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Journal : Journal of Applied Data Sciences

Polarization of Religious Issues in Indonesia’s Social Media Society and Its Impact on Social Conflict Faizin, Barzan; Fitri, Susanti Ainul; AS, Enjang; Maylawati, Dian Sa'adillah; Rizqullah, Naufal; Ramdhani, Muhammad Ali
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.447

Abstract

In this new era, people use social media to share information and discuss political, social, and religious issues, leading to pros and cons arguments. In Twitter’s hashtags and tweets, religious issues frequently trigger a hot conversation that causes disputes among citizens and even street movements. This study is intended to reveal the religious issues that often trigger polarization among Twitter users and how they influence horizontal conflict in society as what happened during the election period in 2019. This research applied mixed methods with social media analytics to conceal religious issues in Indonesia's social media society. The data collection was done by crawling data from the Indonesian Twitter users’ tweets regarding religious issues hashtags, which is a reference for further analysis. The research findings show that the top eight religious issues widely discussed based on 23,433 Twitter users’ tweets are the hashtags (#) salafi, wahabi, intoleransi (intolerance), taliban, anti-Pancasila, politisasi agama (politicization of religion), politik identitas (identity politics), and radikalisme (radicalism). In social conversation networks, these issues are related to each other and other issues such as political figures, the three presidential candidates, the general election, and the Republic of Indonesia presidential election in 2024. Concerning these issues, Twitter users believe that the issues, positive or negative, do not influence their religious and political stance. However, to a certain extent, they believe that religious issues impact social discourses regarding horizontal conflicts. 44% opinions prove this indicated that the debate over religious matters had little influence on their opinion of these topics, and 64.5% agreed that religious concerns can cause social strife. Finally, it is hoped that further studies will elaborate on how religious issues on Twitter and other social media directly impact social harmony in everyday life.
Feature Engineering for Tropical Rainfall Forecasting Using Random Forest and Support Vector Regression Cepy Slamet; Rizka M Imron; Agung Wahana; Dian Sa'adillah Maylawati; Wildan Budiawan Zulfikar; Muhammad Ali Ramdhani
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1111

Abstract

The complex dynamics of weather variability in Indonesia, influenced by multiple climatic drivers, make rainfall forecasting in tropical regions a significant scientific challenge. This study proposes an automated feature engineering pipeline to enhance the performance of Random Forest Regression (RFR) and Support Vector Regression (SVR) models for tropical rainfall prediction. Monthly rainfall data spanning 388 months (1993–2025) from a BMKG station were used as the basis for model development. The pipeline systematically generates temporal, seasonal, statistical, and anomaly-based features to provide domain-informed representations for non-sequential learning algorithms. Model performance was evaluated under four temporal data partitioning scenarios using R², RMSE, and probabilistic confidence intervals derived from bootstrap residual simulations. Results indicate that RFR achieved the highest predictive accuracy (R² = 0.93; RMSE = 31.01 mm) and demonstrated superior temporal–seasonal stability (Rolling CV: R² = 0.81 ± 0.07; RMSE = 55.44 ± 16.18), with comparable performance between wet and dry seasons. Conversely, SVR showed greater sensitivity to seasonal variability, with R² dropping to 0.55 during the wet season, indicating higher uncertainty under extreme rainfall conditions. Robustness and drift analyses further revealed that RFR adapts better to temporal and seasonal shifts, while SVR remains relevant as an adaptive model for extreme risk analysis. Overall, this study contributes to the development of automated feature engineering, reproducible climatological forecasting pipelines, and probabilistic modeling frameworks for rainfall prediction under uncertainty in tropical regions.