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COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
International Journal of Informatics and Information Systems Vol 5, No 1: January 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i1.126

Coronavirus (COVID-19) is a rapidly emerging and spreading infectious disease. To minimize the impact caused by the virus, it is necessary to have a vaccine. However, the existence of vaccinations for the Indonesian people has caused controversy so that it invites many people to give an opinion assessment, therefore people choose social media as a place to channel their opinions. In this study, a comparison was made with an observational infoveillance study by collecting data using a Python programming script (Python Software Foundation) to display posts related to the COVID-19 vaccine on Twitter as well as quantitative and qualitative analysis to identify trends and characterize the main themes discussed by twitter users on Twitter. Indonesia. Our research collects data through social media Twitter in the period August 2020 - March 2021. In this study we combine Retrospective Observation and Sentiment Analysis, with the aim of producing periodic timeline evaluations within a predetermined time frame. In this study author found that there was an interaction increase in positive posts due to officially reported developments, on the other hand we were quite difficult to understand the factors behind the emergence of negative posts but we made a conclusion based on the results of sentiment analysis that most of the negative posts were caused by lack of information and understanding of vaccines and vaccines. the COVID-19 outbreak itself.

Integrating Technology and Legal Strategies to Combat Evolving Money Laundering Tactics Hananto, Andhika Rafi
International Journal of Informatics and Information Systems Vol 5, No 3: September 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i1.93

Money laundering has significantly advanced with the aid of technology, enabling perpetrators to exploit technological tools for criminal ease. This trend is compounded by the use of cross-border cash couriers, increasingly favored as a method for laundering illicit funds. International conventions and multilateral agreements acknowledge the vulnerability of cash courier operations to money laundering, yet current frameworks primarily offer detection guidelines rather than precise methods for direct recognition. Given that money laundering involves proceeds of crime, authorities must scrutinize and assess transactions to determine if criminal activity constitutes money laundering, distinct from customs violations.Moreover, the proliferation of innovative financial products and payment systems, including cryptocurrencies like Bitcoin, Litecoin, and other virtual currencies, as well as bearer negotiable instruments, has further facilitated money laundering opportunities. Research indicates that criminal tactics are outpacing legal frameworks, with technology infiltrating the strategies of money launderers and potentially overshadowing regulatory controls. Despite technology's neutral intent, its misuse challenges the traditional role of law enforcement.This qualitative study aims to analyze how legal frameworks can collaborate with technology to combat money laundering effectively. The hypothesis posits that the law can provide crucial guidance amid technological developments, while technology can prompt legal systems to adapt swiftly. By integrating these approaches, the research suggests that combating the evolution of money laundering becomes more formidable when law and technology converge.

An Ensemble and Filtering-Based System for Predicting Educational Data Mining Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.44

When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.

Utilizing Support Vector Machine and Dimensionality Reduction to Identify Student Learning Styles within the Felder-Silverman Model Hananto, Andhika Rafi; Musdholifah, Aina; Wardoyo, Retantyo
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.337

This research explores the impact of questionnaire structure on the accuracy of learning style classification, focusing on the optimization of the Felder-Silverman Learning Style Model (FSLSM) using advanced machine learning techniques. By employing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, the study identifies and retains the most informative variables from the original 44-question FSLSM instrument. These refined features are then processed through a Support Vector Machine (SVM) algorithm to evaluate classification performance across various core-to-secondary item ratios. Results indicate that the most optimal configuration—produced through the combined PCA-t-SNE reduction—achieved a peak accuracy of 89.54%, surpassing other configurations and highlighting the effectiveness of selective question modeling. This approach not only enhances prediction accuracy but also introduces a more efficient and streamlined FSLSM formula, reducing redundancy without compromising diagnostic precision. The study contributes to educational data mining by presenting a data-driven strategy for learning style assessment and offers practical implications for the development of adaptive, personalized learning systems grounded in statistically validated models.

An Android-Based Multimodal AI Application for Contextual Environmental Learning in Children Hananto, Andhika Rafi; Rahardian, Muhammad Izha
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.264

Children’s limited engagement with nature in the digital era poses a growing challenge for environmental education. This study presents the development of an Android-based educational application that leverages multimodal artificial intelligence (AI)—specifically the Google Gemini model—to facilitate contextual environmental learning for preschool and elementary-aged children. Using a prototyping methodology, the application integrates image capture, cloud-based processing through a FastAPI backend, and a Flutter-based interface designed for young learners. The system allows children to photograph plants and receive real-time, age-appropriate explanations about plant names, characteristics, and ecological functions in a narrative format. A limited usability trial involving children of varying age groups demonstrated positive engagement and curiosity, indicating the app’s potential as an interactive and enjoyable learning medium. Despite occasional inaccuracies in AI-generated descriptions and reliance on internet connectivity, user feedback suggested strong interest and educational value. Future enhancements will focus on developing localized plant databases, improving accuracy, and incorporating gamification elements. Overall, this study contributes to the growing field of AI-driven educational technology, demonstrating how multimodal AI can effectively bridge digital learning with real-world environmental experiences.

Comparative Analysis of Ensemble Learning Techniques for Purchase Prediction in Digital Promotion through Social Network Advertising Hananto, Andhika Rafi; Srinivasan, Bhavana
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.7

This study conducts a comprehensive comparative analysis of ensemble learning techniques for predicting user purchases in social network advertising. The ensemble methods evaluated include Random Forest, Gradient Boosting Machines (GBM), AdaBoost, and Bagging. The dataset, consisting of 7,000 records of user interactions with social network advertisements, was preprocessed to handle missing values, encode categorical variables, and standardize numerical features. Performance metrics such as accuracy, precision, recall, F1 score, and ROC AUC score were used to evaluate each model. The Random Forest model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.948. The GBM model also performed well, with an accuracy of 0.875, precision of 0.846, recall of 0.786, F1 score of 0.815, and ROC AUC score of 0.948. The AdaBoost model showed the highest performance, with an accuracy of 0.9, precision of 0.917, recall of 0.786, F1 score of 0.846, and ROC AUC score of 0.969. The Bagging model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.939. Feature importance analysis revealed that Age and Estimated Salary were the most significant predictors across all models. Hyperparameter tuning was crucial in optimizing each model's performance, ensuring they were neither too simple nor too complex. The study's findings underscore the effectiveness of ensemble learning techniques in social network advertising and provide valuable insights for marketers. Future research could explore larger and more diverse datasets, other ensemble methods, and the computational efficiency of these models. This research contributes to predictive analytics in marketing, enhancing the accuracy and effectiveness of advertising strategies.

Analysis of the Relationship Between Trading Volume and Bitcoin Price Movements Using Pearson and Spearman Correlation Methods Hananto, Andhika Rafi; Sugianto, Dwi
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.8

This study investigates the relationship between trading volume and Bitcoin price movements using Pearson and Spearman correlation methods. The aim is to determine if trading volume can reliably predict Bitcoin price changes. Using a comprehensive dataset of daily Bitcoin prices and trading volumes, various statistical techniques were employed. Pearson and Spearman correlation analyses revealed very weak and statistically insignificant relationships, with correlation coefficients of -0.023788 and 0.021093, respectively. Linear regression analysis further supported these findings, showing an insignificant regression coefficient for trading volume and a very low R-squared value of 0.000566. Volatility analysis, measured by the standard deviation of daily returns, demonstrated high price volatility, consistent with the cryptocurrency market's nature. This volatility is influenced by factors such as market sentiment, regulatory developments, and macroeconomic events. The study also utilized 30-day moving averages to smooth short-term fluctuations and highlight long-term trends in trading volume and closing prices, revealing underlying trends not visible in daily data. A 1-day lagged correlation analysis indicated a very weak relationship (0.008145) between trading volume on one day and price changes on the next, suggesting other factors drive price movements. Visualizations, including time series graphs, histograms, moving averages, and volatility graphs, further illustrated the lack of a clear pattern between trading volume and price changes. In conclusion, trading volume is not a significant predictor of Bitcoin price movements, highlighting the need for comprehensive analytical approaches considering multiple variables to understand and predict Bitcoin price dynamics better.

Comprehensive Analysis of Twitter Conversations Provides Insights into Dynamic Metaverse Discourse Trends Kumar, Aayush; Hananto, Andhika Rafi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.2

The metaverse, a concept originating from science fiction, has gained substantial traction in recent years as advancements in technology have brought it closer to reality. This virtual shared space, accessed through immersive technologies like virtual reality (VR) and augmented reality (AR), has captivated the imagination of both tech enthusiasts and the general public. This study aims to explore the dynamics of the metaverse discourse by analyzing online discussions across various platforms. We employed a combination of data collection methods, including Twitter API access and web scraping, to gather a diverse dataset of tweets related to the metaverse. Subsequently, the collected data underwent extensive preprocessing to ensure consistency and prepare it for analysis. Our analysis encompassed user statistics, word analysis in tweets, hashtag analysis, and tweet distribution patterns. The findings reveal intriguing insights into user behavior, content trends, and temporal patterns within the metaverse discourse. We observed prominent usernames, geographic distributions of users, prevalent words and hashtags, as well as temporal fluctuations in tweet activity. For instance, the most common username is "Fatemeh ashoobian" with 800 users, indicating a significant presence in the metaverse community. Furthermore, the number of tweets about the metaverse per day over a certain period shows daily fluctuations with the highest peak on November 14, 2023. These insights contribute to a deeper understanding of the metaverse ecosystem and its implications for society, technology, and culture. Through our research, we aim to provide valuable insights to stakeholders across various sectors, including technology developers, policymakers, content creators, and end-users. By understanding the emergent trends and themes within the metaverse discourse, stakeholders can navigate this rapidly evolving landscape more effectively and harness its transformative potential for the benefit of humanity.

Geospatial Analysis of Virtual Property Prices Distributions and Clustering Sugianto, Dwi; Hananto, Andhika Rafi
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.10

This paper presents an analysis of property prices in the virtual world, focusing on geographical distribution and district comparisons. Utilizing a dataset of virtual properties, we applied scatter plot analysis, cluster analysis using DBSCAN, and box plot comparison to identify key patterns and opportunities within this market. The scatter plot analysis revealed that property prices are unevenly distributed, with higher prices clustering in specific regions, indicating areas of higher desirability and value. The DBSCAN clustering identified distinct high-value clusters, each containing 10 to 67 properties, and highlighted 1,067 properties as noise, suggesting a dispersed distribution of lower-value properties. Box plot comparisons across districts showed significant variations in property values. Some districts exhibited higher median prices, with the highest at 35,452.60 MANA, while others had lower medians. Variability within districts varied, with some showing a wide range of prices and others more uniform values. Outliers suggested unique investment opportunities in both premium and undervalued properties. For virtual real estate investors, the findings emphasize the importance of location and strategic investment. High-value districts and emerging areas offer potential for significant returns. Developers and urban planners can use these insights to focus on high-demand areas, enhancing project value through strategic investments in infrastructure and amenities. This study highlights the dynamic nature of the virtual real estate market and the importance of ongoing research to understand factors influencing property values. Stakeholders can make informed decisions and capitalize on opportunities in this evolving market.

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