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Cattle farming plays a strategic role in rural economic development by generating employment, supplementary household income, and contributing to food and nutritional security. However, in Indonesia, most smallholder cattle enterprises remain traditional and small-scale, leading farmers to treat livestock farming primarily as a secondary livelihood. While previous studies have largely focused on technical adoption and production performance, limited attention has been given to farmers’ psychological interest as a foundation for sustaining livestock enterprises. Therefore, this study aims to analyze the level of community interest in cattle farming and examine the influence of farmers’ characteristics (age, education level, herd size, number of family dependents, farming experience, and primary income) on such interest. A quantitative survey was conducted involving 34 cattle farmers in Taji Village, Jabung District, Malang Regency, selected using total sampling. Data were collected through structured questionnaires and analyzed using multiple linear regression. The findings reveal that community interest in cattle farming is generally high, as reflected in emotional (3.93), cognitive (3.81), and conative (3.74) dimensions measured on a four-point Likert scale. Regression results indicate that age has a significant positive effect (p = 0.022), whereas farming experience has a significant negative effect (p = 0.025) on interest levels. Other variables show no statistically significant influence. These findings highlight that psychological engagement in cattle farming is shaped more by demographic and experiential factors than by formal education or income level. The study contributes to the literature by positioning interest as a psychological determinant of livestock sustainability. Policy implications emphasize the urgency of risk-mitigation extension programs to address farmer burnout, incentivized intergenerational knowledge transfer, and cooperative-based institutional support to ensure smallholder livestock sustainability.

This study examines the financial performance of PT Unilever Indonesia Tbk during the 2023–2025 period by evaluating key financial indicators, namely the Current Ratio (CR), Debt to Equity Ratio (DER), Return on Assets (ROA), and Return on Equity (ROE). The study aims to assess the company's financial condition and analyze the impact of its business transformation strategy on financial performance. A descriptive quantitative approach was employed using secondary data obtained from the company's published annual financial reports. Data analysis focused on comparing financial ratio trends over the three-year period to evaluate liquidity, solvency, and profitability performance. The findings indicate that the company's financial performance experienced fluctuations during the business transformation process. Liquidity and solvency gradually improved toward the end of the observation period, reflecting stronger short-term financial capability and a healthier capital structure. Profitability also demonstrated increased efficiency in utilizing company assets, although changes in equity returns indicated adjustments in capital management during the transformation process. Overall, the implementation of the company's transformation strategy contributed positively to strengthening financial performance and improving resilience in responding to changing business conditions and market competition. This study provides useful insights for management, investors, and other stakeholders in evaluating the effectiveness of corporate transformation strategies through financial ratio analysis and highlights the importance of maintaining financial stability to support sustainable business growth.

This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.      

This study aims to analyze the management of young preacher (dai cilik) training in developing students’ preaching skills at SD IT Al-Washliyah Kelambir Lima Medan using a management function approach (*planning, organizing, actuating, and controlling*). This study employed a descriptive qualitative method, with data collected through in-depth interviews, participant observation, and documentation. The research informants consisted of the principal and teachers responsible for the young preacher training program. The collected data were analyzed systematically to describe the implementation of management functions in the young preacher training activities and their contribution to the development of students’ preaching skills. The results showed that planning was carried out through the development of a practice-based preaching curriculum, organizing was implemented through task allocation and regular scheduling, implementation focused on *public speaking* practice, sermon material preparation, and moral development, while evaluation was conducted periodically through student performance assessments. The implementation of this systematic training management contributed to the improvement of students’ preaching skills in cognitive, affective, and psychomotor aspects. These findings indicate that management functions applied in a structured manner play a strategic role in supporting the development of students’ preaching competencies at the elementary school level. Furthermore, continuous training provides opportunities for students to enhance their self-confidence, communication skills, and readiness to effectively deliver Islamic preaching messages

This study aims to assess the financial performance of 11 non-financial companies that conducted acquisitions in 2025 and are listed on the Indonesia Stock Exchange (IDX). Using a quantitative descriptive-comparative approach with a case study design, six financial ratios were analyzed: Current Ratio (CR), Debt to Asset Ratio (DAR), Debt to Equity Ratio (DER), Total Asset Turnover (TATO), Return on Assets (ROA), and Return on Equity (ROE). Data were obtained from consolidated financial statements as of December 31, 2024 (pre-acquisition) and December 31, 2025 (post-acquisition). The results show that the impact of acquisitions varies across companies. No consistent or significant differences were found in the CR, DAR, DER, ROA, or ROE ratios between the two periods. Meanwhile, the TATO ratio tended to decrease after the acquisition, indicating that the newly consolidated assets have not yet operated optimally. These findings confirm that the short-term financial impact of an acquisition is heavily influenced by the transaction’s funding structure, the size of the acquired entity, and the industry sector. This study contributes to the financial accounting literature on corporate acquisition strategies in the Indonesian capital market.

Substandard housing remains a multidimensional urban problem because it intersects with structural safety, indoor environmental quality, sanitation, clean water, poverty, and the effectiveness of policy implementation. This study evaluates the implementation of the substandard housing improvement program (RTLH) in Lubuk Linggau City by integrating a technical audit of housing quality with an implementation analysis based on the variables of communication, resources, implementer disposition, and bureaucratic structure. A descriptive-evaluative mixed approach was applied through field observation, technical scoring of ten rehabilitated houses, document review, and structured interviews with regulators, field facilitators, and beneficiary households. The results show that the cumulative technical conformity index reached 3.325 out of 4.00, equivalent to 83%, and was classified as adequate housing. Structural resilience achieved the highest score at 88%, followed by space adequacy at 85%, natural lighting and ventilation at 80%, and sanitation and clean water at 75%. Five houses were classified as adequate housing, while five were classified as fairly adequate/light substandard housing. The implementation analysis indicates that clear practical communication and facilitator assistance strengthened structural quality, but limited financial resources, weak environmental-health prioritization, and administratively oriented supervision reduced sanitation and ventilation performance. The study recommends standardized septic systems, stronger post-construction technical verification, healthy-housing education, realistic unit budgets, and quality-oriented monitoring.

Teacher performance evaluation is a crucial factor in improving educational quality, but many schools still conduct it manually and subjectively, resulting in less transparent, time‑consuming, and error‑prone outcomes. This study aims to design and develop a web‑based decision support system using the Simple Additive Weighting (SAW) method for selecting the best teacher at MTs Mambaul Ulum. The system was developed using the Waterfall model with the Laravel 12 framework and MySQL database, incorporating two evaluator roles: the principal (assessing 5 criteria: teaching hours, responsibility, mastery of material, personality, and attendance) and the vice principal (assessing 4 criteria excluding personality). Data were collected through observation, interviews, and document review. System testing employed Black Box Testing and accuracy validation by comparing system outputs with manual Excel calculations. The results show that the SAW method effectively ranks teachers objectively, with Kusyanti achieving the highest score of 0.9450. The system attained 100% accuracy, as no difference was found between manual and system calculations. Moreover, the system reduced evaluation time from days to seconds and provides well‑documented results, thus supporting more transparent and accountable decision‑making. Therefore, this system proves to be reliable, objective, and efficient in assisting school administrators in evaluating teacher performance in a structured and fair manner.

Cracker pr machines are widely used in small and medium-scale food industries. However, limitations in frame design and motor speed control often result in suboptimal machine performance. This study aims to perform a redesign of the upper conveyor frame, analyze the structural strength of the frame, and evaluate the effect of using a Variable Speed Drive (VSD) inverter on improving the production capacity of a cracker printing machine.The research method includes frame redesign, load calculation, and numerical analysis using the Finite Element Method (FEM) with SolidWorks Simulation. The applied loads consist of machine component weights and operational loads. In addition, performance testing was conducted by varying the motor rotational speed controlled by the VSD inverter to determine its effect on production output.The results show that the redesigned frame has a maximum displacement of 0.18 mm, a maximum stress of 8.9 MPa, and a minimum safety factor of 31, indicating that the structure is safe and operates within the elastic region. The implementation of the VSD inverter successfully increased the machine’s production capacity from the initial condition up to 78.5 kg/h. Based on these results, it can be concluded that the frame redesign and the application of a VSD inverter are effective in improving the performance and reliability of the cracker printing machine.

This research investigates the integration of Sound Governance principles within the sustainable tourism policy cycle as a mechanism to mitigate institutional fragmentation and evaluation inefficiencies in developing economies. Adopting a sequential explanatory mixed-methods design, the study establishes a multidimensional evaluation framework congruent with the Sustainable Development Goals (SDGs). Policy performance is rigorously analyzed across five strategic domains: Economy, Social-Welfare, Culture-Education, Environment, and Governance. The quantitative methodology employs 17 adapted SDG indicators to assess policy efficacy, while the subsequent qualitative phase utilizes semi-structured interviews and stakeholder mapping to deconstruct complex power dynamics within multi-actor co-management structures. The empirical findings demonstrate that Sound Governance—predicated on transparency and accountability—acts as a fundamental catalyst for policy effectiveness. It significantly enhances destination sustainability through a structured input-process-output-outcome-impact pathway. Evidence from the case study of Penglipuran Village, Bali, validates these results, showing that while indigenous institutional legitimacy bolsters social responsiveness, it remains susceptible to economic dependencies driven by overtourism. Ultimately, this study asserts that embedding SDGs into the policy evaluation cycle elevates assessments from perfunctory administrative exercises to strategic instruments essential for ecosystem preservation and long-term demand stability. These insights establish Sound Governance as a strategic intangible asset, providing significant theoretical contributions to development administration and offering pragmatic frameworks for policy-makers managing sustainable destinations in competitive global markets.

This study aims to examine the implementation of policies regarding the use of the e-Kelurahan feature in the Sangu Palu application in Taipa Village. The study employs George C. Edward III’s policy implementation theory, which consists of four dimensions: communication, resources, disposition, and bureaucratic structure. A qualitative descriptive approach was used, with data collected through observation, in-depth interviews, documentation, and literature review involving nine informants. The findings indicate that the implementation of the e-Kelurahan policy has not yet run optimally. The main obstacle lies in the communication aspect, particularly the lack of direct public socialization regarding the use of the application, resulting in low public understanding and limited utilization of digital services. In addition, training for village officials has not been conducted continuously, as it was only provided during the initial implementation stage. Therefore, periodic retraining is needed to improve technical competence and evaluate service performance. Although the aspects of disposition and bureaucratic structure have generally functioned well, they have not been sufficient to optimize digital service utilization. These findings are consistent with the Electronic-Based Government System (SPBE) evaluation results, which place Palu City in the “sufficient” category, indicating the need for further improvement in digital service implementation.

The rapid development of information technology drives various business sectors, including the beauty and health industry, to adopt digital systems in their operational management. A Beauty Clinic, a medium-scale aesthetic clinic, faces significant operational challenges in managing human resources, finance, and digital marketing in an integrated manner. This study aims to analyze the existing manual operational system of A Beauty Clinic using the PIECES framework (Performance, Information, Economy, Control, Efficiency, Service), design and configure an Odoo ERP Community Edition implementation integrating the HR, Invoicing/Point of Sale, and Social Marketing modules, and evaluate the operational efficiency improvements achieved. The research employed a qualitative descriptive approach with a case study method and Rapid Application Development (RAD) as the system development methodology. Data was collected through direct observation and semi-structured interviews. The results show that the manual system experienced critical problems across all six PIECES dimensions. Post-implementation evaluation indicates an estimated 80% reduction in the secretary's administrative workload (from 20–25 hours to 3–5 hours per month), over 90% reduction in payroll error frequency, and an 87% reduction in transaction processing time per cashier transaction. The implementation of role-based access control and complete audit trails fundamentally addressed the security vulnerabilities identified in the as-is analysis. This study demonstrates that Odoo Community Edition is an effective, comprehensive, and cost-efficient ERP solution for small and medium-scale beauty clinics seeking to transform from fragmented manual systems to integrated, data-driven information systems.

Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

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.

This study aims to analyze the financial management practices of the Es Degan Bu Ulfa MSME and their compliance with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM). The study used a qualitative approach with a case study method. Data were obtained through interviews and observations, then analyzed descriptively and qualitatively.   This study aims to analyze the financial management practices of the Es Degan Bu Ulfa MSME and their compliance with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM). The study used a qualitative approach with a case study method. Data were obtained through interviews and observations, then analyzed descriptively and qualitatively. The results indicate that financial management is still carried out simply and does not yet implement written records. Business income and expenses still rely on memory, even though business owners perform daily profit and loss calculations and separate personal and business finances. The main obstacles faced include limited human resources and a low understanding of financial record keeping and the SAK EMKM. Based on the analysis, financial management practices are not fully compliant with SAK EMKM standards. Therefore, the implementation of simple financial record keeping is necessary to structure business financial information and support business decision-making. Proper financial management can help improve business performance, monitor cash flow, evaluate profitability, and enhance accountability. Furthermore, the application of SAK EMKM is expected to facilitate access to financing and strengthen the sustainability and competitiveness of MSMEs in an increasingly competitive business environment.

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.

The advancement of information technology has driven higher education institutions to manage their digital presence professionally, particularly through quality website management as a representation of institutional identity. Preliminary observations of the Universitas Graha Karya (UGK) website (ugk.ac.id) identified several issues, including inconsistent page layouts, inactive hyperlinks, and irregularly updated content. This study aims to analyze the quality of the UGK website based on three WebQual 4.0 dimensions—usability, information quality, and service interaction quality—and to determine improvement priorities using Importance Performance Analysis (IPA). A descriptive quantitative approach was employed, with data collected through questionnaires distributed to 308 active students selected via purposive sampling using the Slovin formula at a 5% margin of error. Data analysis included validity testing, reliability testing, WebQual 4.0 analysis, gap analysis, and IPA Cartesian diagram mapping. Results indicate negative grand mean gap values across all dimensions, with service interaction quality recording the largest gap (-0.16), followed by information quality and usability (both -0.01). IPA mapping identified five indicators in Quadrant I as the main improvement priorities: ease of menu navigation (U2), relevance of academic information (IQ2), clarity of information language (IQ4), website access security (SI1), and ease of contacting administrators (SI4). The study concludes that UGK's website quality has not fully met user expectations and requires structured, user-oriented improvements.

Accountability in preparing financial reports is a crucial instrument for the sustainability of business entities, because inaccurate financial data management can hinder decision-making and harm business performance in the future. For MSME actors in the PKK Tanggamus community, strengthening financial reporting competence is an urgent need so they can map expenditure structures, record income, calculate profit, and evaluate business development periodically. This community service activity aims to improve participants’ financial discipline, particularly in separating personal assets, business capital, and gross profit, while introducing accessible office technology. The training focused on optimizing LibreOffice Calc as an alternative to Microsoft Excel with similar functions for creating transaction tables, cost recapitulations, and simple financial reports. The activity method included material presentation, software demonstrations, report preparation practice, and interactive discussions based on participants’ business needs. Training results showed high enthusiasm, improved understanding, and readiness to use LibreOffice Calc as a more organized, transparent, and sustainable financial recording tool. Thus, this activity provides practical contributions to building an accountable financial administration culture for community-based MSMEs.

This study aims to implement Business Intelligence using Microsoft Power BI to support network disturbance monitoring based on trend analysis. The main problem addressed in this research is that network disturbance data has only been used as documentation, resulting in suboptimal monitoring and data analysis processes. The data used in this study consists of historical network disturbance data, including information on disturbance location, type of disturbance, occurrence time, and resolution duration. The research methodology includes data collection, data preprocessing, implementation of a Business Intelligence dashboard, and trend analysis using interactive visualizations in Microsoft Power BI. The developed dashboard consists of Key Performance Indicators (KPI), interactive filters, line charts, pie charts, bar charts, and table visuals to support a more effective and structured network monitoring process. The results show that the implementation of the Business Intelligence dashboard is capable of supporting real-time, interactive, and user-friendly monitoring of network disturbances. The trend analysis results indicate that the number of network disturbances fluctuated each month, with the highest number occurring in August and the lowest in November. In addition, the dominant types of disturbances were identified as Supporting Facilities and Electrical Problems. Based on functional testing and user testing results, all dashboard features performed successfully and were considered effective in supporting monitoring activities and data-driven decision making.