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All Journal Jurnal Teknologi dan Manajemen Informatika Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Sistem Informasi dan Aplikasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Decode: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Lidya Maukar, Anastasia
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods Nabil, Muh; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16311

Abstract

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.
Sentiment Analysis E-Wallet Application Services Using the Support Vector Machine and Long Short-Term Memory Methods Arya Darmansyah, Mochammad Dzikri; Vitianingsih, Anik Vega; Lidya Maukar, Anastasia; Yuliani, SY.; Fitri Ana Wati, Seftin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apedaz75

Abstract

The rapid growth of financial technology services in Indonesia has increased the volume of user reviews, yet their utilization for sentiment-based insights remains limited in the e-wallet sector. This study compares the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) in classifying the sentiment of 3,185 DANA e-wallet reviews collected from the Google Play Store and Instagram. The research process includes text preprocessing, lexicon-based labeling, and feature extraction using TF-IDF for SVM and word embeddings for LSTM. Model evaluation is conducted using a confusion matrix based on accuracy, precision, and recall, without inferential statistical testing. The results show that LSTM outperforms SVM, achieving an accuracy of 86.66%, a recall of 81.86%, and a precision of 82.09%, while the best SVM variant with an RBF kernel attains an accuracy of 84.93%. This study contributes by identifying key service-related factors influencing user satisfaction and dissatisfaction and by providing practical, sentiment-based insights to support service quality improvement. The novelty lies in the multi-platform analysis of Indonesian e-wallet reviews and the direct comparison of classical machine learning and deep learning approaches without statistical hypothesis testing. These findings confirm the effectiveness of deep learning for sentiment analysis of unstructured Indonesian text.
Co-Authors Anik Vega Vitianingsih Arumsari, Andini Dwi Arya Darmansyah, Mochammad Dzikri Budi Suprio, Yoyon Arie Damayanti, Erika DWI CAHYONO Fardhan Maulana, Abelardi Febrian Rusdi, Jack Fitri Ana Wati, Seftin Hamidan, Rusdi Hengki Suhartoyo, Hengki Jazaudhi’fi, Ahmad Mustafa, Zulfikar Amirul Nabil, Muh Ongko, Bagus Kustiono Pujiono, Halim Putra Selian, Rasyid Ihsan Rahmansyah, Ragada Slamet Kacung, Slamet Slamet Winardi Warsito Sujatmiko, Achmad Wati, Seftin Fitri Ana Yasin, Verdi Yudi Kristyawan, Yudi Yuliani, SY. Yunior, Kevin Heryadi