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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Prabu Kresna Putra
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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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.
SENTIMENT AND TREND ANALYSIS OF PUBLIC OPINION ON BANK BTN MORTGAGE SERVICES VIA INSTAGRAM Putri Dian Zara; Veny Anggraini; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.64333

Abstract

Bank BTN is the leading provider of mortgage (KPR) services in Indonesia, yet customer complaints on social media remain frequent. According to the OJK 2023 Annual Report, the banking sector recorded 10,848 consumer complaints, an increase from 7,425 complaints in 2022. This trend highlights the need for a systematic analysis of public opinion on KPR services. This study analyzes public sentiment expressed on Bank BTN’s official Instagram account during 2023. User comments were classified into positive and negative sentiments using text mining techniques. Four machine learning algorithms were compared: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). A total of 1,507 comments were obtained after data cleaning, preprocessing, and filtering from 28,745 raw comments. SMOTE was applied to address the class imbalance. Decision Tree achieved the highest F1-score (0.9348) on the imbalanced test set, followed by SVM (0.9259). Trend analysis was conducted using Pearson, Spearman, and Kendall correlation tests. The correlation between daily sentiment and KPR disbursement was weak and not statistically significant (Pearson: 0.082, p = 0.187; Spearman: 0.099, p = 0.112; Kendall: 0.077, p = 0.102; N = 259). These results indicate that sentiment fluctuations on Instagram cannot be used as a direct predictor of same-day mortgage disbursement. There may be a time lag, a non-linear relationship structure, or the influence of more dominant fundamental factors.
Co-Authors Adimas Fiqri Ramdhansya Aris Budi Santoso Dhenda Rizky Pradiptyo Indra Budi Irfanda Husni Sahid Putri Dian Zara Shella Maria Vernanda Veny Anggraini