ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES
DOI:
https://doi.org/10.23960/jitet.v14i1.8776Abstract Views: 87 File Views: 57
Keywords:
sentiment analysis; Flo application; Naive Bayes; text mining; mHealth.Abstract
Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo.
Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
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