Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods

Ferdinand Aprillian Manurung, Munawir, Deden Pradeka — Sat, 18 Jan 2025 00:00:00 +0000

The rapid development of communication technology has led to an increase in the number of unwanted messages, such as spam and phishing attempts. However, this progress has not been accompanied by sufficient user awareness of the basics of technology use. Additionally, the enforcement of laws regarding internet-based crimes remains unclear, further increasing the risk for users of internet technology to fall victim to such crimes. As one of the media prone to spam and phishing, WhatsApp is the focus of this research, which aims to develop an application capable of filtering spam and phishing messages. The application employs the TF-IDF (Term Frequency-Inverse Document Frequency) method and machine learning using the Random Forest model. It is developed using the MVVM (Model-View-ViewModel) architecture, enabling the separation of business logic from the user interface, thereby improving development and maintenance efficiency. The research findings demonstrate that the combination of TF-IDF and Random Forest achieves high accuracy in classifying spam and phishing messages. Performance evaluation using a confusion matrix reveals an accuracy rate of 92%. For the safe message class, the precision, recall, and F1 scores are 89%, 95%, and 92%, respectively, while for the dangerous message class, the scores are 95%, 88%, and 92%, respectively. Furthermore, the integration of the model and application performed exceptionally well, as evidenced by black-box testing results. All test scenarios were met, successfully detecting test messages with 98% accuracy. Therefore, the developed application provides enhanced protection for WhatsApp users against digital threats.

Harnessing Smart Farming: Key Determinants of Automated Mini Greenhouse Adoption and Use in the Philippines

Eugenia R. Zhuo — Tue, 04 Feb 2025 00:00:00 +0000

This research investigated the determinants of adopting and sustaining the utilization of automated mini-greenhouses in the Philippines, a nation particularly vulnerable to climate change. Using an integrated theoretical framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovation (DOI), and Actor-Network Theory (ANT), this research employed a quantitative approach to assess key constructs, such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, trust, habit, and technology readiness. Data were collected through structured surveys administered to smallholder farmers, and the results were analyzed using Python-based statistical tools. The findings indicated that performance expectancy and social influence were significant predictors of technology adoption, while habit and facilitating conditions strongly influenced continued use. Trust and resource accessibility, derived from DOI and ANT, also emerged as critical factors in sustained utilization. These results contributed to understanding smart farming adoption in the context of climate resilience and sustainable agriculture. Future research should explore broader applications of such technologies and further examine their long-term sustainability.

Green Intelligent Systems and Applications

Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods

Harnessing Smart Farming: Key Determinants of Automated Mini Greenhouse Adoption and Use in the Philippines