Sentiment Analysis of Indonesian-Language Plantix Application Reviews for Plant Disease Diagnosis Using Naive Bayes Methods
DOI:
https://doi.org/10.64878/jistics.v1i2.12Keywords:
Sentiment analysis, Plantix, Naive Bayes, TF-IDF, text-mining, Play Store ReviewAbstract
The Plantix app is one of the digital solutions widely utilized by farmers to diagnose plant diseases through image recognition technology and support from the user community. The large amount of Indonesian-language customer feedback on Google's application can be a valuable source of information for assessing the effectiveness and user satisfaction of this application. This study uses naive Bayes algorithms to classify sentiments based on the Plantix application's customer feedback. The dataset was obtained by implementing web scraping techniques with the Google Play scraper library, resulting in more than 354 reviews. Data preprocessing stages include case folding, text cleaning, tokenization, stemming using the Sastrawi library, and text transformation into numerical form using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Sentiment labels are determined based on user star ratings, which are divided into three categories: positive, neutral, and negative. The Multinomial Naive Bayes algorithm performs the classification process and is assessed through the K-fold Cross Validation technique (K=10). The assessment results show that the model achieves the highest accuracy of 75.10% and F1-score of 72.35% with the shuffle sampling method, which falls into the category of fairly good classification. This study demonstrates that naive Bayes methodology is effectively used in sentiment analysis of text-based agricultural application reviews in Bahasa Indonesia.
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References
L. Shang, T. Heckelei, M. K. Gerullis, J. Börner, and S. Rasch, “Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction,” Agric. Syst., vol. 190, 2021, doi: 10.1016/j.agsy.2021.103074.
N. Petrellis, “Plant disease diagnosis for smart phone applications with extensible diseases,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091952.
K. S. Nugroho, A. Y. Sukmadewa, H. Wuswilahaken Dw, F. A. Bachtiar, and N. Yudistira, “BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews,” ACM Int. Conf. Proceeding Ser., pp. 258–264, 2021, doi: 10.1145/3479645.3479679.
S. Siswanto, Z. Mar’ah, A. S. D. Sabir, T. Hidayat, F. A. Adhel, and W. S. Amni, “The Sentiment Analysis Using Naïve Bayes with Lexicon-Based Feature on TikTok Application,” J. Varian, vol. 6, no. 1, pp. 89–96, 2022, doi: 10.30812/varian.v6i1.2205.
D. Surya Sayogo, B. Irawan, and A. Bahtiar, “Analisis Sentimen Ulasan Instagram Di Google Play Store Menggunakan Algoritma Naïve Bayes,” JATI (Jurnal Mhs. Tek. Inform.), vol. 7, no. 6, pp. 3314–3319, 2024, doi: 10.36040/jati.v7i6.8178.
H. N. Zuhdi and B. Prasetiyo, “Sentiment Analysis On Ipusnas Application Reviews In Google Play Store Using Naive Bayes Classifier Analisis Sentimen pada Ulasan Aplikasi iPusnas di Google Play Store Menggunakan Naive Bayes Classifier,” vol. 5, no. 1, pp. 12–19, 2025.
H. H. Mubaroroh, H. Yasin, and A. Rusgiyono, “Analisis Sentimen Data Ulasan Aplikasi Ruangguru Pada Situs Google Play Menggunakan Algoritma Naïve Bayes Classifier Dengan Normalisasi Kata Levenshtein Distance,” J. Gaussian, vol. 11, no. 2, pp. 248–257, 2022, doi: 10.14710/j.gauss.v11i2.35472.
V. Bobicev and M. Sokolova, “Inter-annotator agreement in sentiment analysis: Machine learning perspective,” Int. Conf. Recent Adv. Nat. Lang. Process. RANLP, vol. 2017-Septe, pp. 97–102, 2017, doi: 10.26615/978-954-452-049-6_015.
A. Santosa, I. Purnamasari, and Mayasari Rini, “Pengaruh Stopword Removal dan Stemming Terhadap Performa Klasifikasi Teks Komentar Kebijakan New Normal Menggunakan Algoritma LSTM,” J. Sains Komput. Inform., vol. 6, pp. 81–93, 2022.
K. P. Harmandini and K. M. L, “Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment,” Sinkron, vol. 8, no. 2, pp. 929–937, 2024, doi: 10.33395/sinkron.v8i2.13376.
A. M. Kibriya, E. Frank, B. Pfahringer, and G. Holmes, “Multinomial naive bayes for text categorization revisited,” Lect. Notes Artif. Intell. (Subseries Lect. Notes Comput. Sci.), vol. 3339, pp. 488–499, 2004, doi: 10.1007/978-3-540-30549-1_43.
D. Wilimitis and C. G. Walsh, “Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial,” Jmir Ai, vol. 2, p. e49023, 2023, doi: 10.2196/49023.
O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-56706-x.
D. Maz, “A Crossing-Symmetric OPE Inversion Formula.”
B. Kostic, R. Crastes, S. Hess, and J. Scheiner, “Uncovering life-course patterns with causal discovery and survival analysis.”
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Copyright (c) 2025 Virzza Rahmaliyadi, Maula Muhammad Maridjan
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