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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 13 Documents
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Search results for , issue "vol. 6 no. 2 (2026)" : 13 Documents clear
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.
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.
Designing an Intelligent Decision Support System for Evaluating Teaching Effectiveness in Technology-Enhanced Classrooms Ramadiani Ramadiani; Azainil Azainil; Kenya Permata Kusumadewi; Guellica Agnesia Claudia Thanos; Toong Hai Sam
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.daengku4848

Abstract

The rapid digital transformation of education has significantly increased the adoption of technology-enhanced classrooms, generating substantial educational data that can support intelligent instructional evaluation. However, conventional teacher assessment systems remain limited by subjectivity, inconsistent evaluation standards, and the inability to analyze multidimensional learning analytics data effectively. This study aims to design an Intelligent Decision Support System (IDSS) for evaluating teaching effectiveness in smart classroom environments using the ELECTRE (Elimination and Choice Translating Reality) method integrated with Artificial Intelligence (AI)-based educational analytics. The proposed framework combines learning analytics indicators, machine learning models, and outranking-based multi-criteria decision-making to support transparent and data-driven educational governance. The evaluation criteria include student engagement, attendance rate, assignment completion, student satisfaction, learning outcomes, classroom interaction, technology integration, and instructor responsiveness. The computational process involved decision matrix construction, normalization, weighted normalization, concordance-discordance analysis, and aggregate dominance evaluation. The results demonstrated that the ELECTRE method effectively identified dominant teaching alternatives and handled conflicting instructional criteria systematically. Teacher 3 achieved the highest performance ranking due to superior instructional performance across all evaluation indicators. Additionally, AI-based predictive analysis improved evaluation accuracy and instructional pattern identification within technology-enhanced classrooms. The study contributes theoretically by extending the application of ELECTRE within intelligent educational DSS frameworks and practically by providing educational institutions with a scalable and transparent mechanism for evaluating teaching effectiveness. The proposed system supports smart educational governance, data-driven decision-making, and sustainable classroom quality assurance in digital learning ecosystems.
Two Decades in Five Years: Mapping Digital HR, Workflow Automation, and Innovation Ecosystem Research (2020–2025) Arciana Damayanti; Asep Miftahuddin; Rofi Rofaida; Yoga Perdana; Rizqiana Arifatul Husna; Juliana Juliana
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.daengku4894

Abstract

Digital transformation has reshaped human resource management (HRM), giving rise to digital HR practices, workflow automation and innovation-ecosystem approaches. However, the literature on Digital HRM, electronic HRM (e-HRM), and innovation ecosystems remains scattered across disciplines and themes, leaving the field's intellectual structure, thematic evolution, and future directions only partially understood. This study reviews and maps research on digital human resource management, workflow automation, and innovation ecosystems published between 2020 and 2025 using a systematic literature review (SLR) combined with bibliometric analysis and following the PRISMA 2020 framework for a transparent and reproducible selection process. An initial search of the Scopus database returned 2,163 documents for review. After applying predefined inclusion and exclusion criteria, 244 journal articles were retained and analysed in Bibliometrix and Biblioshiny to examine publication trends, influential authors, journals, countries, and institutions, along with keyword co-occurrence and thematic structures. Research activity rose sharply over the period, at an annual growth rate of 23.84%, with the conceptual core of the field centring on e-HRM, digital HRM, digital transformation, workflow automation and innovation ecosystems. The thematic map identified technology, Industry 4.0, Society 5.0, and helix-based innovation models as the dominant driving themes. The Triple Helix and Quintuple Helix frameworks are well established, whereas Pentahelix-related work remains underexplored and is still emerging. This study offers a comprehensive picture of the intellectual landscape of digital HR transformation and innovation-ecosystem research and points to a clear opportunity for future work: bringing together workflow automation, digital HR practices, and Pentahelix-based collaboration to support sustainable workforce transformation and organizational innovation.
Mapping the Digital Ecosystem of Muslim-Friendly Tourism: Stakeholder Networks, Sentiment, and Themes on X Asep Miftahuddin; Juliana Juliana; Lazuardi Imani Hakam; Rizqiana Arifatul Husna; Muhammad Apriandito Arya Saputra; Budhi Pamungkas Gautama
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.daengku4895

Abstract

The rise of digital communication has reshaped the way tourism stakeholders interact, share information, and influence destination development. Muslim-Friendly Tourism has become one of the fastest-growing segments of the global industry; however, it has rarely been studied through a digital ecosystem lens. This study examines the halal tourism ecosystem by analyzing halal tourism conversations on X (formerly Twitter), mapping stakeholder interaction patterns, public sentiment, emotional responses, and dominant discussion themes. A dataset of 2,000 posts was collected via the SocialX platform between January 30, 2025, and March 30, 2026, and analyzed through an integrated framework combining Social Network Analysis, trend analysis, sentiment and emotion analysis, text clustering, topic modeling, and text network analysis. The network proved highly fragmented with 1,725 nodes, 1,417 directed edges, and 725 communities, reflecting the wide range of stakeholders involved; however, a few influential actors emerged as information brokers linking otherwise separate communities. Sentiment was predominantly neutral (46.75%), with positive sentiment (35.70%) clearly outweighing negative sentiment (17.55%), while happiness dominated the emotional landscape at 88.45%. The thematic analyses returned 14 discussion clusters and 117 topics, and the semantic network revealed three interconnected domains underpinning the ecosystem: the Destination and Policy Ecosystem, the Muslim Traveler Experience Ecosystem, and the Global Halal Travel Ecosystem. These findings extend Digital Ecosystem Theory to Muslim-Friendly Tourism and underscore the role of digital platforms in enabling stakeholder interaction, knowledge exchange, and value co-creation, offering practical guidance for policymakers, destination management organizations, and tourism businesses seeking to strengthen digital engagement and competitiveness
Instagram Marketing for Brand Awareness in Certification and Training MSMEs: Insights from Social Media Analytics Dian Addinna; Gilang Garnadi Suryadi; Yosep Hernawan; Edi Suryadi; Asep Miftahuddin; Rizqiana Arifatul Husna
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.daengku4896

Abstract

Early-stage micro, small, and medium enterprises (MSMEs) in the certification and training sector often struggle to build brand awareness on Instagram, where success depends heavily on trust, competence, and credibility. Most prior research has measured the effect of social media marketing through surveys rather than producing a directly applicable, data-driven model, and the certification services sector remains underexplored. This study addresses that gap by analysing Instagram discourse on professional certification and formulating an evidence-based Instagram marketing model. Using a social media analytics approach, 2,000 posts were collected through the SocialX application with the hashtag #sertifikasiprofesi and examined through five procedures: word cloud analysis, text network analysis, emotion analysis using an Indonesian RoBERTa model, sentiment network analysis, and BERTopic modelling. The findings show that the discourse is anchored in competency- and institution-oriented language (notably recognised schemes such as BNSP and LSP), framed around tangible career benefits, expressed through predominantly neutral-to-positive sentiment, and circulated within a sparse, broadcast-oriented network (288 nodes, 240 edges, density 0.6%) dominated by a few central institutional accounts. Based on these findings, this study proposes a marketing model that emphasises recognised credentials, concrete career outcomes, distinctive storytelling, cross-platform integration, and collaboration with influential accounts. The model is analytically derived; its implementation and evaluation are recommended for future research.
Supply Chain Efficiency Analysis of Rice Commodities in Improving Farmers' Profit Margins in Indramayu Regency Ikhsan Nendi; Chiska Nova Harsela; Feri Hardiyanto; Siti Komara; Anisa Ayu Dwi Lestari
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.daengku4901

Abstract

This study investigates the supply chain efficiency of rice commodities in Indramayu Regency and its implications for improving farmers' profit margins. Indramayu Regency is one of Indonesia's principal rice-producing regions, yet farmers continue to receive disproportionately low returns due to inefficiencies across the supply chain, including excessive intermediary layers, high post-harvest losses, and weak market integration. This study employs a quantitative approach using survey data from 120 rice farmers and 45 supply chain actors selected through stratified random sampling. Data were collected between January and June 2024. Analytical methods include marketing margin analysis, farmer's share analysis, Supply Chain Operations Reference (SCOR) efficiency scoring, and multiple regression to identify determinants of profit margin. The results show that farmers retain only 42.6% of the final consumer price (Channel III), compared to 68.4% in the shortest marketing channel (Channel I). Supply chain efficiency scores averaged 0.73 across all actors, with rice millers recording the highest efficiency (0.87) and farmers the lowest (0.61). Regression analysis confirms that channel choice, access to milling technology, and cooperative membership significantly improve profit margins. The findings suggest that policy interventions focused on reducing intermediary dependency, strengthening farmer cooperatives, and investing in post-harvest infrastructure can substantially increase rice farmers' income in Indramayu Regency.
Why Very Low Leverage Varies Across ASEAN: A Dynamic Panel Perspective Maya Sari; Netti Siska N; Nurhuda Nizar; Azreen Roslan; Inomjon Quadratov
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.daengku4907

Abstract

This study investigates the determinants of very low leverage (VLL) among publicly listed non-financial firms in Malaysia, Indonesia and Singapore. The study evaluates whether family ownership and firm-level financial fundamentals shape firms' decisions to maintain extremely low debt levels. An unbalanced panel of 10,160 firm-year observations from 2015 to 2024 is analysed using the two-step System GMM estimator to address endogeneity, dynamic persistence and unobserved heterogeneity. Key explanatory variables include family ownership, profitability, liquidity and operating cash flow, with alternative leverage thresholds used for validation. Results show that capital structure persistence is the strongest predictor of VLL, with firms maintaining a 55 to 57% likelihood of staying in VLL positions across periods. Family ownership does not significantly influence VLL behaviour, challenging agency- and socioemotional wealth-based expectations. Financial fundamentals only matter in Malaysia; their effects disappear in Indonesia and Singapore once dynamic endogeneity is controlled. The findings reveal that several relationships identified in static models are artefacts of endogeneity bias. The strong persistence of VLL suggests that initial financing decisions have long-term effects, highlighting the need for continuous capital structure reassessment. Policymakers should consider institutional differences: Malaysia's relationship-based banking environment reinforces reliance on internal liquidity, while Indonesia requires stronger market infrastructure and creditor protections. Investors should interpret low leverage cautiously, as it may reflect historical path dependence rather than current firm performance. This study provides one of the first comparative dynamic-panel analyses of VLL behaviour in ASEAN markets using System GMM. It advances the capital structure literature by demonstrating that persistence dominates firm fundamentals and that family ownership does not determine extreme leverage choices. The study also clarifies methodological distortions found in static capital structure research
Examining the Relationships among Servant Leadership, Organizational Commitment, and Job Satisfaction within Government Organizations in Cambodia Sarom Mok; Ramy Chhun; Mengheang Hor; Kosal Thay; Chamnan Sok
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.daengku4921

Abstract

Although servant leadership (SELE) has been understudied, especially in the public sector, it has been a popular topic in contemporary leadership theories and has been linked to outcomes like job satisfaction (JOSA) and organizational commitment (ORCO). Given its importance, this study was to ascertain if official JOSA and ORCO were predicted by government official perceptions of SELE, and whether ORCO moderated the relationship between SELE and JOSA. This study used a correlational research design. A structured questionnaire was used to gather data from 224 government officials from various departments both in leadership and non-leadership roles. The Servant Leadership Seven, the Organizational Commitment Questionnaire, and the Abridged Job Descriptive Index were among the online surveys used to gather data for the study. Multiple regression to examine moderation effects, an independent-samples t-test, and Pearson correlations were among the statistical methods used. The findings showed a correlation between higher levels of ORCO and JOSA among government officials and higher perceptions of SELE. There was a significant positive correlation found between department leader JOSA, top management, and SELE, indicating that SELE is linked to government official attitudes at several organizational levels. The results also revealed that managerial self-ratings of SELE differed significantly from non-managerial ratings, with leaders giving their leadership behaviors higher ratings than officials. While organizational commitment and servant leadership both strongly predicted job satisfaction, the moderation analysis revealed that ORCO did not moderate the connection between SELE and JOSA. The findings imply that staff morale and institutional loyalty are positively impacted by SELE. The results emphasize how crucial SELE is in influencing official attitudes in government organization settings and imply that its beneficial effects hold true for all government official roles.

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