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This study aims to systematically analyze the implementation of severance pay policy as a manifestation of post-employment compensation and its profound impact on the harmony of industrial relations within the regulatory framework of the Omnibus Law in Indonesia. Utilizing a Systematic Literature Review (SLR) methodology aligned with the PRISMA framework, this research comprehensively synthesizes data from 25 selected academic articles encompassing human resource management, employment law, and organizational behavior. The findings reveal that the paradigm shift from the previous labor regulations to the Omnibus Law framework has fundamentally altered the calculation mechanisms and statutory floors for severance pay. While these legislative adjustments are strategically designed to enhance organizational agility and mitigate financial distress for corporations, they have engendered substantial apprehension among the workforce regarding the degradation of normative rights. Consequently, this policy transformation presents a critical challenge to sustaining industrial harmony, frequently precipitating labor disputes, diminishing employee morale, and intensifying bipartite conflicts. This study recommends that human resource practitioners proactively develop transparent communication strategies and design complementary post-employment benefit architectures to restore distributive justice. Furthermore, policymakers are urged to institute robust oversight mechanisms to ensure equitable implementation and safeguard worker welfare without compromising long-term business sustainability.

Image Quality Assessment has become an important research area in computer vision due to the increasing use of digital imaging in medical, industrial, and remote sensing applications. This study conducted a Systematic Literature Review (SLR) using articles retrieved exclusively from the Scopus database. A total of fifteen Scopus-indexed articles were selected through the PRISMA process consisting of identification, screening, eligibility, and inclusion stages. The findings indicated that convolutional neural networks, transformer architectures, and no-reference approaches dominated recent studies because of their ability to capture complex visual features and improve evaluation accuracy. The application domain strongly influenced method selection, particularly in medical imaging, industrial quality control, and remote sensing. Deep learning methods achieved high performance but required large datasets and high computational resources. Overall, recent research trends shifted from traditional image enhancement toward artificial intelligence-based quality evaluation.

Tomato (Solanum lycopersicum) is a high-value horticultural commodity in Indonesia, yet its cultivation is frequently disrupted by leaf diseases that are difficult to distinguish visually. Diseases such as Bacterial Spot, Early Blight, and Tomato Yellow Leaf Curl Virus often present overlapping visual symptoms, making early and accurate diagnosis a significant challenge for farmers. The manual identification methods currently in use are inefficient and error-prone, ultimately leading to reduced crop yield  and quality. The general objective of this study is to develop software capable of automatically classifying tomato  leaf diseases. Specifically, this research aims to implement the MobileNetV3 Small architecture based on Convolutional Neural  Network (CNN) with ImageNet pre-trained weights to classify 10 types of tomato leaf diseases. The research methodology encompasses dataset collection from Kaggle comprising 10,000 images (1,000 per class), image pre-processing through resizing to 224x224 pixels, and normalization, as well as hyperparameter optimization (optimizer, learning rate, epoch, batch size) via scheduler. Model performance is evaluated using a confusion matrix encompassing accuracy, precision, recall, and F1-score.

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

Manual teaching journal management at SMA Negeri 1 Kayen frequently leads to documentation inefficiencies, risks of data loss, and challenges in the teaching supervision process. This research tends to design and develop a web-based teacher teaching journal information system employing the CodeIgniter 3 framework with a Model-View-Controller (MVC) architecture. The system was improved employing the Waterfall methodology, comprising requirements analysis, system design, implementation, testing, and maintenance phases. The system integrates four user roles—administrator, teacher, student, and supervisor—to support the digitalization of recording processes, real-time teacher attendance verification by students, and centralized activity monitoring. Black Box Testing outcomes present that all system functions, involving authentication, data management, and reporting, operate 100% in accordance with user requirements. The implementation of this system effectively reduces administrative time compared to manual methods while enhancing the accountability and transparency of teaching journal management within the school. With centralized data integration, this system helps as an effective solution for supporting data-driven managerial decision-making in education.

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments

Evaluating food portions and types in the Free Nutritious Meal (MBG) program is generally still performed manually, which is time-consuming and potentially subjective. This study aims to develop an automated deep learning-based system to efficiently detect food types and estimate their nutritional value. The method used is a quantitative experiment integrating the YOLOv11m architecture for real-time object detection and the Google Gemini 2.5 Flash Large Language Model (LLM) for contextual nutritional estimation reasoning. The model training utilized a dataset of 2,630 food tray images categorized into five classes (fruit, side dish, staple food, vegetable, milk) that had undergone an augmentation process. The results showed that the YOLOv11m model achieved excellent performance with a mean Average Precision (mAP@0.5) of 0.9727 and the highest F1-score of 0.9522 at a confidence threshold of 0.1. Furthermore, validation of the LLM integration demonstrated a high prediction agreement rate of 85%. In conclusion, the combination of the YOLOv11m algorithm and LLM reasoning is capable of detecting and validating nutritional classification quickly and precisely, showing strong potential as an objective nutritional evaluation monitoring solution for large-scale MBG program implementation.

Children's literacy can be improved through interactive learning media. A digital children's story platform, previously a mobile application, required web version development to expand user accessibility via browsers. The main challenge in this development was presenting multimedia content such as text, images, and audio dubbing synchronously scene-by-scene without degrading system performance, as well as facilitating reader interaction through a rating feature. This research aims to design and develop the platform using a Decoupled Architecture approach. The software development method used is Waterfall, encompassing requirement analysis, system design (UML, CDM, and PDM), implementation, testing, and maintenance. During implementation, ReactJS is utilized on the client-side for responsive interface rendering and state management, while Laravel is implemented on the server-side as a RESTful API provider to manage multimedia asset transmission, rating records, and the MySQL database. Functional system testing was evaluated using Black-box Testing, while user experience was validated using Spearman's Rank Correlation. The results indicate that this architectural separation (front-end and back-end) effectively resolves server overload issues commonly found in monolithic architectures and produces a seamless, stable asynchronous data transmission system across various devices.

This study analyzes how digital interface design (UI/UX) and system architecture are manipulated to unethically extract user consent, resulting in personal data commercialization and massive online loan (pinjol) spam marketing. Employing a mixed-methods approach, this research combines descriptive quantitative analysis of digital user perception surveys with qualitative observations involving UI teardowns, privacy policy evaluations, and extensive literature reviews. Initial findings indicate a profound crisis of genuine informed consent, exacerbated by the deployment of dark patterns such as pre-ticked checkboxes, disguised opt-out architectures, and information overload engineered into legal documents. This systematic manipulation facilitates unauthorized personal data distribution to third parties under the guise of legitimate agreement, directly triggering privacy intrusions and psychological distress, including severe anxiety among data subjects. Philosophically and morally, these practices constitute a severe breach of Information Technology Professional Ethics (EPTI). Consequently, this study highlights the urgent need for developer accountability and advocates for the implementation of proactive, transparent ethical design frameworks compliant with Human-Computer Interaction (HCI) standards to reclaim digital autonomy.

Perkembangan teknologi informasi saat ini mendorong organisasi, termasuk Usaha Mikro, Kecil, dan Menengah (UMKM), untuk meningkatkan efisiensi operasional melalui penerapan sistem yang terintegrasi. Penelitian ini bertujuan untuk merancang Enterprise Architecture (EA) pada UMKM Golden Spice menggunakan pendekatan The Open Group Architecture Framework (TOGAF) melalui metode Architecture Development Method (ADM). Permasalahan utama yang dihadapi adalah sistem penjualan dan manajemen stok yang masih dilakukan secara manual dan tidak terintegrasi, sehingga menyebabkan inefisiensi operasional, ketidaksesuaian data, serta keterbatasan dalam pengambilan keputusan. Metode penelitian yang digunakan meliputi observasi, wawancara, dan studi literatur dengan referensi terbaru pada rentang tahun 2021-2026. Hasil penelitian menunjukkan bahwa perancangan Arsitektur Perusahaan yang diusulkan mampu mengintegrasikan sistem penjualan dan manajemen stok secara terstruktur, sehingga meningkatkan efisiensi operasional dan akurasi data. Selain itu, sistem yang diusulkan juga mampu menyediakan informasi secara real-time untuk mendukung pengambilan keputusan berbasis data. Penelitian ini diharapkan dapat menjadi solusi praktis bagi UMKM dalam mendukung transformasi digital serta menjadi referensi bagi penelitian selanjutnya.

. The effort to re-visualize regional architectural elements combined with modern touches is known as neo-vernakular architecture. In North Sumatra, there are various ethnic groups, including Batak, Mandailing, Malay, and others, each possessing rich traditional architectural values. This study aims to identify and analyze traditional architectural elements applied to the façade of the North Sumatra Regional House of Representatives (DPRD) building. The DPRD North Sumatra building is one of the government buildings that functions as a workplace for representatives of the people, council members, and the secretariat in carrying out governmental duties and public services. The building is located on Jalan Imam Bonjol with an area of approximately 8,000 square meters. This study uses a qualitative descriptive method with data collection techniques through direct field surveys and literature studies related to the façade design of the DPRD North Sumatra building. The results show that the neo-vernakular character on the building façade is represented through several architectural elements, such as exposed columns, sunshading, opening forms, and other supporting elements that reflect a combination of traditional and modern architectural features.  

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

The increasing demand for scalable and high-quality digital marketing content has exposed limitations in traditional manual copywriting processes, which are time-intensive and difficult to scale. This research proposes an adaptive AI-driven copywriting framework that integrates a full-stack web architecture with optimized prompt engineering strategies for automated content generation. The system is implemented using React.js for the frontend, Node.js with Express for backend services, and a GPT-based API for language generation. Unlike prior implementations, this research introduces a structured prompt optimization mechanism to enhance content relevance and consistency. Experimental evaluation was conducted using multiple datasets of marketing prompts, with comparisons against baseline GPT usage and manual copywriting. Quantitative results show that the proposed system achieves improvements in BLEU (+18.7%) and ROUGE-L (+21.3%) scores over baseline methods. Human evaluation involving 30 participants indicates a significant increase in perceived content quality, coherence, and persuasiveness (p < 0.05). System performance analysis demonstrates an average response time of 1.8–3.0 seconds and a GTmetrix performance score of 82%. The findings confirm that the proposed framework significantly enhances efficiency, scalability, and content quality, contributing to both applied AI systems and intelligent web-based content production.

Gorontalo Province possesses significant mineral resource potential, particularly gold, silver, and copper, positioning the mining sector as a key driver of regional economic growth. However, a shortage of skilled local labor and the scarcity of vocational educational institutions in the mining field severely hamper human resource development in this sector. This study aims to design a Mining Polytechnic Campus in Gorontalo by applying sustainable architecture principles, encompassing energy efficiency, environmentally friendly materials, sound wastewater management, and user comfort. The research approach involves literature studies, field observations, interviews with relevant stakeholders, and quantitative data analysis regarding resource potential, the number of senior high school students, and educational space requirements. The design results emphasize site arrangement, building mass configuration, utility systems, and interior and exterior spaces that support academic, social, and community activities. The application of sustainable architecture principles is expected to create a campus that not only meets the needs of mining vocational education but also contributes to environmental conservation and sustainable regional development.

The contemporary global economy faces a profound paradox: global Gross Domestic Product (GDP) growth and technological innovation are reaching all-time highs, yet inequality, climate crisis, and geopolitical instability are simultaneously worsening. This article employs Integrated Reality Theory (IRT) as an analytical framework to diagnose the systemic roots of this paradox. By mapping the global economy onto five IRT variables—Energy (E), Information (I), Entropy (S), Consciousness (C), and Evolution (v)—this article demonstrates that the world's economic architecture is experiencing a fundamental disequilibrium: exponential growth in E and I is not matched by adequate management of S. The accumulation of entropy, manifested in three dimensions—global debt, ecological degradation, and social polarization—has reached a critical point where every addition of energy and information only exacerbates systemic instability. This article offers a paradigmatic reorientation through the enhancement of C as the primary strategy for reducing S, while proposing the policy framework of Techno-Spiritual Based Developmentalism (TSBD) as a middle path between market fundamentalism and state fundamentalism. The function Φ = (E × I / S) × C is treated as a heuristic model for diagnosis, not a deterministic law. IRT opens the possibility for developing an economic model that integrates material efficiency, ecological sustainability, and ethical reflexivity into a unified systemic framework.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

The absence of integrated blood stock management in hospitals, relying on current manual procedures, frequently results in delayed services and challenges in fulfilling blood requirements under emergencies. This research seeks to design and build a web-based blood stock monitoring platform called HemoBridge, offering real-time data accessible by the public, healthcare facilities, and the Ministry of Health. The System Development Life Cycle (SDLC) framework with the well-known Waterfall approach was adopted, encompassing needs analysis, system architecture, development, verification, deployment, and upkeep. This study used a qualitative methodology through field observation and literature review. The platform was developed using PHP and the Laravel framework, with HTML, CSS, JavaScript, and MySQL as supporting technologies. Findings indicate HemoBridge was successfully deployed with core features comprising a comprehensive landing page, donor education, role-based access control, an integrated hospital management panel, a Ministry of Health dashboard, and an advanced blood stock search module with referral capability. Validation through Black Box Testing across six primary functions confirmed all features performed as intended. The platform demonstrates the capacity to enhance data management efficiency, accelerate information retrieval, and strengthen evidence-based decision-making in healthcare. Prospective enhancements include automated stock alerts, data encryption, and wider interoperability with health institutions throughout Indonesia.

Gorontalo Province has abundant freshwater resources, yet lacks integrated and sustainable aquaculture facilities. This article presents the conceptual design of a Freshwater Fish Aquaculture Center in Gorontalo Province as a response to this condition, applying Ecological Architecture as the primary design approach. The designed area accommodates various activities including freshwater fish cultivation, education, tourism, as well as research and development of fisheries technology. The method used is a qualitative descriptive approach through literature studies, field observations, interviews, and precedent studies. The design results show that the area can be divided into four main zones: the aquaculture zone, education zone, tourism zone, and supporting zone. The ecological approach is realized through the use of natural lighting and ventilation, environmentally friendly materials, an integrated water management system, and the maximization of green open spaces. This design is expected to increase fisheries productivity while promoting community empowerment through educational activities and environmentally sustainable tourism.

This study examines disaster management strategies in earthquake-prone countries, with a comparative focus on Japan and the Philippines as case studies for lessons applicable to public administration systems worldwide. Using a qualitative comparative analysis approach, the research evaluates institutional frameworks, policy instruments, community engagement mechanisms, and intergovernmental coordination systems deployed in both countries. Japan’s highly centralized yet locally adaptive Disaster Management Basic Act framework is contrasted with the Philippines’ decentralized National Disaster Risk Reduction and Management (NDRRM) system. Findings reveal that effective disaster management hinges on five critical pillars: strong legal frameworks, inter-agency coordination, investment in early warning systems, community resilience programs, and post-disaster recovery governance. The study further identifies that public trust, administrative capacity, and fiscal decentralization significantly influence disaster response outcomes. Lessons drawn from both countries offer practical recommendations for developing nations seeking to strengthen their disaster governance architectures. This research contributes to the growing body of knowledge on comparative public administration and disaster risk reduction, underscoring the imperative of integrated, adaptive, and community-centered governance frameworks in seismically active regions.

This research examines digital charity practices within Christian media communication on YouTube, focusing on two distinct donation formats: marapthon live stream donations (e.g., 24‑hour fundraising events) and sermon‑based donations (offerings collected during or after online worship services). Despite the rapid growth of faith‑based online giving, a critical problem remains: the absence of an integrated system that aligns these two donation models with Christian values of transparency, accountability, and community stewardship. Existing platforms often treat live marapthon and sermon donations separately, leading to fragmented donor experiences and inefficient fund utilization. Therefore, this study aims to develop a conceptual framework for an integrated digital charity system by comparatively analyzing media communication strategies in both donation contexts. The proposed method is a netnographic comparative analysis, involving systematic observation of YouTube comments, chat logs, and video descriptions from 10 Christian channels (5 marapthon‑focused, 5 sermon‑focused) over six months, supplemented by semi‑structured interviews with content creators and donors. The main findings reveal that marapthon donations emphasize urgency and real‑time social proof, while sermon donations rely on theological framing and pastoral trust. The synthesis proposes a hybrid system architecture incorporating real‑time donation tracking, automated acknowledgment, and weekly theological reflection modules. In conclusion, integrating both models into a single development framework enhances donor engagement and aligns digital charity with Christian communication ethics, offering practical guidelines for church‑based YouTubers and platform developers.