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This study aims to analyze how human development governance is implemented by the Government of South Sulawesi Province in reducing regional disparities, identify institutional factors influencing implementation quality, and examine its implications for human development outcomes. The study employs a qualitative approach using a case study design. Data were collected through interviews with key informants from the Regional Development Planning Agency (Bappeda) and relevant Regional Apparatus Organizations, analysis of regional planning documents such as RPJMD and RKPD, and statistical data from the Central Bureau of Statistics related to the Human Development Index (HDI), poverty, and employment. Data were analyzed thematically using triangulation to ensure validity of findings. The results show that the Human Development Index has been positioned as a key performance indicator in regional development planning documents and prioritized in education and health sector policies. Although overall HDI achievement in South Sulawesi is categorized as high, significant disparities remain across regencies and municipalities, particularly in education and standard of living. A poverty rate of 8.06 percent and the increasing trend of the Gini Ratio indicate that distribution of welfare has not been fully equitable. Furthermore, the dominance of the informal sector in the employment structure reflects challenges related to job quality, income stability, and limited social protection coverage. These findings suggest that the main challenge of human development in South Sulawesi lies not only in improving aggregate indicators but also in strengthening bureaucratic capacity, cross-sectoral coordination, and policy implementation consistency to ensure more inclusive and equitable development across regions.

Bureaucratic reform in Indonesia has encouraged the strengthening of civil service management oriented toward performance through the policy of appointing Government Employees with Work Agreements (PPPK) in order to improve the effectiveness of public organizations. This study aims to analyze the performance of PPPK in supporting the implementation of tasks at the Environmental Agency of Jambi City by using Mangkunegara’s performance theory as the analytical framework. This research employs a qualitative approach with a descriptive method. Data were collected through in-depth interviews and documentation, and then analyzed using data reduction, data display, and conclusion drawing techniques. The results show that the performance of PPPK is generally in the good category and contributes positively to organizational effectiveness. This is reflected in the aspects of work quality, work quantity, and responsibility as explained in Mangkunegara’s performance theory. The alignment between job placement and competence, increased work motivation due to employment status certainty, and organizational support through training and coaching are the main factors supporting performance optimization. However, there are still constraints, particularly the suboptimal understanding of main duties and functions, thus requiring strengthened organizational communication and continuous capacity development. This study confirms that competency-based management and systematic development of PPPK play an important role in improving the effectiveness of task implementation in the public sector.

Public service is a key indicator in evaluating local government performance; however, various service issues continue to generate increasing public complaints. This study aims to analyze trends in public complaints, identify the most frequently reported institutions, and examine the types of maladministration and the status of complaint resolution through the Ombudsman of the Republic of Indonesia in South Sulawesi Province during 2023–2025. This study offers novelty by providing an integrated analysis of complaint trends, distribution of reported institutions, types of maladministration, and resolution outcomes based on Ombudsman data at the regional level. This research employs a qualitative approach using document analysis, with public complaint reports as the unit of analysis. Data were analyzed through data reduction, data display, and conclusion drawing. The results indicate that local governments are the most frequently reported institutions (55.1%), with dominant maladministration types including procedural deviations and prolonged delays. Several complaints were proven to involve maladministration and were resolved through the Ombudsman’s supervisory mechanism. These findings highlight that public complaints serve as an important instrument in enhancing accountability and transparency in public service delivery. Therefore, local governments need to strengthen service standards, improve the capacity of public officials, and develop more effective complaint management systems.

Micro, Small, and Medium Enterprises (MSMEs) constitute the backbone of Indonesia's national economy, contributing approximately 61% of GDP and absorbing 97% of the total workforce; however, the majority of MSME actors, particularly in eastern Indonesia, continue to face structural barriers in digital technology adoption and capital access that constrain optimal business performance. This study aims to analyze the partial and simultaneous effects of marketing digitalization and business capital on MSME performance in Ambon City. A quantitative approach with associative-causal design was employed, involving 30 respondents selected through purposive sampling from active MSME operators in Ambon City. Data were collected via a five-point Likert scale questionnaire and analyzed using multiple linear regression with IBM SPSS version 26, preceded by validity, reliability, and classical assumption tests. Results demonstrate that marketing digitalization exerts a positive and significant partial effect on MSME performance (t = 8.060; sig. = 0.000; β = 1.061), establishing it as the most dominant predictor in the model. Conversely, business capital shows no significant partial effect (t = 0.746; sig. = 0.462), attributable to the homogeneity of capital access among MSME actors in archipelagic regions. Simultaneously, both variables significantly influence MSME performance (F = 287.070; sig. = 0.000), with an exceptionally high R Square of 0.955, indicating that 95.5% of performance variance is collectively explained by the two predictors. These findings underscore the critical role of digital marketing capabilities over financial resources alone in archipelagic contexts. It is recommended that the Ambon City Government integrate digital marketing literacy training programs synergistically with inclusive financing schemes to comprehensively strengthen MSME competitiveness across the Maluku archipelago.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

The Low Voltage Main Distribution Panel (LVMDP) is a critical component in industrial power distribution systems, functioning to regulate, control, and distribute electrical energy to various production equipment. During operation, LVMDP panels often operate under high electrical loads, which may lead to temperature increases in their components. Undetected temperature rise can result in performance degradation, equipment failure, and even fire hazards. Therefore, an effective monitoring method is required to detect the condition of electrical components at an early stage. This study aims to analyze the temperature difference (ΔT) of LVMDP components using the Infrared Thermography method as part of predictive maintenance. The research employs a quantitative descriptive approach with data collected through direct observation from July 1 to July 31 at PT. Dongjin Indonesia. The data consist of hotspot and ambient temperatures measured from several panel components, which are then analyzed to calculate the temperature difference (ΔT) as an indicator of component operating conditions. The results indicate that the highest temperature difference is 26.5 °C in the capacitor bank, while the lowest is 4 °C in other components. All ΔT values are below the threshold limit of 50 °C, indicating that the LVMDP components are in safe operating conditions and do not require corrective actions. Thus, Infrared Thermography is proven to be an effective method for early detection of component conditions and can enhance the reliability and safety of industrial power distribution systems.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

This study aims to analyze the employee turnover rate experienced by CV Premium Indonesia employees through the influence of workload and job stress. As a company engaged in the retail and distribution of mobile phone accessories from leading brands, the desire to leave the company often arises due to high workload and feelings of work stress because of the many demands within the company. The methodology used in this study is quantitative, employing SEM model data analysis with the help of SmartPLS software. The sampling technique used is saturated sampling, with a total sample of 127 respondents. The results of this study indicate that high levels of workload can increase employee turnover rates. Meanwhile, high levels of job stress experienced by employees can also increase employee turnover rates. These findings suggest that CV Premium Indonesia should address the issues of workload and job stress in order to retain employees. Effective strategies such as work-life balance, stress management programs, and workload adjustments could help reduce employee turnover and improve overall organizational performance.

This study aims to evaluate the growth performance of KUB chickens fed with a combination of fermented agricultural waste, specifically Maggot BSF (Hermetia illucens) and Azolla microphylla. A total of 96 KUB chickens aged 30 days were used in a Completely Randomized Design (CRD) with four treatments and four replications. The treatments consisted of a basal diet substituted with the fermented waste combination at levels of 0% (P0), 5% (P1), 10% (P2), and 15% (P3). Parameters observed included feed consumption, body weight gain (BWG), and Feed Conversion Ratio (FCR). The results showed that increasing the level of waste substitution significantly affected feed consumption due to the bulky nature of the fiber, yet body weight gain remained stable across all treatments. The FCR values ranging from 3.21 to 3.25 indicated that the high-quality protein from Maggot BSF effectively compensated for the reduced feed intake. It is concluded that the substitution of fermented agricultural waste up to 15% (P3) is an optimal and efficient formulation to maintain the growth performance of KUB chickens.

This study aims to analyze the effect of green accounting and carbon emission disclosure on firm value in palm oil sector issuers listed on the Indonesia Stock Exchange (IDX) and participating in the PROPER program during the 2020–2024 period. Green accounting is proxied using the PROPER rating, which reflects a company's environmental management performance, while carbon emission disclosure is measured based on the level of carbon emission disclosure in the company's annual report or sustainability report. This study uses a quantitative approach with panel data regression analysis. The sampling technique used was purposive sampling, with the criteria being palm oil companies listed on the IDX, participating in PROPER, and consistently publishing annual reports throughout the study period. The data used are secondary data obtained from financial reports, sustainability reports, and official publications related to PROPER. The results are expected to show that the implementation of green accounting has a positive effect on firm value, as it reflects the company's commitment to sustainability and increases investor confidence. Furthermore, carbon emission disclosure is expected to have a positive effect on firm value, depending on market perception and the quality of environmental information disclosure. This research is expected to contribute to the development of environmental accounting literature and serve as a reference for regulators, investors, and company management in improving transparency and environmental performance to create sustainable corporate value.

Public service quality has emerged as a crucial metric for assessing how well the government functions and the efficiency of its administration. Recently, rising expectations from the public have led gov-ernments to offer services that are open, responsible, effective, and attuned to citizens' needs. Alongside administrative changes and the evolution of governance models focused on efficiency and citizen satisfaction, the appetite for high-quality public services has surged. Nonetheless, numerous public entities continue to struggle with providing reliable and top-notch services due to a lack of defined service norms and systems for measuring performance. The purpose of this research is to investigate ways to enhance public service quality by establishing robust service standards and quantifiable performance metrics. The study employs a qualitative descriptive methodology, drawing on a review of relevant lit-erature from the past seven years. Multiple academic sources were analyzed to uncover essential ideas, frameworks, and empirical evidence concerning public service management, the enhancement of service quality, and the assessment of performance in governmental organizations. The results show that adopting well-defined service standards, along with measurable performance metrics, can greatly en-hance service effectiveness, accountability, and overall public satisfaction. Additionally, merging prin-ciples of service excellence with quantifiable indicators promotes transparency, boosts organizational performance, and fosters ongoing improvements in service delivery. The research concludes that to enhance public service quality, a structured framework for service standards, ongoing performance assessments, and a firm organizational dedication to innovation and service enhancement are necessary.

This study analyzes the governance performance of information systems at the Career Center of Universitas Muhammadiyah Jambi using the COBIT 2019 framework. The primary objective is to evaluate the maturity level of IT governance and provide recommendations to enhance the efficiency and effectiveness of information systems in supporting the institution’s strategic objectives. Data were collected through interviews, observations, and questionnaires involving system users and decision-makers within the Career Center. The findings indicate that several areas require greater attention, particularly Managed IT Changes, Managed Risk, and Managed Operations. These domains are considered critical in improving the management and reliability of the existing information systems. The study also identifies several challenges affecting system performance, including limited system integration, insufficient human resources, and the use of outdated technology. Based on these findings, the research recommends strengthening the organizational structure, improving the competence of human resources, and optimizing IT processes in accordance with COBIT 2019 standards. Implementing these improvements is expected to increase IT governance maturity, enhance service quality for students and alumni, and better support the university’s strategic development goals.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

The synchronization of physical and juridical land data is a fundamental prerequisite for ensuring legal certainty in every land rights transfer process. Discrepancies between physical field conditions and juridical documents frequently trigger land disputes that impede the performance of Notary/PPAT offices. This community service article reports on a Field Practice (PPL) activity conducted at the Office of Notary/PPAT in Jember Regency during January–February 2026. The primary objective was to provide technical assistance in accelerating physical-juridical data synchronization to guarantee the validity of issued deeds. The method employed was participatory observation, in which students were directly engaged in the partner's workflow. Assistance activities covered document inspection, file digitization, coordination with BPN and tax authorities, and factual field verification. Results indicate that PPL student assistance measurably accelerated the office's administrative workflow, enhanced data validation accuracy, and confirmed conformity between the physical condition of rice-field land and the certificate documents.

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.

This study aims to analyze the influence of internal company factors, including company size, networking capital, operating efficiency, liquidity, and leverage, on financial performance, proxied by Return on Assets. The research population includes 40 food & beverage subsector companies listed on the Indonesia Stock Exchange during the 2019–2024 period. Using purposive sampling, 17 sample companies were selected, yielding a total of 102 data observations. This study adopts a quantitative approach, using secondary data obtained from the company's annual financial statements. Data analysis was performed using multiple linear regression to identify partial and simultaneous influences between variables. Empirical findings show that not all internal factors exert a uniform influence on financial performance, as some variables have been shown to have a significant influence, while others do not show a statistically significant relationship. These results have important implications for managers and investors in formulating internal management strategies to drive sustainable profitability

Good governance has become a crucial framework for improving public sector effectiveness, accountability, and responsiveness to citizens’ needs. In Indonesia, decentralization policies since 1999 have devolved significant authority and resources to local governments, creating both opportunities and challenges in implementing good governance principles. This study examines administrative reform efforts in Indonesian local governments, focusing on progress, challenges, and key success factors. Using a multiple case study design, the research analyzed reform initiatives in six district/city governments with diverse contexts. Data were collected through document analysis, semi-structured interviews with government officials, civil society representatives, and citizens, as well as direct observation of administrative processes conducted between March and November 2024. The study assessed six core principles of good governance: participation, transparency, accountability, effectiveness and efficiency, equity and inclusiveness, and rule of law. The findings reveal variations in implementation across regions, with high-performing governments demonstrating strong leadership commitment, systematic planning, active citizen engagement, and effective monitoring mechanisms. However, challenges persist, including limited capacity, bureaucratic resistance, weak accountability enforcement, and low public participation. Key success factors include visionary leadership, capacity development, strategic partnerships, and institutional strengthening. Policy recommendations emphasize enhancing local capacity, accountability systems, citizen participation, and performance evaluation. Good governance has become a crucial framework for improving public sector effectiveness, accountability, and responsiveness to citizens’ needs. In Indonesia, decentralization policies since 1999 have devolved significant authority and resources to local governments, creating both opportunities and challenges in implementing good governance principles. This study examines administrative reform efforts in Indonesian local governments, focusing on progress, challenges, and key success factors. Using a multiple case study design, the research analyzed reform initiatives in six district/city governments with diverse contexts. Data were collected through document analysis, semi-structured interviews with government officials, civil society representatives, and citizens, as well as direct observation of administrative processes conducted between March and November 2024. The study assessed six core principles of good governance: participation, transparency, accountability, effectiveness and efficiency, equity and inclusiveness, and rule of law. The findings reveal variations in implementation across regions, with high-performing governments demonstrating strong leadership commitment, systematic planning, active citizen engagement, and effective monitoring mechanisms. However, challenges persist, including limited capacity, bureaucratic resistance, weak accountability enforcement, and low public participation. Key success factors include visionary leadership, capacity development, strategic partnerships, and institutional strengthening. Policy recommendations emphasize enhancing local capacity, accountability systems, citizen participation, and performance evaluation.

Leadership constitutes a critical determinant of organizational efficiency, yet the relationship between leadership styles and administrative effectiveness remains incompletely understood, particularly across different institutional contexts. This comparative study examines how transformational, transactional, and laissez-faire leadership styles influence administrative efficiency in public and private sector institutions in Indonesia. Using a mixed-methods approach, quantitative data were collected from 428 employees across 28 public institutions and 24 private organizations through validated survey instruments; qualitative data were gathered through 36 semi-structured interviews with organizational leaders and managers. Results reveal significant positive relationships between transformational leadership and administrative efficiency in both sectors (β = 0.54, p < 0.001 for public; β = 0.48, p < 0.001 for private), with transactional leadership showing moderate positive effects (β = 0.32 public; β = 0.38 private) and laissez-faire leadership demonstrating negative relationships (β = −0.28 public; β = −0.24 private). Leadership styles collectively explain 52% of efficiency variance. Mediation analysis reveals that organizational culture (28%), employee engagement (35%), and trust in leadership (22%) partially mediate transformational leadership's effects, together accounting for 62% of total indirect effects. Sector differences emerged qualitatively: transformational leadership leverages public service motivation in public institutions, while transactional leadership's performance accountability resonates more strongly in private organizations. The study contributes theoretical understanding of leadership effectiveness across institutional contexts and provides practical guidance for developing contextually appropriate leadership development programs.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

This community service activity aims to analyze the effectiveness of Time and Motion Study in the production process at PT. Adis Dimension Footwear and provide recommendations for improving work methods. This study employed a qualitative descriptive approach using observation, interviews, and documentation. The results show that implementing Time and Motion Study can enhance work time efficiency, optimize operator movements, and increase productivity by up to 15%. Reducing unnecessary movements, optimizing workflow, and training operators significantly improve production performance. The success of this method depends heavily on socialization, training, and management support. These findings confirm that Time and Motion Study serves not only as a tool for measuring time and movements but also as a strategic instrument for improving efficiency, reducing waste, and increasing overall productivity.