Journal of Hunan University Natural Sciences

This study examines the institutional design of Indonesia’s representative bodies - the House of Representatives of the Republic of Indonesia (DPR RI) and the Regional Representative Council of the Republic of Indonesia (DPD RI) - with particular attention to their constitutional status, functions, and competences within Indonesia’s democratic constitutional framework. Using a doctrinal (normative juridical) legal research method, the study analyzes relevant constitutional provisions, statutory regulations, judicial decisions, and constitutional doctrines governing Indonesia’s bicameral legislature.
The findings reveal structural and functional deficiencies within Indonesia’s representative system that potentially undermine legislative effectiveness and equitable national development. These deficiencies include the asymmetrical distribution of legislative authority between the two chambers, overlapping competences, and procedural constraints that significantly restrict the DPD RI’s substantive participation in the lawmaking process. Such institutional imbalances weaken the system of checks and balances envisioned in a democratic constitutional state and limit the effectiveness of bicameralism as a mechanism for territorial representation.
In response, this study proposes a strategic restructuring of Indonesia’s representative institutions aimed at strengthening democratic accountability and institutional equilibrium. The proposed reforms consist of two principal measures: (1) permitting independent (non-party-affiliated) candidacy for membership in the DPR RI in order to broaden political inclusion and enhance representational diversity; and (2) establishing functional and authority parity between the DPR RI and the DPD RI to reinforce genuine bicameralism and improve legislative coherence. The study concludes that such reforms are essential to enhancing democratic representation, consolidating checks and balances, and fostering inclusive and sustainable national development in Indonesia.

Keywords: Bicameralism; Constitutional Reform; Legislative Institutional Design; Democratic Representation; Checks and Balances; Territorial Representation; National Development; Indonesia.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.6

This study investigates recent advancements in machine learning (ML) algorithms for Big Data analytics, with a focus on scalability, real-time processing, and ethical considerations. A qualitative literature review was performed, examining recent ML developments through thematic analysis of peer-reviewed publications and industry reports. The findings highlight notable improvements in scalability via distributed computing frameworks such as Apache Spark and Hadoop, as well as enhanced real-time processing achieved through online learning techniques. Nevertheless, challenges persist in maintaining model accuracy in the presence of noisy data and mitigating algorithmic bias. Ethical issues concerning fairness, transparency, and accountability were also identified. This research advances understanding of ML's role in Big Data applications and provides practical insights for deploying scalable, interpretable, and ethically responsible models across industries. Future work should focus on refining hybrid approaches and evaluating their applicability in real-world scenarios.

Keywords: Machine Learning; Big Data Analytics; Scalability; Real-Time Processing; Ethical AI; Distributed Computing; Online Learning.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.5

This study examines the joint moderating effects of government policy and financial support on destination loyalty in the Wakatobi National Strategic Tourism Area, Indonesia. Adopting a quantitative approach and PLS-SEM analysis, data were collected from 160 repeat visitors using purposive sampling. The results indicate that destination image does not directly influence destination loyalty (β = 0.063, p = 0.320) but has a significant impact on tourist engagement (β = 0.665, p < 0.001) and tourist satisfaction (β = 0.580, p < 0.001). Both tourist engagement (β = 0.416, p < 0.001) and tourist satisfaction (β = 0.350, p = 0.001) significantly affect destination loyalty and fully mediate the relationship between destination image and loyalty. Notably, the combined moderating effect of government policy and financial support significantly weakens the relationship between destination image and destination loyalty (β = -0.197, p = 0.025), suggesting that excessive government intervention may create image-reality gaps that hinder loyalty formation. The proposed model explains 80.7% of the variance in destination loyalty (R² = 0.807). These findings contribute to the literature on destination loyalty by highlighting the paradoxical effects of government intervention and offer practical implications for sustainable tourism development in priority destinations.

Keywords: Destination loyalty; Government policy; Financial support; Tourist engagement; Tourist satisfaction; Moderation effects.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.4

This paper investigates the Klein–Gordon equation within the framework of fractional calculus by incorporating non-integer time and spatial derivatives to model physical processes characterized by memory effects and nonlocal interactions. Fractional operators in the Riemann–Liouville and Caputo senses are employed, together with the Laplace transform and the Mittag–Leffler function, to reformulate and solve the associated initial value problem. The resulting solutions are examined both analytically and graphically in order to evaluate the influence of fractional orders on the temporal and spatial dynamics of the system.
The analysis demonstrates that small variations in the fractional orders lead to significant qualitative changes in the behavior of the scalar field, indicating transitions between classical and fractional regimes. In particular, anomalous damping, power-law decay, and nonlocal propagation phenomena are observed, which are intrinsically linked to the properties of the Mittag–Leffler function.
The principal contribution of this study is the systematic characterization of the role of fractional order in the Klein–Gordon equation. The proposed fractional model generalizes the classical formulation and provides a suitable mathematical framework for describing systems exhibiting anomalous dissipation, long-term memory, and non-Euclidean geometric effects.

Keywords: Fractional calculus; Klein–Gordon equation; Caputo derivative; Riemann–Liouville derivative; Laplace transform; Mittag–Leffler function; nonlocal dynamics; memory effects.

DOI https://doi.org/10.55463/issn.1674-2974.52.11.17

Hospital triage requires rapid and accurate assessment of vital signs, supported by well-trained personnel within a robust emergency medical system. However, in remote regions of Colombia, access to skilled staff and monitoring equipment is limited. This study proposes a machine learning framework to estimate blood pressure using photoplethysmography (PPG) signals, demographic data, and comorbidity information within an automated IoT-based triage system. As no prior machine learning–based solutions have addressed patient health status prediction in isolated Colombian regions, this framework aims to provide a complementary triage system in areas lacking expert support. The system integrates a forearm-worn wearable device with a kiosk to collect data, generating 20 input features encompassing demographic/comorbidity information, summary vital signs, and PPG morphology and variability descriptors. Three regression models - feedforward neural network, XGBoost, and Random Forest - are trained and compared for simultaneous estimation of systolic and diastolic blood pressure. Training uses a public short-record PPG dataset comprising 657 signal segments from 219 subjects with subject-wise cross-validation; external validation is performed on the PhysioNet Pulse Transit Time-PPG database. Tree-based ensemble models outperform the neural network on the main dataset, with XGBoost achieving the best performance for both systolic and diastolic blood pressure. These findings highlight ensemble models as competitive and interpretable alternatives for PPG-based blood pressure estimation, supporting their integration into IoT-enabled triage systems to improve evidence-based patient prioritization, especially in underserved regions.

Keywords: Hospital Triage; Machine Learning; Blood Pressure Estimation; Photoplethysmography (PPG); IoT; XGBoost; Random Forest.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.3