Article Per Year (5 Year)
p-Index From 2021 - 2026
3.138
P-Index
This Author published in this journals
All Journal Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ICT : Information Communication & Technology Vygotsky: Jurnal Pendidikan Matematika dan Matematika Infotekmesin Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Multimedia Trend and Technology Jurnal Komunikasi Global Jurnal Informatika: Jurnal Pengembangan IT Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Jurnal Informatika
Primandani Arsi
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Primandani Arsi; Reza Arief Firmanda; Iphang Prayoga; Pungkas Subarkah
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification Aulia Dian Agustina; Primandani Arsi; Pungkas Subarkah; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 5 No. 1 (2026): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v5i1.82

Abstract

This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.
Co-Authors Akhmad Mustolih Aldi Setia Pambudi Ali Nur Ikhsan Andi Dwi Riyanto Arbangi Puput Sabaniyah Aulia Dian Agustina Azizan Nurhakim Azizan Nurhakim Bagus Adhi Kusuma Berlilana Berlilana Desta Nur Efika Ardini Desty Rakhmawati Dewi Fortuna Dwi Krisbiantoro Faridatun Nida Iphang Prayoga Iphang Prayoga Irfan Santiko Irma Darmayanti Jali Suhaman Joko Prayogi Khoirul Abbi Rokhman Laili Nur Hidayati Mohammad imron Muhammad Hasyim Asyari Nurhopipah, Ade Oman Somantri Prasetyo, Agung Pungkas Subarkah Raditya Danar Dana Retno Waluyo Reza Arief Firmanda Rizki Wahyudi Rosana Fadilla Sari Saeful Karim Septi Fajarwati Septi Oktaviani Nur Hidayah Sulis waningsih Tri Astuti Trian Damai Wawa Maisa Yuni Novita Yusyida Munsa Idah