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All Journal Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika RISTEC : Research in Information Systems and Technology JIMT: Jurnal Informatika, Multimedia dan Teknik
Cahyan, Yayan
Universitas Sebelas April, Sumedang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering

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Journal : RISTEC : Research in Information Systems and Technology

 1 
Review Analysis of SatuSehat Application Using Support Vector Machine and Latent Dirichlet Allocation Modeling Fikri Fahru Roji; Nava Gia Ginasta; Yayan Cahyan; Dinar Rahayu; Dendi Ramdani
RISTEC : Research in Information Systems and Technology Vol 4, No 1 (2023): Riset Sistem dan Teknologi Informasi
Publisher : Institut Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/ristec.v4i1.3312

Abstract

SatuSehat is a contact tracing application that replaces the PeduliLindungi application initiated by the Government of Indonesia with the aim of tracking the Covid-19 Virus. The success of the application can be known by analyzing sentiment reviews. In addition to the high number of reviews, there are also other things that need to be highlighted, namely the pattern of reviews that are not in accordance with refined spelling and diverse topics, so that identifying a topic from a collection of reviews is very difficult and takes a lot of time if done manually by humans. This research describes sentiment analysis and topic modeling on SatuSehat app user reviews. By applying Support Vector Machine (SVM) method for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling, this study reveals the views and trends expressed by users. The analyzed review data from Google Play Store includes 171,428 positive reviews and 131,246 negative reviews. The sentiment analysis results indicated the dominance of positive responses. LDA modeling resulted in 8 identified topics, from health concerns to app appreciation. However, negative topics included vaccination challenges, access issues, and app functionality. This research provides insight into users' perceptions of the SatuSehat app, providing a basis for further development and improvement of the app. Keywords: Sentiment Analysis; Topic Modeling; OneHealth App; SVM; LDA
Review Analysis of SatuSehat Application Using Support Vector Machine and Latent Dirichlet Allocation Modeling Fahru Roji, Fikri; Gia Ginasta, Nava; Cahyan, Yayan; Rahayu, Dinar; Ramdani, Dendi
RISTEC : Research in Information Systems and Technology Vol. 4 No. 1 (2023): JURNAL RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

SatuSehat is a contact tracing application that replaces the PeduliLindungi application initiated by the Government of Indonesia with the aim of tracking the Covid -19 Virus. The success of the application can be known by analyzing sentiment reviews. In addition to the high number of reviews, there are also other things that need to be highlighted, namely the pattern of reviews that are not in accordance with refined spelling and diverse topics, so that identifying a topic from a collection of reviews is very difficult and takes a lot of time if done manually by humans. This research describes sentiment analysis and topic modeling on SatuSehat app user reviews. By applying Support Vector Machine (SVM) method for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling, this study reveals the views and trends expressed by users. The analyzed review data from Google Play Store includes 171,428 positive reviews and 131,246 negative reviews. The sentiment analysis results indicated the dominance of positive responses. LDA modeling resulted in 8 identified topics, from health concerns to app appreciation. However, negative topics included vaccination challenges, access issues, and app functionality. This research provides insight into users' perceptions of the SatuSehat app, providing a basis for further development and improvement of the app.
Uncovering Hidden Sentiments and Topics in Online Lending Application Reviews with the Valence Aware Dictionary and sEntiment Reasoner (VADER) and Latent Dirichlet Allocation (LDA) Approaches Fahru Roji, Fikri; Ariesti Anggraeni, Windi; Sabilul Muminin, Riyad; Ramdani, Dendi; Cahyan, Yayan
RISTEC : Research in Information Systems and Technology Vol. 4 No. 2 (2023): RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

Online lending (pinjol) has become an important part of the digital transformation of the financial sector, offering people easy access to funds. However, the increasing reliance on user reviews as a decision-making factor raises concerns about their authenticity and credibility. This research aims to analyze the sentiments and topics that appear in the reviews of Akulaku, Kredivo, and EasyCash lending apps on the Google Play Store. Using text mining techniques, VADER sentiment analysis, and LDA topic modeling, this research reveals dominant positive sentiments related to ease of use, service speed, and customer support. However, there were also negative reviews regarding loan application difficulties, technical issues, and bad experiences with billing and payments. This research provides valuable insights into the preferences and concerns of pinjol users, which can serve as a reference for service providers to improve the quality of their products and services.
Co-Authors Anggraeni, Windi Ariesti Dendi Ramdani Dinar Rahayu Dody Herdiana Eggi Julian Fikri Fahru Roji Gia Ginasta, Nava Hidayat, Faishal Akbar Muhammad Agreindra Helmiawan Nava Gia Ginasta Rahayu, Dinar Ramdhani, Ravi Rizki Sabilul Muminin, Riyad