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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIEET (Journal of Information Engineering and Educational Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Electrical Engineering and Computer Science Journal of Informatics and Computer Science (JINACS) J-Icon : Jurnal Komputer dan Informatika Journal of Emerging Information Systems and Business Intelligence (JEISBI) Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA)
Wiyli Yustanti
Universitas Negeri Surabaya
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Sentiment Analysis and Topic Modeling of Tourist Attractions in Gresik Regency Using the BERT Method Salsabilla Putri Saharani; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.72873

Abstract

Tourism is one of the key sectors in national economic growth as well as a pillar of regional community welfare. Gresik Regency in East Java has considerable tourism potential, with more than 100 destinations covering religious tourism, natural attractions, and family recreation. However, tourist visit data from 2022–2024 shows a declining trend that requires an in-depth evaluation of visitor perceptions and experiences. This study aims to analyze public sentiment toward tourist destinations in Gresik Regency and identify the main topics of concern for tourists. The research data was collected from Twitter and Google Maps within the period of 2021–2024 using crawling techniques. Sentiment analysis was carried out with IndoBERT, while topic modeling was conducted using BERT. The results indicate that tourism reviews are dominated by positive sentiments highlighting the uniqueness of religious destinations, natural beauty, and family recreation atmosphere. However, negative sentiments were also found, emphasizing issues related to facilities, cleanliness, staff services, and accessibility to the sites. Topic modeling successfully grouped tourist opinions into coherent themes, and evaluation with coherence scores demonstrated good quality outcomes. The study concludes that although Gresik has strong tourism appeal, challenges in facility management, services, and digital promotion need to be addressed immediately. The integration of sentiment analysis and BERT-based topic modeling has proven effective in providing comprehensive insights into tourist perceptions and can serve as a basis for formulating regional tourism development strategies.
Sentiment Analysis and Topic Modeling Using BERT And LDA Methods (Case Study of Free Meal Program on Twitter) Siti Mahmudah Putri Yanna; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.75819

Abstract

The Free Meal Program is one of the Indonesian government's strategic efforts to structurally address poverty and malnutrition (stunting). As a new policy with massive social and fiscal impacts, an in-depth evaluation is required to measure public acceptance. This study aims to categorize public sentiment into positive and negative categories and identify the dominant topics discussed on Twitter (X) regarding the program. The methodology involved crawling Twitter data, resulting in a total of 8,307 datasets. Sentiment labeling was performed automatically using the IndoBERT deep learning model, followed by topic modeling using the Latent Dirichlet Allocation (LDA) method for each sentiment category. The results of the topic modeling were validated through topic coherence tests using word instruction task and topic instruction task techniques. The results showed an imbalanced sentiment distribution, with 7,034 negative sentiments and 1,273 positive sentiments. LDA modeling successfully extracted 5 optimal topics for both sentiment categories. Positive sentiments included topics such as budget efficiency, the role of government institutions (National Police), technical implementation, and local economic empowerment. Meanwhile, negative sentiments encompassed concerns regarding state budget (APBN) priorities, health/poisoning issues, and the comparative urgency between the free meal program and the education and health sectors. The coherence test results showed an interpretation accuracy rate of 93% for keywords and 79% for topic relevance, indicating that the developed LDA model was optimal in extracting public opinion.
Sentiment Analysis and Word Association Patterns in Skincare Product Customer Reviews Aliyah Alifi; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.76813

Abstract

The growth of the skincare industry and the increasing activity of consumer reviews on e-commerce platforms have generated large text data containing customer opinions, experiences, and perceptions. This study aims to analyze sentiment and identify word association patterns in Indonesian-language customer reviews of skincare products. The literature review covers sentiment analysis, Natural Language Processing, the IndoBERT language model, Data Mining, Knowledge Discovery in Databases, as well as Association Rule Mining using the Apriori algorithm. The research method uses a quantitative approach based on KDD, which includes Data Selection, Preprocessing, Data Transformation, Data Mining, and interpretation and evaluation. Data was obtained through web scraping of skincare product reviews on the Shopee platform, resulting in 7,320 clean reviews. Sentiment analysis was conducted using IndoBERT with a Hybrid Linguistic approach to handle neutral rating ambiguities. The results of the sentiment classification were then used as the basis for analyzing word association patterns using the Apriori algorithm for each sentiment category. The findings indicate that IndoBERT is capable of classifying sentiment contextually, while Apriori successfully uncovers word patterns that represent product aspects such as quality, effectiveness, and user experience. This study concludes that the integration of sentiment analysis and word association patterns provides a more comprehensive understanding of consumer perceptions and can be utilized as a basis for strategic decision-making in the skincare industry.
Co-Authors Agus Prihanto Ahmad Khoiru Shofa Ainandita Riwipapusa Aliyah Alifi Amalia Putri Andhika Edo Prasetyo Anita Qoiriah Dhiany Hana Amalia Ervin Yohannes Hapsari Peni Agustin Tjahyaningtijas Irhamah - Miftakhul Alief Faturrahman Mohammad Hasan Machfoed Muhammad Dafi Bagas Muwirotul Hasanah Nabiilah Winda Kurnia Putri Naim Rahmawati Naim Rochmawati Nur Iriawan Octania Sriwahyuni Rahadian Bisma Salsabilla Putri Saharani Siti Mahmudah Putri Yanna Stevi Aprilianti Cahyani Susi Purwani Tama Priyadi Windy Chikita Cornia Putri Yoyok Prastyo YUNI YAMASARI