Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Journal of Applied Data Sciences DEVOTE: Jurnal Pengabdian Masyarakat Global
Ni Putu Widya Yuniari
Magister Teknik Elektro Universitas Udayana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
Enhancing Aspect-based Sentiment Analysis in Visitor Review using Semantic Similarity Iswari, Ni Made Satvika; Afriliana, Nunik; Dharma, Eddy Muntina; Yuniari, Ni Putu Widya
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.249

Abstract

The global economy greatly depends on the tourism industry, which fosters job opportunities and stimulates economic development. With the growing reliance of tourists on online platforms for guidance, evaluations of tourist destinations have gained heightened significance. These assessments, frequently expressed through user-generated content, offer valuable perspectives on customer experiences, viewpoints, and levels of satisfaction. Nevertheless, analyzing and interpreting these reviews can pose difficulties because of the unstructured or semi-structured nature of user-generated content. Conventional sentiment analysis methods might not adequately grasp the intricacies and particular aspects of tourism encounters that users convey in their reviews. The efficacy of sentiment analysis can be augmented by integrating semantic similarity. This study explores methods to enhance aspect-based sentiment analysis within tourism reviews by utilizing semantic similarity approaches. Five aspects have been curated, representing keywords frequently reviewed by visitors to the tourist attraction. These aspects encompass scenery, dusk, surf, amenities, and sanitation. Based on the data analysis, F-Measure values with Semantic Similarity tend to increase for the scenery and dusk aspects. This is because in the sample data used, visitor reviews for the scenery and dusk categories may use other words that are semantically similar. The sample data used for these categories is also quite extensive, resulting in a better classification model for both categories. While it is valuable to analyze user-generated content data from visitor reviews, it's important to consider the limitations and potential biases associated with this data. The classification results per aspect need to be further reviewed in more depth. What aspects lead visitors to give positive reviews will certainly be maintained and even improved by stakeholders. Similarly, for negative review outcomes, it is necessary to investigate more deeply the factors contributing to visitor dissatisfaction so that they can be addressed by stakeholders.
Environment Sentiment Analysis of Bali Coffee Shop Visitors Using Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 2 (GPT2) Model Yuniari, Ni Putu Widya; Iswari, Ni Made Satvika; Kumara, I Made Surya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.302

Abstract

Bali is one of the provinces with the most abundant natural and cultural wealth in Indonesia. One commodity that supports it is coffee. Bali Coffee is not only a gastronomic identity, but also a cultural identity which makes it have added value to be developed into various business lines. One business derivative that is quite promising is a coffee shop. However, these favorable conditions also need to be maintained to ensure good quality reaches consumers. One thing that can do is analyze reviews from customers. One of the most popular methods is Sentiment Analysis. This technique allows business to analyze customer reviews on social media. It can be a feedback to maintaining and improving quality and good relationships with customers. This research aims to create a machine learning model to analyze customer reviews at several coffee shops in Bali which are divided into three labels, namely: positive, negative and neutral. The methods used are: scraping, cleaning, stopword removal, embedding, undersampling, and modeling. The algorithms used are Bidirectional Encoder Representation from Transformer (BERT) and Generative Pre-trained Transformers (GPT). The performance metrics used in this research are precision, recall, accuracy and loss. This research succeeded in creating a sentiment analysis model for coffee shop customers in Bali. The BERT model obtained an accuracy value of 78% without undersampling with a loss in the 10th iteration of 0.27. Meanwhile, the BERT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.16. The GPT2 model without undersampling gets an accuracy of 78% with a loss in the 10th iteration of 0.25. Meanwhile, the GPT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.15.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Iswari, Ni Made Satvika; Wijaya, I Nyoman Yudi Anggara; Yuniari, Ni Putu Widya
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Co-Authors Afriliana, Nunik Bhaskara, Made Adi Darma, I Gede Wira Dharma, Eddy Muntina I Made Surya Kumara I Nyoman Yudi Anggara Wijaya Jati, Gde Putu Rizkynindra Sukma Mahayani, Ni Komang Indra Ni Made Satvika Iswari Prasandya, Km. Deddy Endra Putu Aryastana Raharja, I Kadek Agus Wahyu Wikan Pradnya Dana, Gde