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Daengku: Journal of Humanities and Social Sciences Innovation
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 27756165     DOI : https://doi.org/10.35877/454RI.daengkuv1i1
Core Subject : Humanities, Education, Social,
Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other
The Daengku seeks to publish high-quality research papers, review articles, and book reviews that make a contribution to knowledge through the application and development of theories, new data exploration, and/or scientific analysis of salient policy issues. The Scope of the Daengku includes the following areas: Social Sciences: Anthropology, Asian Studies, Communication, Demography, Development, Gender Studies, Government & Public Policy, Human Ecology, International Relations, Media Studies, Peace and Conflict, Political Science, Science, Technology & Society, Sociology. Humanities: Cultural Studies, Education, History, Human Geography, Linguistics, Philosophy, Religion.
Arjuna Subject : Umum - Umum
Articles 485 Documents
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Navigating Barriers: Access to Justice and Legal Protection for Rohingya Refugees in Bangladesh Badsha Mia; Joynab Binta Mariam Kali; Faijul Islam
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4387

Abstract

The Rohingya refugee crisis is an immense humanitarian challenge in South Asia. More than one million Rohingya individuals from Myanmar currently reside in refugee camps in Cox’s Bazar, Bangladesh, due to persecution and violence in their native country. Despite Bangladesh's considerable humanitarian gesture in offering sanctuary, the lack of a legal refugee protection system has impeded the Rohingyas' ability to seek justice. Protection of justice for these stateless Rohingya refugees entails many legal, institutional, and socio-political barriers. Ensuring access to justice for these stateless Rohingya refugees requires navigating complex legal, institutional, and socio-political barriers. This study examines the justice-seeking processes available to Rohingya refugees in Bangladesh, identifies the principal barriers to accessing these mechanisms, and evaluates the effectiveness of existing legal and policy frameworks. The study used a mixed-method approach, integrating qualitative interviews, focus group discussions (FGDs), and legal content analysis, revealing that structural discrimination, absence of legal identity, inadequate institutional capacity, and restrictive national laws substantially impede access to justice. The findings reaffirm the pressing need for policy reforms, legal empowerment efforts, and enhanced institutional collaboration across state agencies, NGOs, and other international organizations. The study suggests policy recommendations to incorporate access to justice into a comprehensive framework of human rights and sustainable solutions for the Rohingya refugee crisis in Bangladesh.
Does Diplomatic Influence Pay? A Gravity Model Estimation of the Effect of Morocco’s Soft Power Assets on its Bilateral Trade Volumes in Africa Hamid Fayou; Nabil Boubrahimi
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4569

Abstract

This study investigates whether Morocco’s soft power assets its (cultural religious, economic, and diplomatic influence) translate into tangible economic benefits in the form of increased bilateral trade with African partners. Using an augmented gravity model of trade estimated with Poisson Pseudo-Maximum Likelihood (PPML) for a panel of 45 African countries over the period 2010–2024, we quantify the impact of various soft power proxies on Morocco’s trade flows. The results indicate that soft-power instruments, particularly cultural diplomacy, religious ties, and active peacekeeping contributions, exert a statistically significant and economically meaningful positive effect on trade volumes, even after controlling for traditional gravity determinants such as GDP, distance, and colonial links. The findings suggest that Morocco’s deliberate soft-power strategy in Africa is not merely a political tool but also a viable economic diplomacy that can enhance regional trade integration. Policy implications include the continued investment in soft-power channels as a complement to traditional trade liberalization measures.
Implementasi Sistem Integrasi Layanan Deklarasi Keimigrasian All Indonesia di Tempat Pemeriksaan Imigrasi Bandara Ngurah Rai, Bali Pranaditha, Ida Bagus Yogi; Widanti, Ni Putu Tirka; Widnyani, Ida Ayu Putu Sri
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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

Abstract

The objective of this study is to analyze the implementation of the All Indonesia Immigration Declaration Service Integration System at the Ngurah Rai Airport Immigration Checkpoint in Bali, to identify obstacles and challenges, and to formulate strategic and operational solutions. The impetus for this research stems from two key factors. Firstly, the number of international travelers has reached 15 million per year, exhibiting a 14% growth rate. Secondly, the fragmentation of health, customs, and immigration declaration systems has led to inefficiencies and security gaps in the past. The All Indonesia system, which has been fully operational since October 2025, integrates all declarations into a single digital platform with an H-3 arrival declaration feature. The present study employs a descriptive qualitative method with a case study approach. The data were collected through a variety of methods, including in-depth interviews with key informants from the Ngurah Rai Class I Special TPI Immigration Office, direct observation in the international arrival area, document review, and analysis of system submission data from October 2025 to January 2026. The data analysis employed the interactive model developed by Miles and Huberman, with Max Weber's Rational Bureaucracy theory serving as the analytical framework. The study's findings indicate that the system's implementation successfully reduced inspection times by 60–70 percent at manual counters and 80 percent at autogates. Furthermore, the system was adopted by 2.5 million passengers within four months. However, a number of technical challenges were identified, including system disruptions and recurring OCR errors, low pre-arrival data entry rates among Indonesian citizens (30–38 percent), suboptimal cross-agency data interoperability, and limited multilingual support. Solutions were formulated in the Adaptive Digital Bureaucracy Model, which encompasses the strengthening of a resilient system architecture, adaptive knowledge management, structured collaborative governance, user-centered design, and transformational change management.
Contribution of Work Environment on Employee Retention in the Financial Sector of Nepal Prakash Bahadur Chand
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4779

Abstract

Employee retention is a critical challenge for organizations globally, affecting operational efficiency, morale, and long-term success. This study investigates the impact of the work environment encompassing both physical (workspace layout, safety) and psychological factors (organizational culture, leadership and management practices) on employee retention in Nepal’s financial sector. By identifying key environmental determinants, the research aims to provide actionable insights for organizations seeking to enhance retention strategies. This study adopts a descriptive and analytical research design, employing a purposive sampling technique to collect data from 385 respondents in Nepal’s financial sector. A structured, closed-ended questionnaire using a five-point Likert scale was administered to assess employee perceptions of workspace layout, safety, organizational culture, and leadership. Data analysis was conducted using SPSS to evaluate the relationship between work environment factors and retention. Workspace layout, organizational culture, and leadership & management significantly enhance employee retention. Employees reported high satisfaction with leadership and management, followed by workspace layout and organizational culture, underscoring their pivotal role in retention. While safety remains important, its direct impact on retention is relatively minimal, suggesting room for improvement. This study adds to existing literature by examining employee retention in Nepal’s financial sector a relatively unexplored context—while integrating both physical and psychological work environment factors for a comprehensive analysis. By providing empirical evidence on their varying impacts, the research helps organizations prioritize retention strategies. Additionally, it addresses a regional research gap and offers practical insights for policymakers and business leaders in developing economies.
Analysis and Forecasting of Unilever Indonesia Stock Prices Using a Long Short-Term Memory (LSTM) Model Sitti Masyitah Meliyana; Abdul Rahman
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4787

Abstract

This study aims to forecast the stock price of Unilever Indonesia using the Long Short-Term Memory (LSTM) model. The dataset consists of weekly stock price data from May 2015 to May 2025, representing a financial time series with nonlinear and dynamic patterns. The LSTM model is employed due to its capability to capture long-term dependencies in sequential data. To evaluate model performance, the dataset is partitioned into three training–testing scenarios, namely 90:10, 80:20, and 70:30. Model accuracy is assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that the best predictive performance is achieved using the 90:10 data split, yielding the lowest MAPE value of 6.973%, which falls into the highly accurate forecasting category. In comparison, the 70:30, and 80:20 scenarios produce higher MAPE values of 13.732% and 17.263% respectively. These findings demonstrate that increasing the proportion of training data significantly improves the performance of the LSTM model in forecasting stock prices. This study highlights the effectiveness of LSTM in modeling financial time series and provides practical insights for data-driven decision-making in stock market analysis.
Forecasting Farmer Exchange Rates as a Welfare Proxy: BetaSutte's Role in Predicting Agricultural Income Stability in Indonesia Ansari Saleh Ahmar
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku1036

Abstract

This study presents BetaSutte — a novel hybrid forecasting model applying the four-lag ?-Sutte Indicator to OLS-detrended residuals rather than to the raw level series — and evaluates it on Indonesia’s monthly Nilai Tukar Petani (NTP, Farmer Exchange Rate), a composite agricultural welfare index for over 40 million farming households. Using 84 monthly observations from January 2019 to December 2025 (Badan Pusat Statistik), the model separates NTP into a linear trend component and a residual , applies the ?-Sutte formula to the stationary residual domain, and generates forecasts as with ? optimised by grid search. Calibrating on the first 60 observations (January 2019–December 2023), the OLS trend explains 82.75% of NTP variance , and the optimal ? = 0.30 yields in-sample RMSE = 1.6887, MAE = 1.3695, and MAPE = 1.2881% — an 11.5% RMSE reduction versus the trend-only baseline. Crucially, two full years of genuinely out-of-sample validation (January 2024–December 2025, n = 24) confirm BetaSutte’s operational superiority: RMSE = 5.4841 versus 6.0782 for trend-only, a 9.8% improvement representing 112 months of independently collected data never seen during calibration. Residuals are normally distributed (Shapiro-Wilk p = 0.130), confirming well-conditioned model inputs. The full-sample retrained model (n = 84) estimates , forecasting January 2026 NTP at 123.91. This study constitutes the first BetaSutte application to a composite agricultural welfare index with two-year prospective out-of-sample validation.
Integration of the Al-Qur'an and Science to Improve the Intellectual Intelligence of Graduate Quality Baihaqi Aziz; M Maskuri; Dian Mohammad Hakim
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4830

Abstract

In the era of globalization, science is dominated by the West, while the contribution of Muslims is often considered minimal. SMA Trensains Tebuireng 2 Jombang integrates the Qur’an and science into the curriculum to improve students’ religiosity and intellectual intelligence. This study aims to examine the verses of the Qur’an, the process, and the science integration model to enhance graduates’ quality. With a qualitative phenomenological approach, data were obtained through observation, interviews, and documentation. The results show integration based on verses such as QS. Al-Syu’ara: 4, QS. Al-Rum: 25, and QS. Al-Insan: 17, which discusses creation and natural phenomena. The process involves a thematic curriculum, an approach to scientific interpretation, and discussion-based learning and presentations. Supporting factors include laboratories, libraries, and institutional support, although constrained by limited human resources and media. This integration model is efficacious in improving intellectual intelligence and religiosity, producing superior graduates based on Islamic values.
Artificial Intelligence–Driven Learning Analytics for Enhancing Student Engagement and Academic Performance in Digital Learning Environments Dendi Pratama; Eka Maya S.S. Ciptaningsih; Ramadiani Ramadiani; Achmad Fawaid; Winci Firdaus; Bambang Sudarsono
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4835

Abstract

The quick development of digital learning ecosystems after educational reform in the post-pandemic era requires an increase in intelligent monitoring systems that assess student engagement and predict academic performance. Traditional learning assessment techniques frequently have flaws when detecting early disengagement signals and initiating corrective actions for at-risk students. This research proposes an Artificial Intelligence (AI)-Driven Learning Analytics method that aims to improve student engagement monitoring and academic performance prediction in digital learning environments. A fabricated LMS-based educational dataset was used, which includes behavior analysis, engagement factors, academic factors, interaction factors, and temporal learning behavior obtained from LMSs like Moodle, Google Classroom, and Canvas. Several machine learning models, including Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory (LSTM), were tested. The results revealed that the LSTM model had the best performance with an accuracy rate of 95% and a ROC-AUC value of 0.98, highlighting the importance of temporal learning behavior in educational prediction systems. Some of the essential engagement factors found to be most effective were assignment submission, quiz score, inactivity period, session length, and login number. The findings make a theoretical contribution to Artificial Intelligence in Education and Learning Analytics by combining multidimensional engagement analysis, temporal behavior modeling, and explainable AI into a unified framework. In practice, the suggested framework can aid adaptive learning, early warning, individualized intervention, and evidence-based education decisions in intelligent digital learning ecosystems.
Hybrid Beats Classical: Why BetaSutte Dominates ARIMA for Emerging Market Inflation Forecasting During Supply Shocks Ahmar, Ansari Saleh
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 5 No. 6 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4836

Abstract

This study demonstrates that hybrid trend-decomposition forecasting (BetaSutte) substantially outperforms classical ARIMA(1,1,1) methods for inflation prediction in emerging markets experiencing supply-shock-driven regime changes. Using Indonesian central bank inflation data spanning September 2021 through October 2024 (50 monthly observations), we partition the sample into 40 in-sample training observations (capturing the Russia-Ukraine supply shock peak of August 2022 at 7.71% and its policy-driven deflation) and 10 out-of-sample evaluation observations (January–October 2024, the critical disinflation recovery phase). BetaSutte achieves out-of-sample RMSE of 0.3516% compared to ARIMA's 0.5377%—a 34.6% reduction in forecast error. Critically, while BetaSutte's in-sample RMSE is 1.73× larger than ARIMA's (4.01 vs. 2.32), this apparent weakness reflects superior generalization: the model deliberately prioritizes trend signal extraction over training-data fitting, discarding noise to minimize out-of-sample prediction errors. The reversal from inferior in-sample to dominant out-of-sample performance is a defining characteristic of parsimonious hybrid methods operating under structural breaks. We attribute BetaSutte's superiority to its explicit decomposition of trend and transitory components, which captures the nonstationary deflation path better than ARIMA's differencing-based approach when regime transitions occur. Policy implications are substantial: central banks targeting inflation via published rate paths can improve forecast-based monetary decisions by adopting hybrid methods. This finding challenges the conventional dominance of ARIMA in finance and economics applications and suggests that emerging market policymakers should evaluate model choice based on out-of-sample rather than in-sample metrics when designing inflation forecasts. The paper provides empirical evidence for the bias-variance trade-off in time-series model selection and offers a practical methodology applicable to commodity-dependent central banks worldwide.
A Hybrid Soft Computing Approach to Inflation Forecasting: HybridSutte Versus Exponential Smoothing Benchmarks in an Emerging Economy Ansari Saleh Ahmar; Abdul Rahman
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4841

Abstract

Central banks in commodity-dependent emerging economies face a structural forecasting challenge: exponential smoothing methods calibrated on supply-shock training windows systematically overproject the downward trend into post-shock stabilisation phases, producing compounding errors that undermine monetary policy communication. This paper proposes HybridSutte, a soft computing model that fuses four-point alpha-Sutte recurrence with exponential smoothing correction, as an alternative to conventional exponential smoothing benchmarks. Monthly year-on-year Consumer Price Index data published by Bank Indonesia cover January 2021 through December 2025 (n = 60 observations), capturing Indonesia's complete monetary policy cycle: COVID-19 demand recovery, Russia-Ukraine commodity supply shock (peak: 5.95%, September 2022), Bank Indonesia's 250 basis-point rate-hike disinflation campaign, and the subsequent 2025 post-shock stabilisation within the 2.5% ± 1% target band. The 51/9 in-sample/out-of-sample partition places the evaluation window (April–December 2025) entirely within the structurally distinct post-shock stabilised regime. HybridSutte achieves out-of-sample RMSE of 0.606% and MAPE of 21.25%, compared with Holt's double exponential smoothing (ETS) RMSE of 3.069% and MAPE of 121.60%, yielding reductions of 80.2% and 82.5%, respectively. The performance advantage grows monotonically with forecast horizon h, reaching a 451.1% cumulative absolute error differential by  = 9. This is the first application of HybridSutte to central bank inflation data in an emerging market and the first to evaluate a soft computing hybrid model across a complete five-year monetary policy cycle. Findings support regime-aware model selection for central bank forecasting departments.

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