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The Influence of Financial Literacy and Financial Attitude on Personal Financial Management: Pengaruh Financial Literacy, Financial Attitude terhadap Pengelolaan Keuangan Pribadi Rahmah, Nur; Idris, Munadi; Salehaman; Imran, Muhammad
Robust: Research of Business and Economics Studies Vol. 5 No. 2 (2025): Oktober 2025
Publisher : IAIN Kendari

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Abstract

This study aims to examine the impact of financial literacy and financial attitudes on students' personal financial management. The position of this research is important because good financial literacy and financial attitudes can prevent students from excessive consumer behavior and improve their financial stability. The method used was the distribution of questionnaires to 100 students, employing Multiple Linear Regression Analysis with stages including classical assumption testing, t-test hypothesis testing, and F-test hypothesis testing. The results show that both financial literacy and financial attitudes have a significant effect on students' personal financial management, contributing new insights into the development of understanding the importance of these two factors in achieving better financial management. The implications of this study suggest that the development of financial literacy and positive financial attitudes should be a priority in higher education, particularly in the field of finance, to help students achieve more stable and sustainable personal financial management.
A Deep Learning-Based Approach for Bot Detection in Trending Hashtags on X Hussain, Mehboob; Rana, Muhammad Rizwan Rashid; Imran, Muhammad; Shoaib, Muhammad; Mujtaba, Muhammad Hasaan
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is employed for feature extraction to capture deep contextual semantics, followed by classification through a deep neural network (DNN) to effectively distinguish between human users and bots. The proposed model is evaluated against established baselines using standard performance metrics. Experimental results demonstrate that DeepBot achieves superior performance with an accuracy of 92.82%, precision of 91.24%, and recall of 91.78%, confirming its effectiveness in enhancing the reliability of bot detection in social media trend analysis.