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Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company Dhenda Rizky Pradiptyo; Irfanda Husni Sahid; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.74486

Abstract

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.
Customer Satisfaction Evaluation in Online Food Delivery Services: A Systematic Literature Review Adimas Fiqri Ramdhansya; Shella Maria Vernanda; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6205

Abstract

The rapid growth of online food delivery services has heightened the need for effective customer satisfaction measurement. This systematic literature review examines 476 papers, selecting 15 key studies to identify prevailing evaluation approaches. Findings reveal that sentiment analysis and PLS-SEM are the most frequently used analytical methods, each appearing in six studies. Satisfaction measurement relies on sentiment polarity scores in five studies and SERVQUAL frameworks in three studies. Data collection primarily involves surveys in seven studies and user-generated content in six studies, but limited demographic diversity reduces generalizability. Three key future research directions emerge. Advanced analytical techniques appear in 5 of 11 future works in the analysis methods domain. Expanding evaluation metrics is mentioned in 6 of 12 proposals in the evaluation domain. Exploring demographic context is highlighted in 10 of 25 recommendations in the dataset’s domain, with dataset development receiving twice the attention of methodological advancements. These results provide researchers with a structured framework for customer satisfaction evaluation while guiding food delivery platforms in refining service quality. By systematically mapping current methodologies and future priorities, this study bridges gaps between academia and industry, ensuring more effective customer satisfaction assessments.
A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches Kusumawati, Karisma Vinda Nissa; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.34947

Abstract

Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis.   Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles.   Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.