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Hendrati Dwi Mulyaningsih
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ijmesh@researchsynergypress.com
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Jl. Nyaman No 31 Komplek Sinergi Antapani Bandung, Indonesia
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International Journal of Management, Entrepreneurship, Social Science and Humanities (IJMESH)
Published by Research Synergy Foundation
ISSN : -     EISSN : 25800981     DOI : https://doi.org/10.31098/ijmesh
Core Subject : Economy, Humanities, Science, Social,
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Social Sciences
The journal has an international perspective on Management, entrepreneurship, Social Science and Humanities and publishes conceptual papers and empirical studies which bring together issues of interest to academic researchers and educators, policy-makers and practitioners worldwide. The editorial team encourages quality submissions which advance the study of Entrepreneurship including entrepreneurs behavior, Social entrepreneurship, Social enterprise, small medium enterprise, small economics; Management includes Operational management, People management, knowledge management, Finance, Marketing management, business administration, International business, Business communication, human resource, organization behavior; Social Science inlcudes Psychology, law, Language, sociology, Government science, Community, community development, politic and social science, culture; Humanities inculdes Human right, women empowerment, conflict resolution, middle east conflict
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 172 Documents
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Halal Certification and Culinary Traditions: Rethinking Cultural Food Practices in Muslim-Majority Indonesia Encu M Syamsul; Elis badriah; Nur'aeni Nur'aeni; Anggi Prayitno
International Journal of Management, Entrepreneurship, Social Science and Humanities Vol. 10 No. 1 (2026): January - June Volume
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores the relationship between halal certification and culinary traditions in Indonesia, the world's largest Muslim-majority country. Despite the government's efforts to expand halal certification through formal regulations, the extent to which certification influences everyday food consumption practices remains unclear. Using a mixed-methods approach, this research combines quantitative survey data with qualitative interviews involving Muslim consumers and food vendors in Majalengka Regency, West Java. The findings reveal a significant gap between awareness and behavior. While participants generally understand the concept of halal certification and can recognize the official halal logo, most do not consider certification a primary factor in their food purchasing decisions. Instead, food choices are largely influenced by cultural familiarity, affordability, and trust within the local community. Traditional foods are commonly perceived as inherently halal due to their local origins and preparation by Muslim vendors. The study concludes that halal certification functions more as a regulatory mechanism than as a cultural determinant of consumption behavior. These findings highlight the importance of developing culturally grounded halal governance that bridges formal regulatory frameworks and community-based values. This research contributes to the broader discourse on religion, culture, and governance by demonstrating that halal practices in Indonesia are shaped not only by institutional compliance but also by social trust, cultural traditions, and everyday ethical considerations.
Multi-Branch Transformers for Stock Market Prediction using Previous Market Data and News Articles Lamia Rahim Laibi
International Journal of Management, Entrepreneurship, Social Science and Humanities Vol. 10 No. 1 (2026): January - June Volume
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ijmesh.v10i1.4281

Abstract

The stock market is a complex and dynamic system influenced by numerous factors, including technical indicators, financial news, and long-term historical price movements, among others. Understanding and accurately forecasting market behavior requires integrating diverse data sources and identifying underlying trends across multiple modalities. The primary objective of this research is to demonstrate the advantages of employing multi-branch Transformer architectures for managing multimodal financial data and to evaluate the model's effectiveness in predicting stock market trends over short, medium, and long term horizons. In this study, we investigate the implementation of a multi-branch Transformer model designed to forecast stock market prices by integrating multiple data sources, such as news articles and historical market data, over extended periods. The proposed architecture comprises two main branches: the first is a BERT based Transformer that processes textual information related to daily stock performance, while the second is an LSTM based neural network that analyzes long term historical price data. After the feature extraction and processing stages, the outputs from both branches are fused through dedicated layers to enable highly accurate and efficient stock price predictions. Leveraging advanced artificial intelligence, particularly deep Transformer architectures, the proposed multi-branch model processes heterogeneous financial data simultaneously, significantly improving forecasting accuracy and predictive capability. The model achieves a mean square error (MSE) of 0.0006, demonstrating its strong performance and minimal loss value. This study underscores the potential of multi-branch Transformer architectures to seamlessly integrate textual and numerical financial information, offering a robust and advanced framework for stock market prediction and trend analysis. The proposed approach relies on large scale datasets, which pose challenges related to data quality, accessibility, and processing efficiency. Furthermore, the model's substantial computational requirements may limit its practicality for small organizations or institutions with constrained resources.

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