ANALISIS SENTIMEN KEPUASAN PENGGUNA APLIKASI FLO HEALTH PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER

Azzahra Shabbani Putri; Umi Hayati; Riri Narasati; Ade Rizki Rinaldi

Authors

Azzahra Shabbani Putri STMIK IKMI Cirebon, Indonesia
Umi Hayati STMIK IKMI Cirebon, Indonesia
Riri Narasati STMIK IKMI Cirebon, Indonesia
Ade Rizki Rinaldi STMIK IKMI Cirebon, Indonesia

Keywords:

analisis sentimen, Naïve Bayes, mobile health

Abstract

Perkembangan aplikasi mobile health (mHealth) memberikan kemudahan bagi pengguna dalam memantau kondisi kesehatan secara mandiri, salah satunya melalui aplikasi Flo Health yang digunakan untuk melacak siklus menstruasi dan kesehatan reproduksi wanita. Ulasan pengguna pada Google Play Store menjadi sumber informasi penting untuk mengetahui tingkat kepuasan serta pengalaman pengguna dalam menggunakan aplikasi tersebut. Namun, jumlah ulasan yang sangat banyak menyulitkan proses analisis secara manual, sehingga diperlukan pendekatan otomatis berbasis machine learning untuk mengidentifikasi sentimen pengguna secara lebih efisien. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo Health serta mengevaluasi kinerja algoritma Multinomial Naïve Bayes dalam mengklasifikasikan sentimen ke dalam kategori positif, negatif, dan netral. Data penelitian diperoleh melalui proses web scraping pada Google Play Store yang menghasilkan 9.442 ulasan pengguna. Tahap prapemrosesan dilakukan melalui beberapa proses, yaitu cleaning, case folding, normalisasi, tokenisasi, stopword removal, dan stemming untuk meningkatkan kualitas data teks. Selanjutnya dilakukan pembobotan kata menggunakan metode Term Frequency–Inverse Document Frequency (TF–IDF) untuk mengubah data teks menjadi representasi numerik. Dataset kemudian dibagi menjadi data latih sebesar 80% dan data uji sebesar 20% sebelum diterapkan algoritma Multinomial Naïve Bayes untuk proses klasifikasi. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa sebagian besar ulasan pengguna memiliki sentimen positif yang berkaitan dengan kemudahan penggunaan dan fitur pelacakan siklus menstruasi, sedangkan sentimen negatif umumnya berkaitan dengan kendala teknis aplikasi dan isu privasi data. Penelitian ini menunjukkan bahwa algoritma Multinomial Naïve Bayes efektif digunakan untuk analisis sentimen pada ulasan aplikasi kesehatan digital.

References

E. K. Ameyaw, A. P. Adusei, and O. Ezezika, “Effectiveness of mHealth Apps for Maternal Health Care Delivery: Systematic Review of Systematic Reviews,” Journal of Medical Internet Research, vol. 26, p. e49510, 2024.

C. Lee and et al., “Mobile Health Apps for Breast Cancer: Content Analysis and Quality Assessment,” JMIR mHealth and uHealth, vol. 11, p. e43522, 2023.

M. Thanet, E. Wibowo, and P. Santosa, “Mobile health application for Thai women: investigation and model,” BMC Medical Informatics and Decision Making, vol. 22, p. 202, 2022.

M. Haoues, R. Mokni, and A. Sellami, “Machine learning for mHealth apps quality evaluation: An approach based on user feedback analysis,” Software Quality Journal, 2023.

J. Li et al., “Mining user reviews from hypertension management mobile health apps to explore factors influencing user satisfaction and their asymmetry: Comparative study,” JMIR mHealth and uHealth, vol. 12, p. e55199, 2024.

M. Haoues, R. Mokni, and A. Sellami, “Machine learning for mHealth apps quality evaluation: An approach based on user feedback analysis,” Software Quality Journal, 2023.

J. Li et al., “Mining user reviews from hypertension management mobile health apps to explore factors influencing user satisfaction and their asymmetry: Comparative study,” JMIR mHealth and uHealth, vol. 12, p. e55199, 2024.

G. A. Id and G. Silahtaroglu, “Insights into mobile health application market via a content analysis of marketplace data with machine learning,” pp. 1–22, 2021, doi: 10.1371/journal.pone.0244302.

M. B. Id et al., “ACCU 3 RATE : A mobile health application rating scale based on user reviews,” pp. 1–24, 2021, doi: 10.1371/journal.pone.0258050.

E. L. Funnell, B. Spadaro, N. Martin-key, T. Metcalfe, S. Bahn, and S. Bahn, “mHealth Solutions for Mental Health Screening and Diagnosis : A Review of App User Perspectives Using Sentiment and Thematic Analysis,” vol. 13, no. April, pp. 1–17, 2022, doi: 10.3389/fpsyt.2022.857304.

T. Malik, A. J. Ambrose, C. Sinha, and T. Malik, “Evaluating User Feedback for an Artificial Intelligence – Enabled , Cognitive Behavioral Therapy – Based Mental Health App ( Wysa ): Qualitative Thematic Analysis Corresponding Author :,” vol. 9, 2022, doi: 10.2196/35668.

A. S. Review, “Twenty Years of Machine-Learning-Based Text Classification : A Systematic Review,” pp. 1–28, 2023.

A. Ulinuha, E. Majid, R. Nuari, U. T. Indonesia, and B. Lampung, “PERFORMANCE COMPARISON OF BERT METRICS AND CLASSICAL MACHINE LEARNING MODELS ( SVM , NAIVE BAYES ) FOR SENTIMENT ANALYSIS PERBANDINGAN KINERJA METRIK BERT DAN MODEL MACHINE LEARNING KLASIK ( SVM , NAIVE BAYES ) UNTUK,” vol. 10, no. 2, pp. 741–752, 2025.

A. Bahar, T. Astuti, P. Arsi, and U. A. Purwokerto, “PERFORMANCE COMPARISON OF SVM , NAIVE BAYES , AND LOGISTIC REGRESSION CLASSIFICATION ALGORITHMS IN ANALYZING NOICE APP USER PERBANDINGAN KINERJA ALGORITMA KLASIFIKASI SVM , NAIVE BAYES , DAN LOGISTIC REGRESSION DALAM ANALISIS ULASAN PENGGUNA,” vol. 5, no. 4, pp. 469–477, 2024.

C. H. A. Vasconcelos and H. Wang, “A large scale analysis of mHealth app user reviews,” Empirical Software Engineering, 2022.

G. A. Id and G. Silahtaroglu, “Insights into mobile health application market via a content analysis of marketplace data with machine learning,” pp. 1–22, 2021, doi: 10.1371/journal.pone.0244302.

M. B. Id et al., “ACCU 3 RATE : A mobile health application rating scale based on user reviews,” pp. 1–24, 2021, doi: 10.1371/journal.pone.0258050.

E. L. Funnell, B. Spadaro, N. Martin-key, T. Metcalfe, S. Bahn, and S. Bahn, “mHealth Solutions for Mental Health Screening and Diagnosis : A Review of App User Perspectives Using Sentiment and Thematic Analysis,” vol. 13, no. April, pp. 1–17, 2022, doi: 10.3389/fpsyt.2022.857304.

T. Malik, A. J. Ambrose, C. Sinha, and T. Malik, “Evaluating User Feedback for an Artificial Intelligence – Enabled , Cognitive Behavioral Therapy – Based Mental Health App ( Wysa ): Qualitative Thematic Analysis Corresponding Author :,” vol. 9, 2022, doi: 10.2196/35668.

A. S. Review, “Twenty Years of Machine-Learning-Based Text Classification : A Systematic Review,” pp. 1–28, 2023.

A. Rass, K. Tammi, and G. Demidova, “Mechatronics Technology and Transportation Sustainability,” pp. 10–12, 2022.

M. Cristina, H. Lee, J. Braet, and J. Springael, “applied sciences Performance Metrics for Multilabel Emotion Classification : Comparing Micro , Macro , and Weighted F1-Scores,” 2024.

A. Ulinuha, E. Majid, R. Nuari, U. T. Indonesia, and B. Lampung, “PERFORMANCE COMPARISON OF BERT METRICS AND CLASSICAL MACHINE LEARNING MODELS ( SVM , NAIVE BAYES ) FOR SENTIMENT ANALYSIS PERBANDINGAN KINERJA METRIK BERT DAN MODEL MACHINE LEARNING KLASIK ( SVM , NAIVE BAYES ) UNTUK,” vol. 10, no. 2, pp. 741–752, 2025.

J. Dąbrowski, E. Letier, A. Perini, and A. Susi, “Analysing app reviews for software engineering: a systematic literature review,” Empirical Software Engineering, vol. 27, no. 2, 2022, doi: 10.1007/s10664-021-10065-7.

A. Yasin, R. Fatima, A. Nauman, and Z. Wei, “Python data odyssey : Mining user feedback from google play store ✩,” Data in Brief, vol. 54, p. 110499, 2024, doi: 10.1016/j.dib.2024.110499.

M. A. Palomino, “applied sciences Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis,” 2022.

Rianto, A. B. Mutiara, E. P. Wibowo, and P. I. Santosa, “Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation,” Journal of Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00413-1.

N. Umaira, C. Mohd, and N. A. Shafie, “Performance of TF-IDF for Text Classification Reviews on Google Play Store : Shopee,” vol. 9, no. 2, 2024, doi: 10.24191/jcrinn.v9i2.410.

M. I. Alfarizi, L. Syafaah, and M. Lestandy, “Emotional Text Classification Using TF-IDF ( Term Frequency-Inverse Document Frequency ) And LSTM ( Long Short-Term Memory ),” vol. 10, no. 2, pp. 225–232, 2022.

M. Cristina, H. Lee, J. Braet, and J. Springael, “applied sciences Performance Metrics for Multilabel Emotion Classification : Comparing Micro , Macro , and Weighted F1-Scores,” 2024.

A. Rass, K. Tammi, and G. Demidova, “Mechatronics Technology and Transportation Sustainability,” pp. 10–12, 2022.

J. Opitz, “A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice,” vol. 12, no. 2018, pp. 820–836, 2024.

A. Bahar, T. Astuti, P. Arsi, and U. A. Purwokerto, “PERFORMANCE COMPARISON OF SVM , NAIVE BAYES , AND LOGISTIC REGRESSION CLASSIFICATION ALGORITHMS IN ANALYZING NOICE APP USER PERBANDINGAN KINERJA ALGORITMA KLASIFIKASI SVM , NAIVE BAYES , DAN LOGISTIC REGRESSION DALAM ANALISIS ULASAN PENGGUNA,” vol. 5, no. 4, pp. 469–477, 2024.

M. Enjeli and A. Wijaya, “Analisis Sentimen Pengunaaan Aplikasi Kinemaster Menggunakan Metode Naive Bayes,” vol. 2, pp. 89–98, 2024.