Publication Search

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

This study aims to analyze the innovation of the use of e-SDM Applications in improving the quality of digital employee data management at the Surabaya City Human Resources Development Agency (BKPSDM). This study uses a qualitative approach with a case study method through data collection techniques such as in-depth interviews, direct observation, and supporting documentation. The research analysis refers to the success factor model of e-Government innovation according to Maulidhia J.P., which includes aspects of leadership, stakeholders, resources, technology and information, processes, goals and values, and laws and regulations. The results of the study indicate that the implementation of e-SDM Applications can improve work efficiency, data accuracy, transparency, and ease of access to employee information through an integrated digital system. This success is supported by leadership commitment, collaboration between stakeholders, and the availability of adequate resources. However, this study also found several challenges, including technical system and network constraints, the need to increase human resource capacity, and the need for continuous regulatory and SOP updates. Overall, e-SDM innovations have made a positive contribution to improving the quality of employee data management in government environments.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

Digital transformation in public services represents a strategic shift in integrating information technology into governmental administrative systems to enhance service efficiency, transparency, and accountability. One prominent innovation in this effort is the adoption of electronic land certificates initiated by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) as part of land service modernization. This policy is formally regulated under Ministerial Regulation No. 3 of 2023 concerning electronic documents in land registration. This study employs a descriptive qualitative approach to examine the implementation process, identify enabling and constraining factors, and assess the impact of electronic land certificates on the quality of land services. Data were obtained through interviews, field observations, and document analysis at the Tulungagung Regency Land Office and analyzed using the interactive model proposed by Miles and Huberman. The findings indicate that electronic land certificates contribute significantly to improving service efficiency, administrative speed, and data security. Nonetheless, several challenges persist, including inadequate network infrastructure in rural areas, limited public digital literacy, and insufficient information technology personnel. Despite these constraints, the initiative has been positively received and reflects the local government’s commitment to advancing digital governance and good governance principles. The effectiveness of this transformation largely depends on institutional readiness, technological support, and community engagement.

The development of information technology requires innovative learning media to improve the quality of instruction in elementary schools. One technology-based learning medium that can be utilized is Kahoot as an interactive quiz. This study aims to describe the implementation of Kahoot in science learning on the topic of plant structure and functions and its effects on students’ conceptual understanding, learning activeness, and motivation in fourth-grade elementary school students. This research employed a descriptive approach with data collection techniques including observation, learning outcome tests, and documentation. The results indicate that the use of Kahoot creates a more interactive and engaging learning environment, increases students’ active participation, and enhances their understanding of plant structure and function concepts. In addition, Kahoot is effective as an evaluation tool because it provides immediate feedback and presents learning outcomes quickly and systematically. However, the use of Kahoot also has limitations, particularly its dependence on the availability of digital devices and stable internet access. Therefore, Kahoot can be used as an innovative alternative learning medium, provided that school conditions are considered and it is combined with other learning methods to optimize science learning.

Human resource quality serves as a crucial pillar for national development, as it significantly influences a country’s progress and competitiveness. Therefore, enhancing human resource capacity has become a strategic priority, particularly at the regional level where local governments play a vital role. The Government of Bojonegoro Regency has introduced several initiatives to support this objective, including the Ten Scholars per Village Scholarship Program, the Scientist Scholarship, and Final Project Assistance. Despite these efforts, several implementation challenges remain, such as cases of manipulated eligibility requirements among applicants, the absence of a digitized system for registration and document submission, and limited communication channels between program administrators and scholarship recipients. This research seeks to examine, explain, and evaluate the role of local government, especially the Education Office, in executing scholarship programs based on the theoretical framework of government roles as regulator, dynamizer, and facilitator. The study employs a descriptive qualitative method, utilizing observation, interviews, and documentation for data collection, followed by data analysis using the Miles and Huberman model. The findings reveal that although the local government has begun implementing scholarship programs, the execution is not fully effective. Supervision during participant selection focuses mainly on administrative completeness rather than document authenticity. Furthermore, direct outreach activities are limited to certain regions, coordination with village authorities remains minimal, and digital transformation has yet to be realized due to unsuccessful collaboration with the Communication and Information Agency despite several coordination meetings.

This study aims to describe in detail the implementation of the SMART-TENDIK PAUD innovation as a model for managing educators and educational personnel based on reflection, collaboration, and the use of simple technology, as well as to analyze its impact on improving teacher professionalism in early childhood education settings. This study employs a qualitative approach using a literature review method to explore relevant theories and previous research findings. Data collection techniques include document analysis and systematic online searches from various credible academic sources. Data analysis is conducted through the stages of data reduction, data display, and conclusion drawing to ensure comprehensive and accurate interpretation. The results indicate that the implementation of SMART-TENDIK PAUD enhances teachers’ reflective awareness, strengthens collaboration through active participation in learning communities, and improves the effectiveness of educational personnel management through the practical use of simple and accessible technology. Furthermore, this innovation contributes significantly to improving the quality of learning, making it more creative, adaptive, and innovative. Therefore, SMART-TENDIK PAUD can serve as an alternative model of effective, applicable, and sustainable management innovation in enhancing the professionalism of early childhood education teachers.

Evaluation of training programs at the UPT Balai Latihan Kerja (BLK) in East Java has become an essential component in measuring the success of competency-based training implementation that aligns with labor market needs. This study applies the Systematic Literature Review (SLR) method to answer the research questions regarding the effectiveness of training programs at UPT Balai Latihan Kerja (BLK) in East Java in improving work competencies, the evaluation models used in BLK training programs, and the challenges documented in the design of the evaluation studies. The literature search was conducted through Google Scholar using inclusion and exclusion criteria, guided by the research questions determined by the researcher, and a three-point Quality Assessment framework to assess the methodological rigor and contribution of each journal's evaluation results. From the five journals analyzed, the key findings indicate that BLK training programs in East Java are relevant to workforce development needs and implemented based on the Competency-Based Curriculum (Kurikulum Berbasis Kompetensi/KBK). This study contributes important insights to the understanding of vocational program evaluation at BLK, highlighting that current evaluation approaches possess structured and relevant implementation foundations, and generate a positive impact in supporting the improvement of work competencies among BLK participants and alumni, particularly in the East Java region.

Background: The maternal mortality rate in Indonesia reaches 177 per 100,000 live births, with 75 percent of deaths occurring in the first week of the postpartum period. The partnership between midwives and village shamans is an adaptive strategy to optimize maternal health services, but its implementation still faces various systemic obstacles. Objective: To analyze factors related to the partnership of village midwives and shamans in caring for postpartum mothers in the BLUD area of the Tumpung Laung Health Center UPT. Methods: A quantitative study with a cross-sectional design involved 50 respondents using a total sampling technique. Data were collected through a structured questionnaire and analyzed using the Chi-square test with a significance level of p<0.05. Results: There was a significant relationship between age (p=0.032), work experience (p=0.048), education (p=0.000), knowledge (p=0.000), competence (p=0.000), quality of communication (p=0.000), frequency of interaction (p=0.000), policy support (p=0.000), and availability of facilities (p=0.000) with partnership effectiveness. Conclusions: Effective partnerships are influenced by a multiplicity of factors at the individual, interpersonal, and structural levels, requiring holistic interventions through capacity building, communication strengthening, and comprehensive policy support to accelerate maternal mortality reduction.

Digital transformation in the public sector has encouraged the adoption of Artificial Intelligence (AI) as a strategic instrument to enhance the effectiveness and quality of public service delivery. In Indonesia, the implementation of AI within the public administrative system remains at an early stage and faces various structural, regulatory, and ethical challenges. This study aims to analyze the opportunities, challenges, and ethical implications of AI implementation in Indonesia’s public administration. The research employs a qualitative approach through literature review and policy analysis of governmental digital transformation regulations. The findings indicate that AI holds significant potential to improve bureaucratic efficiency, service transparency, and data-driven decision-making processes. However, regulatory gaps, limited digital literacy among public officials, the risk of algorithmic bias, and data protection concerns constitute major obstacles to its effective implementation. The novelty of this study lies in integrating public administration management analysis with a public service ethics framework grounded in good governance principles within the context of AI implementation in Indonesia. This study recommends strengthening regulatory frameworks for AI in the public sector, enhancing human resource capacity, and developing ethical guidelines for AI utilization to ensure that public services remain accountable, equitable, and oriented toward the public interest.

Operational quality in the oil and gas sector strongly depends on the accuracy of monitoring instruments, such as oxygen analyzers. This study aims to validate the performance of an oxygen analyzer using the Factory Acceptance Test (FAT) procedure conducted by PT JGC Indonesia. The testing methodology involves the use of reference gases at different concentration levels, including nitrogen as the zero gas, to evaluate the linearity and consistency of the analyzer output within the 4-20 mA signal range. In addition, the tests are performed to verify the conformity of the instrument response with the datasheet specifications and applicable FAT procedures. The results show that the analyzer provides accurate measurement data with low deviation and a responsive alarm system under critical conditions. These findings confirm that the instrument meets the required technical standards. Based on the validation results, the oxygen analyzer is considered ready for installation and suitable for operation to support process safety in oil and gas facilities. This achievement confirms that a rigorous validation process is key to ensuring peace and operational safety in the oil and gas industry.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

The Mapan Satria Program represents the Kediri City Government's commitment, through the Department of Transportation, to enhance public service quality in the bus transportation sector under the name Bus Satria; however, operational obstacles such as schedule unreliability and insufficient public information persist. This study aims to describe the effectiveness of the Mapan Satria Program managed by the Kediri City Department of Transportation commencing in 2023. Program effectiveness was analyzed based on Annas's (2017) seven indicators using a qualitative method with data collection through observation, interviews, and documentation. The analysis indicates that the Mapan Satria Program (Moda Angkutan Pelayanan Aman dan Nyaman Sarana Transportasi Kediri Bahagia) has not yet operated effectively. While aspects such as timeliness, work mechanisms, inter-agency cooperation, fund disbursement, and monitoring and evaluation have been implemented in accordance with regulations, others remain suboptimal, including limited human resources, service deviations, inadequate information updates, and low public utilization. Therefore, the author recommends optimizing the program by expanding socialization to the wider community, providing clear schedule information at bus stops to facilitate passengers, strengthening the operational monitoring and evaluation system, and imposing strict sanctions for deviations.

Instilling local wisdom values in early childhood is crucial for strengthening cultural identity and contextual learning. Learning media based on local wisdom needs to be developed and its effectiveness evaluated in early childhood education. This study aims to evaluate the quality of local wisdom-based instructional media for early childhood education across five aspects: systematics, language, substance, graphics, and usefulness. A descriptive quantitative survey was conducted with 100 early childhood teachers in Surakarta. The instrument was a validated questionnaire using a four-point rating scale. Reliability testing confirmed internal consistency, and ethical protocols (informed consent, anonymity) were observed. The research results show that, Findings reveal that systematics and usefulness scored highest, showing that the media is well-structured and supportive for teaching practice. In contrast, the substance and graphic aspects scored lower, indicating limited content depth and visual quality. Divergent views were noted on the use of foreign terms. Local wisdom-based instructional media is highly relevant and useful, but improvements are required in substance integration and visual design. This study contributes novelty by providing a comprehensive evaluation framework for media grounded in local culture. Practical implications highlight the importance of contextualizing media development for teachers, while future research should examine direct impacts on children’s learning outcomes.

This study aims to analyze the influence of customer experience and perceived value on tourist satisfaction among users of the Sidoarjo City Tour Bus as a form of public tourism transportation service. The Sidoarjo City Tour Bus represents a regional government service innovation intended to support tourism development while improving urban tourism accessibility. This study employs a quantitative approach using a survey method involving tourists who used the Sidoarjo City Tour Bus. Data were collected through questionnaires distributed to 99 respondents and analyzed using multiple linear regression with the assistance of SPSS software. The results indicate that customer experience does not have a significant effect on tourist satisfaction, suggesting that the experiences perceived by users are still subjective and not consistently felt by all service users. In contrast, perceived value has a positive and significant effect on tourist satisfaction, making it the most dominant variable in influencing satisfaction among Sidoarjo City Tour Bus users. These findings indicate that tourist satisfaction is more strongly determined by perceptions of benefits, service quality, and the suitability between sacrifices made and benefits received rather than by emotional experience alone. Therefore, this study recommends that the management of the Sidoarjo City Tour Bus and the local government enhance service value through improvements in facilities, information systems and registration processes, as well as optimization of comfort and schedule certainty. These efforts are important to increase tourist satisfaction and to support sustainable tourism development in Sidoarjo Regency.

Consumer protection is a crucial aspect of trade and industrial activities to ensure consumers’ rights to quality, safety, and fairness in obtaining goods. In the field of metrology, qualitative improvement of product quality plays a strategic role as a preventive effort to minimize consumer losses caused by unfair or non-compliant trading practices. This is in line with Law Number 8 of 1999 on Consumer Protection, which emphasizes legal certainty, honesty, and the responsibility of business actors toward consumers. This study aims to examine qualitative product quality improvement as an effort to realize consumer protection in the field of metrology. The research employs a qualitative descriptive approach through literature review and analysis of regulations, policies, and the role of metrological institutions in supervising product quality. The discussion focuses on compliance with standards, supervisory mechanisms, and the responsibility of business actors as key elements of consumer protection. The findings indicate that qualitative improvement of product quality contributes to fair transactions, enhances consumer trust, and prevents practices that may harm consumers. This study is expected to serve as a reference for relevant institutions in strengthening the role of metrology as an integral part of the consumer protection system.

The use of information technology is a crucial element in improving the effectiveness of village government administration. One implementation of e-government at the village level is the use of the Village Apparatus Information System (Sipede) as a medium for managing village apparatus data. This study aims to analyze the effectiveness of Sipede's implementation in supporting village apparatus data management in Candi District. The study used a descriptive qualitative approach with data collection techniques through in-depth interviews, observations, and system document reviews. Data analysis was conducted using the Agile Response in E-Governance framework, which includes nine effectiveness indicators: Ease of Use, Usefulness, System Quality, Technology Mindfulness, Software Reliability, Communication Transparency, Trust, Collaboration Efficiency, and Traceability. The results show that Sipede is quite effective in improving data accuracy, information integration, and coordination between government agencies. This effectiveness is evident in the ease of use of the system, the clarity of the workflow, and the level of apparatus trust in the data generated. However, the use of Sipede still faces obstacles in the form of limited system access and suboptimal usage practices. Therefore, efforts are needed to develop systems and increase user capacity to support more accountable and sustainable village apparatus administrative governance.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

College represents a critical transitional period requiring high adaptation, where students often face escalating academic demands triggering psychological pressure. This study aims to deeply analyze the reciprocal relationship between sleep patterns and academic stress levels, and identify their subsequent impacts on cognitive function (concentration) and students' physical health. This study employs a qualitative method with a Systematic Literature Review approach. Data were collected through comprehensive searches of accredited national and reputable international scientific articles published within the last five years (2020–2025). Literature selection was conducted rigorously using specific keywords to screen empirical studies relevant to stress, sleep, and student health variables. Data synthesis reveals a strong and consistent negative correlation. High levels of academic stress whether stemming from the adaptation phase of new students (initiation), thesis preparation workload, or role conflicts in working students are significantly associated with decreased sleep quality. Physiologically, stress triggers increased cortisol levels causing insomnia, shortened sleep duration, and prolonged sleep latency. Subsequent impacts are proven to drastically reduce learning concentration and trigger physical health disorders, including an increased risk of primary dysmenorrhea in female students. This study concludes that academic stress and sleep disturbances form a destructive cycle that exacerbates one another. Therefore, holistic institutional interventions in the form of sleep hygiene education and stress management training are needed to break this chain for the sake of student well-being and academic performance.

Hospitals often face difficulties in determining patient treatment priorities due to limited medical resources and the uncertainty of patient conditions. Conventional prioritization methods tend to rely on subjective judgment, which can lead to inconsistent decisions and delays in treatment. This study aims to apply fuzzy logic in a decision support system to determine patient priority levels more objectively and systematically. The proposed method utilizes a fuzzy inference system that processes several criteria, including the severity of symptoms, vital signs, patient age, and waiting time. These criteria are represented as fuzzy sets and evaluated using a set of inference rules to generate priority classifications. The results indicate that the fuzzy logic–based system is able to classify patient priorities more consistently and transparently compared to manual assessment. The system provides clear priority categories that can support medical staff in making faster and more accurate decisions. The findings imply that the implementation of fuzzy logic in hospital decision support systems can improve the quality of healthcare services, enhance fairness in patient handling, and optimize the allocation of medical resources, particularly in emergency and high-demand situations.