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All Journal DoubleClick : Journal of Computer and Information Technology Jurnal Informatika
Reza Arief Firmanda
Universitas AMIKOM Purwokerto
Computer Science & IT

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Analisa Rute Transjateng Rute Purwokerto – Purbalingga Dengan Algoritma Dijkstra Cindy Magnolia; Pungkas Subarkah; Reza Arief Firmanda; Dava Patria Utama
DoubleClick: Journal of Computer and Information Technology Vol 5, No 1 (2021): Peran Penting Digitalisasi Di tengah Pandemi Covid-19
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/doubleclick.v5i1.9593

Abstract

Transportation has grown very rapidly in the last few decades, including in Purwokerto, one of the cities in Central Java that is developing in its economic sector where transportation is one of the facilities needed for people who need high mobility to meet their needs, one of the transportation that is in demand is BRT, To reduce congestion and support the community's economy, the government launched the BRT Trans Jateng Bukateja - Purwokerto which has travel time savings so that fuel consumption is minimal and transportation costs can be optimized. In this study, the authors analyzed the BRT Trans Jateng Bukateja – Purwokerto route using the Djikstra method to measure efficiency and the fastest mileage by paying attention to vehicle specifications on the BRT Trans Jateng Bukateja – Purwokerto, the data used is the distance between shelters, passenger capacity, operating hours, bus departure distance and bus fuel consumption.
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.
Co-Authors Cindy Magnolia Dava Patria Utama Iphang Prayoga Primandani Arsi Pungkas Subarkah