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All Journal Journal of Information Systems Engineering and Business Intelligence Konstelasi: Konvergensi Teknologi dan Sistem Informasi JOINTECOMS (Journal of Information Technology and Computer Science)
Rahman, Resha Ananda
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Computer Science & IT

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Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews Pranatawijaya, Viktor Handrianus; Sari, Nova Noor Kamala; Rahman, Resha Ananda; Christian, Efrans; Geges, Septian
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 3 (2024): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.3.328-339

Abstract

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback.   Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)
Rancang Bangun Sistem Informasi Penjualan Toko Eight Point Store Berbasis Website Kristianti, Novera; Rahman, Resha Ananda
Journal of Information Technology and Computer Science Vol. 2 No. 1 (2022): JOINTECOMS : Journal of Information Technology and Computer Science
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.506 KB) | DOI: 10.47111/jointecoms.v2i1.8836

Abstract

E-commerce can be interpreted as a way of using internet facilities for online shopping or trade or direct sales, where a website can provide "get and deliver" services. E-commerce is a very effective way of marketing products or services at this time, which requires business actors to provide convenience to consumers. The convenience that can be enjoyed by consumers, such as buyers can use mobile devices to view products, access merchandise, store information, order, and pay while traveling or anywhere. Not only that, the existence of a website will be able to expand the reach of marketing, so a web-based application is needed to support the process of marketing and order products. By having your e-commerce website, shops can build their business brand, then they can increase the credibility of the business and look more bona fide, consumers can easily get the latest information, can always be accessed, have a wider target market, provide a place to show products, customers will focus on the product offered. In this research, in designing and building an application using the Waterfall model SDLC method. In the Waterfall stage it flows by completing one stage and then moving to the next stage such as a Waterfall, this stage sequentially starts from the analysis, design, implementation, testing, distribution, and support stages. This website has features for purchasing products by paying via bank transfer, and the admin section can manage product data and print sales reports in a day, month, and year format. Using the Black box results from research on the Eight point store Shop Sales Information System Website can perform functions according to its purpose.
Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek Rahman, Resha Ananda; Pranatawijaya, Viktor Handrianus; Sari, Nova Noor Kamala
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 4 No. 1 (2024): Juni 2024
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v4i1.8922

Abstract

Penggunaan aplikasi mobile meningkat pesat di era digital, termasuk Gojek, aplikasi populer di Indonesia yang menyediakan layanan transportasi, pesan antar makanan, dan pembayaran digital. Ulasan pengguna di Play Store menunjukkan berbagai masalah yang memerlukan perhatian. Ulasan ini memberikan wawasan tentang pandangan pengguna, memungkinkan identifikasi masalah, dan pengembangan layanan. Dengan teknik Aspect Based Sentiment Analysis (ABSA), pandangan pengguna dapat dipahami lebih baik, membantu evaluasi dan perbaikan aplikasi Gojek untuk meningkatkan kualitas layanan dan kepuasan pengguna. Penelitian ini bertujuan menganalisis sentimen berdasarkan aspek-aspek dalam ulasan pengguna aplikasi Gojek di Play Store dalam bahasa Inggris, dengan mencari pola sentimen yang akurat dan mengidentifikasi aspek yang perlu diperbaiki. Data diambil dari ulasan pengguna aplikasi Gojek di Google Play Store. Teknik pemodelan topik Latent Dirichlet Allocation (LDA) digunakan untuk mengidentifikasi topik-topik relevan. Pelabelan sentimen dilakukan menggunakan model BERT, sementara evaluasi sentimen dan aspek dilakukan dengan model distilbert-base-uncased-finetuned-sst-2-english. Hasil menunjukkan bahwa model BERT mencapai akurasi tertinggi untuk sentimen sebesar 96.67% dan aspek Service sebesar 98.78%. Terdapat ruang untuk perbaikan terutama pada aspek user experience, service, dan payment. Faktor-faktor yang mempengaruhi akurasi termasuk distribusi sentimen, jumlah data, preprocessing, dan model yang digunakan. Mobile app usage is increasing rapidly in the digital era, including Gojek, a popular app in Indonesia that provides transportation, food delivery, and digital payment services. User reviews in the Play Store indicate various issues that require attention. These reviews provide insight into user views, enabling problem identification and service development. With the Aspect Based Sentiment Analysis (ABSA) technique, user views can be better understood, helping evaluate and improve the Gojek application to improve service quality and user satisfaction. This research aims to analyze sentiment based on aspects of user reviews of the Gojek application on the Play Store in English by finding accurate sentiment patterns and identifying aspects that need to be improved. The data was taken from user reviews of the Gojek application on the Google Play Store. Latent Dirichlet Allocation (LDA) topic modeling technique was used to identify relevant topics. Sentiment labeling was performed using the BERT model, while sentiment and aspect evaluation were performed with the distilbert-base-uncased-finetuned-sst-2-english model. The results showed that the BERT model achieves the highest accuracy for sentiment at 96.67% and Service aspects at 98.78%. There is room for improvement, especially in the user experience, service, and payment aspects. Factors affecting accuracy include sentiment distribution, amount of data, preprocessing, and the model used.
Co-Authors Efrans Christian Kristianti, Novera Nova Noor Kamala Sari Septian Geges Viktor Handrianus Pranatawijaya