://doi.org/10.66217/digintel-ai.v1i1.3
Garijo, D., Ménager, H., Hwang, L., Trisovic, A., Hucka, M., Morrell, T., Allen, A., Task Force on Best Practices for Software Registries, & SciCodes Consortium. (2022). Nine best practices for research software registries and repositories. PeerJ Computer Science, 8, e1023. https://doi.org/10.7717/peerj-cs.1023
Irwansyah, I., Dittyata, R., Rizal, R., & Wiyono, W. (2024). Optimalisasi Klasifikasi Uji Emisi Sepeda Motor Menggunakan Algoritma Naïve Bayes. Infotech: Journal of Technology Information, 10(2), 337–242. https://doi.org/10.37365/jti.v10i2.327
Irwansyah, I., Wiranata, A. D., & M, T. T. (2023). Komparasi Algoritma Decision Tree, Naive Bayes Dan K-Nearest Neighbor Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta. Infotech: Journal of Technology Information, 9(2), 193–198. https://doi.org/10.37365/jti.v9i2.203
Irwansyah, I., Yudhana, A., & Fadlil, A. (2025). Klasifikasi Jenis Kulit Wajah Menggunakan Algoritma Random Forest. Infotech: Journal of Technology Information, 11(2), 229–236. https://doi.org/10.37365/jti.v11i2.423
Kastrati, Z., Dalipi, F., Imran, A. S., Pireva Nuci, K., & Wani, M. A. (2021). Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986
Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering, 24(6), 3316–3355. https://doi.org/10.1007/s10664-019-09706-9
Nugroho, R. R., & Usman, O. (2025). Analyzing Mobile App Design’s Impact on Instagram User Experience and Satisfaction. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 3(1), 202–215. https://doi.org/10.21009/ISC-BEAM.013.13
Ozimek, P., Lainas, S., Bierhoff, H.-W., & Rohmann, E. (2023). How photo editing in social media shapes self-perceived attractiveness and self-esteem via self-objectification and physical appearance comparisons. BMC Psychology, 11(1), 99. https://doi.org/10.1186/s40359-023-01143-0
Perrig, S. A. C., Ueffing, D., Opwis, K., & Brühlmann, F. (2023). Smartphone app aesthetics influence users’ experience and performance. Frontiers in Psychology, 14, 1113842. https://doi.org/10.3389/fpsyg.2023.1113842
Rahmawati, L., & Santoso, D. B. (2023). Implementasi Metode Naive Bayes Untuk Klasifikasi Ulasan Aplikasi E-Commerce Tokopedia. INTECOMS: Journal of Information Technology and Computer Science, 6(1), 116–124. https://doi.org/10.31539/intecoms.v6i1.5515
Rieuwpassa, J. A., Sugito, S., & Widiharih, T. (2024). Implementasi Metode Naive Bayes Classifier Untuk Klasifikasi Sentimen Ulasan Pengguna Aplikasi Netflix Pada Google Play. Jurnal Gaussian, 12(3), 362–371. https://doi.org/10.14710/j.gauss.12.3.362-371
Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2021). Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning. Expert Systems with Applications, 181, 115111. https://doi.org/10.1016/j.eswa.2021.115111
Salsa Billa Permana Putri, Irwansyah, & Tri M, T. (2026). Implementasi Algoritma K-NN Pada Sosial Media X Untuk Analisis Sentimen Pengalaman Warganet Tinggal Di Luar Negeri. DIGINTEL-AI : DIGital INnovation and inTELligence – AI, 1(1), 37–49. https://doi.org/10.66217/digintel-ai.v1i1.4