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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Scientific Journal of Informatics
Fatihaturrahmah, Aisyah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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PREDIKSI SENTIMEN PENGGUNA SPOTIFY MENGGUNAKAN METODE NAÏVE BAYES STUDI KASUS ULASAN PENGGUNA DI PLAY STORE Fatihaturrahmah, Aisyah; Amanda Ardhani, Dhita; Putri Casanova, Musdalifa; Cahya Aulia, Syifa; Najwa Widasari, Yesya; Ditha Tania, Ken; Kurnia Sari, Winda
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14112

Abstract

Spotify merupakan salah satu platform layanan streaming music digital yang banyak digunakan, tetapi ulasan pengguna menunjukkan variasi tingkat kepuasan terhadap layanan yang tersedia. Penelitian ini berfokus pada analisis dan prediksi sentimen pengguna Spotify berdasarkan ulasan yang diperoleh dari Google Play Store dengan menggunakan metode Naïve Bayes. Penelitian ini mencakup beberapa proses, yaitu pengumpulan data, pra-pemrosesan teks (tokenisasi, normalisasi, penghapusan kata tidak bermakna, dan stemming) serta ekstraksi fitur menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Sentimen pengguna diklasifikasikan ke dalam dua kategori, yaitu positif dan negative. Evaluasi model dilakukan dengan menggunakan metrik akurasi, presisi recall dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan sentimen pengguna Spotify dengan akurasi sebesar 88,42%, precision 0,89, recall 0,74, dan F1-Score 0,79 (macro average). Selain itu, nilai Area Under Curve (AUC) sebesar 0,94 yang mengindikasikan bahwa model memiliki kemampuan klasifikasi yang sangat baik. Sehingga hasil ini menunjukkan bahwa pendekatan yang digunakan sudah sangat efektif dalam menganalisis sentimen pengguna dan dapat menjadi acuan bagi pengembang untuk meningkatkan kualitas terhadap layanan streaming music Spotify.
ANALISIS PENGARUH PENGGUNAAN METODE PEMBAYARAN PAYLATER TERHADAP POLA KONSUMTIF GENERASI MUDA: STUDI KASUS : MAHASISWA UNIVERSITAS SRIWIJAYA Fathoni, Fathoni; Ibrahim, Ali; Fatihaturrahmah, Aisyah; Cahya Aulia, Syifa; Ayuningtiyas, Pratiwi; Tri Zafira, Zahra
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14153

Abstract

A Hybrid Approach of Aspect-Based Sentiment Analysis and Knowledge Extraction for Evaluating Security Perceptions in Digital Payment Applications Fatihaturrahmah, Aisyah; Ditha Tania, Ken
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.31557

Abstract

Purpose: The rapid expansion of digital wallets in Indonesia has heightened concerns regarding user security and trust. This study evaluates user sentiment toward the security features of the DANA digital payment application using Aspect Sentiment Classification (ASC), a subtask of Aspect-Based Sentiment Analysis (ABSA). It aims to compare multiple classification models and generate structured, machine-readable sentiment outputs to support knowledge extraction and system integration. Methods: A total of 4,846 security-related reviews were collected from the Google Play Store using keyword-based filtering, supplemented by 3,000 unfiltered reviews for robustness evaluation. Sentiment labeling was performed using a hybrid rule-based and manual annotation approach. From 300 proportionally sampled reviews (150 positive and 150 negative), the validation achieved 0.8504 accuracy and a Cohen’s κ of 0.951, indicating near-perfect agreement. Five models—Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and IndoBERT—were evaluated using 5-fold stratified cross-validation with random oversampling to address class imbalance. Results: IndoBERT achieved the highest performance with 98% accuracy, an F1-score of 0.974, and an AUC-ROC of 0.996, followed by CNN and BiLSTM. Robustness testing across temporal (DANA June–October) and cross-domain (GoPay) datasets confirmed IndoBERT’s strong generalization with minimal F1-score variation. Novelty: Unlike previous ABSA studies that addressed multiple aspects, this research focuses exclusively on the security aspect, providing fine-grained insights into user trust. The integration of XML-based structured output enhances interpretability and interoperability in digital financial sentiment analysis, contributing to the development of more secure and transparent fintech ecosystems.
Co-Authors Ali Ibrahim Amanda Ardhani, Dhita Ayuningtiyas, Pratiwi Cahya Aulia, Syifa Fathoni Fathoni Ken Ditha Tania Kurnia Sari, Winda Najwa Widasari, Yesya Putri Casanova, Musdalifa Tri Zafira, Zahra