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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences
Hananto, Andhika Rafi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Co-Authors Aina Musdholifah Rahardian, Muhammad Izha Rahayu, Silvia Anggun Retantyo Wardoyo Taqwa Hariguna