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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Mimbar Agama Budaya
Ajif Yunizar Pratama
Kyushu Institut of Technology, Fukuoka, Japan
Religion Arts Humanities Computer Science & IT Education Social Sciences

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How Muslim Women in Jakarta Negotiate the ‘My Body, My Rules’ Narrative on Social Media: A Communication Perspective on Feminism and Islamic Values Naya Adisti Prameswari Putrie; Feri Fahrianto; Rino Febrianno Boer; Ajif Yunizar Pratama
Mimbar Agama Budaya Vol. 43 No. 1 (2026)
Publisher : Center for Research and Publication (PUSLITPEN), UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/mimbar.v43i1.51639

Abstract

A global wave of digital feminism has emerged, and the slogan “My Body, My Rules (abbreviated MBMR)” has spread widely across social platforms including TikTok and Instagram. However, after this discourse entered Indonesia’s Muslim communities, it required reinterpretation through integration with three core Islamic values: amanah, iffah, and maslahah, and the framing of bodily autonomy thus generated cross-cultural tensions. The author team of this paper centered their research on the core question of how Muslim women aged 20 to 40 in Jakarta negotiate and address this tension. Under a qualitative research framework, the study adopted a dual-path approach combining semi-structured interviews with digital discourse analysis. The study found that participants developed a form of negotiated agency, and that social media holds the dual attribute of being both an empowerment space and an arena for ideological contestation. This study supplements relevant research outcomes in the field of communication studies, proving that after localization, global feminist discourse can generate a hybrid agency that integrates individual empowerment and religious responsibility, and reshape the identity construction logic of contemporary digital communication.
Performance Comparison of LSTM, XGBoost, and Residual-Correction Hybrid LSTM–XGBoost Models for Bitcoin Price Forecasting Ihsan Maulana Anwas; Feri Fahrianto; Imam Marzuki Shofi; Ajif Yunizar Pratama
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5983

Abstract

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.
Co-Authors Feri Fahrianto Feri Fahrianto Ihsan Maulana Anwas Imam Marzuki Shofi Naya Adisti Prameswari Putrie Rino Febrianno Boer