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Journal : journal of applied informatics and computing

Analysis of Digital Readiness in the Social Assistance Distribution System with the Unified Theory of Acceptance and Use of Technology (UTAUT) Adiyono, Soni; Latifah, Noor; Laily Fithri, Diana
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9070

Abstract

The adoption of digital systems for social assistance distribution has become increasingly vital in enhancing efficiency and accessibility. This study examines the acceptance of such a system using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, analyzing six key constructs: Performance Expectancy (PE), Effort Expectancy (EX), Social Influence (SI), Facilitating Conditions (FC), Behavioral Intention (BI), and Actual Use (AU). A total of 150 respondents participated in the survey, providing insights into their perceptions of the system. The findings indicate that Performance Expectancy (4.2) received the highest mean score, demonstrating that users perceive the system as beneficial in improving efficiency. Effort Expectancy (4.0) suggests that the system is easy to use, while Social Influence (3.8) highlights the moderate role of external encouragement. Facilitating Conditions (3.9) reveal the availability of infrastructure but also suggest areas for improvement. Additionally, Behavioral Intention (4.1) and Actual Use (4.0) indicate strong user commitment toward system utilization. The study contributes to the understanding of digital technology adoption in social welfare programs and provides recommendations for optimizing system implementation. Future research should explore the long-term impact of digital adoption, assess its effectiveness in different demographic groups, and integrate qualitative insights to deepen the understanding of user experiences. Additionally, expanding the analysis to include external factors such as policy support, economic conditions, and digital literacy could further enhance the model’s applicability.
Comparative Analysis of Machine Learning Algorithms with SMOTE for Imbalanced Sentiment Classification of IndiHome on Platform X Rizky Adisaputra; Muhammad Arifin; Soni Adiyono
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13271

Abstract

Sentiment analysis of IndiHome users on social media X faces a severe class imbalance, with negative tweets dominating 88.26% of the dataset. This study compares four machine learning algorithms, Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest, for sentiment classification using SMOTE to address the imbalance. Initially, 20,001 Indonesian tweets were scraped using Tweet Harvest with the keyword "indihome". After duplicate removal and preprocessing, 7,199 tweets were retained. Each tweet was manually annotated into positive, negative, and neutral categories. TF-IDF was applied for feature extraction, and Stratified 5-Fold Cross Validation was used for evaluation. Algorithms were tested under two conditions: without and with SMOTE. Before SMOTE, SVM achieved the highest accuracy (94.55%) and F1-score (94.05%). After SMOTE, Random Forest outperformed others with 94.14% accuracy and 93.83% F1-score, as the only algorithm showing consistent improvement across all metrics, including balanced accuracy and MCC. Although Wilcoxon tests showed no statistically significant differences between algorithms, Random Forest demonstrated the most stable and consistent performance. These findings confirm that Random Forest with SMOTE is the most effective strategy for imbalanced sentiment classification in this context.