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All Journal Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura
Nabilla Meilyta
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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ANALISIS SENTIMEN PENGGUNA APLIKASI BLIBLI MENGGUNAKAN TEXT MINING BERDASARKAN ULASAN GOOGLE PLAY STORE Nabilla Meilyta; Ahmad Ferdian Shobur
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3316

Abstract

The growth of e-commerce in Indonesia has driven an increase in the volume of user reviews that openly express satisfaction and complaints. Manual analysis of large-scale reviews is inefficient, necessitating an automated computational approach. This study aims to classify user sentiment of the Blibli application based on 1,060 Google Play Store reviews using text mining and the Multinomial Naive Bayes algorithm with TF-IDF weighting, as well as to identify sentiment patterns per service aspect. Of the 350 manually labeled reviews, 245 were used for training and 105 for testing. Model evaluation results show an accuracy of 86.67%, precision of 82.54%, recall of 94.55%, and F1-score of 88.14%. The overall sentiment distribution across 1,060 reviews indicates 61.1% positive and 38.9% negative. Aspect-based analysis reveals that UI/UX is the only aspect with dominant positive sentiment (70.6%), while Application Performance (57.1%), Payment Features (63.9%), Customer Service (66.7%), and Delivery Experience (67.3%) are dominated by negative sentiment. These findings can serve as data-driven evaluation material for improving Blibli application services.
ANALISIS SENTIMEN PENGGUNA APLIKASI BLIBLI MENGGUNAKAN TEXT MINING BERDASARKAN ULASAN GOOGLE PLAY STORE Nabilla Meilyta; Ahmad Ferdian Shobur
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3316

Abstract

The growth of e-commerce in Indonesia has driven an increase in the volume of user reviews that openly express satisfaction and complaints. Manual analysis of large-scale reviews is inefficient, necessitating an automated computational approach. This study aims to classify user sentiment of the Blibli application based on 1,060 Google Play Store reviews using text mining and the Multinomial Naive Bayes algorithm with TF-IDF weighting, as well as to identify sentiment patterns per service aspect. Of the 350 manually labeled reviews, 245 were used for training and 105 for testing. Model evaluation results show an accuracy of 86.67%, precision of 82.54%, recall of 94.55%, and F1-score of 88.14%. The overall sentiment distribution across 1,060 reviews indicates 61.1% positive and 38.9% negative. Aspect-based analysis reveals that UI/UX is the only aspect with dominant positive sentiment (70.6%), while Application Performance (57.1%), Payment Features (63.9%), Customer Service (66.7%), and Delivery Experience (67.3%) are dominated by negative sentiment. These findings can serve as data-driven evaluation material for improving Blibli application services.
Co-Authors Ahmad Ferdian Shobur