Data Collection Stage Research Articles

TRANSFORMATIONAL LEADERSHIP STYLE: HOW PRINCIPALS CAN HELP TO IMPROVE THE QUALITY OF LEARNING IN ELEMENTARY SCHOOL

The improvement of the quality of SD Negeri 07 Purwoharjo cannot be separated from the principal's leadership in managing the school. This study aims to describe the implementation of the principal’s transformational leadership style in enhancing school quality, covering aspects of idealized influence, inspirational motivation, intellectual stimulation, and individual consideration. This research employed a qualitative method with data collected through observation, interviews, and documentation. Data analysis was conducted through the stages of data collection, condensation, presentation, and conclusion drawing. The validity of the data was ensured through source triangulation. The results of the study show: (1) Idealized influence is implemented through role modeling, transparency, fairness, care, communication of the vision and mission, and openness; (2) Inspirational motivation is realized through giving awards, fostering a familial atmosphere, listening to aspirations, and encouraging open communication and collaboration; (3) Intellectual stimulation is applied by promoting innovation, openness to new ideas, facilitating discussions, providing training, and supporting learning communities; (4) Individual consideration is carried out through personal attention and support, personal interactions, mentoring, and open communication with school members.

Analisis Pemeliharaan Pesawat Pasca Insiden Bird Strike pada Engine #1

This study aims to analyze the aircraft maintenance procedures following a bird strike incident on Engine #1, focusing on incident identification, maintenance actions taken, damage evaluation, follow-up recommendations, and compliance with aviation industry standards. Data for the study were obtained through official technical documentation such as the Technical Standard Manual (TSM) and Aircraft Maintenance Manual (AMM). Data analysis was conducted using the Miles & Huberman method, which includes stages of data collection, data reduction, data presentation, and conclusion drawing. Data validity was ensured through source triangulation, relying on various related documents to strengthen the findings. The results showed that the maintenance procedures applied were in accordance with international standards. Post-incident maintenance was carried out systematically and well-documented, with no procedural violations or technical neglect. The damage evaluation of Engine #1 revealed that the corrective actions implemented were effective, ensuring the aircraft's safe return to service. Recommendations for further maintenance include enhancing supervision of existing procedures. Overall, the post-incident maintenance met best practices in the civil aviation industry, ensuring aircraft safety and reliability.

An Examination of Middle School Students' Mathematical Identities in a Social Context

This study examines the mathematical identities of middle school students in a social context. Mathematical identity is an important factor affecting students' relationships with mathematics and their success in mathematics. Identity is formed by individuals' interactions with their perceptions and environment. In this context, mathematical identity is also shaped by social environment, education system, socio-cultural norms, and socio-economic status. In this research, the case study design, one of the qualitative research methods, was used. This research was conducted with 24 middle school students studying in different six public schools in a provincial center in the Marmara Region in the 2023-2024 academic year in Turkey, and 24 relatives determined by these students. The participants were selected through the maximum diversity sampling method. In the data collection stage, the "Mathematics Identity Scale" and the "Mathematics Identity Social Context Interview Form" developed by the researchers were used. The data were analyzed through a content analysis method. Findings indicate that themes such as attitudes toward the social environment, education system, socio-cultural norms, and socio-economic status significantly affect students' mathematical identities. In addition, it has been determined that individual characteristics such as mathematical communication skills, mathematical literacy, attitude towards mathematics, and mathematical persistence also affect the formation of mathematical identity. In conclusion, students' social environments and individual characteristics play a significant role in shaping their mathematical identities. Researchers are recommended to conduct similar studies in different social and cultural contexts and to examine the changes in students' mathematical identities through longitudinal studies.

Multivariate Time-Series Missing Data Imputation with Convolutional Transformer Model

The rapid progress in artificial intelligence technologies has significantly impacted the global economy, driving transformative changes in manufacturing and giving rise to intelligent manufacturing. In this context, multivariable time-series data have become an essential resource for modern industries. This paper introduces the Point Energy Technology, an advanced system for energy monitoring and data acquisition developed by our team. The system has been successfully deployed with several industrial partners, including a combined heat and power system in a local industrial park. Despite its capabilities, data loss remains a persistent issue, which is often caused by measurement or transmission errors during the data collection and transfer stages. These errors result in the loss of vital data samples for effective process monitoring and control. To tackle this issue, we present a convolutional transformer imputation model that is based on self-attention to generate missing data samples. This model effectively captures both historical and future sequence information through an enhanced masking mechanism while also incorporating local dependency information through the symmetrically balanced use of convolution and self-attention. To evaluate the performance of the proposed model against classical models, the energy-related data from a local industrial park were used in this experiment. Considering the real-world conditions, the missing data were categorized into two types: continuous missing and random missing. The experimental results demonstrate that our model produced high-quality data samples, effectively compensating for gaps in the multivariable time-series data.

Prediction of failures in fiber-optic information transmission systems

The article discusses a method for predicting failures in fiber-optic data transmission systems using a self-analysis mechanism. The proposed method is based on the use of machine learning algorithms that can adapt to changing operating conditions by automatically selecting or retraining models. The method includes the stages of data collection and preprocessing, feature extraction, construction of predictive models and their dynamic optimization. The self-analysis mechanism provides continuous assessment of the accuracy of forecasts and allows timely adjustment of model parameters. Testing on actual data showed high forecast accuracy and the superiority of the proposed method over traditional approaches. The results are visualized using error and deviation graphs, confirming the effectiveness of the proposed method.

Perlawanan Ulama dan Petani terhadap Kebijakan Kenaikan Pajak oleh Pemerintah Kolonial Belanda di Cimarame Garut, 1919

This research examines the Cimareme Incident in Garut as a resistance to Dutch colonialism in the early 20th century. Through qualitative qualitative historical method, this research traces the socio-economic background of the Cimareme of the Cimareme people who were mostly farmers, as well as the oppressive colonial policies such as the obligation to sell rice, which triggered resistance from the Cimareme people led by Haji Hasan Arif. The people of Cimareme, who were mostly farmers, lived under the pressure of colonial economic policies that forced them to sell their crops at disadvantageous prices. This research was conducted through the stages of data collection or heuristics from literature sources, verification of the validity of sources, interpretation of historical facts, and historiography. The results revealed that the Cimareme Incident was not only a resistance to colonial policies that harmed farmers, but also showed the strong spirit of nationalism among the Garut people. This resistance showed how religious figures, especially Haji Hasan Arif, played an important role in mobilizing the people to uphold justice and fight against colonial exploitation. Although this resistance was followed by the arrest and suppression of Kyai, Peasants, Santri, and Hajis, this event became an important symbol in the struggle for Indonesian independence. The Cimareme Incident strengthened the collective consciousness of the community to unite against colonialism and showed how religion and nationalism became a driving force in the struggle towards independence.

Game Edukasi Jenis Bunga Berbasis Android

Android-based educational games are increasingly evolving as interactive learning tools. This research aims to design and develop an educational game that introduces various types of roses. The game is developed using Unity and the C# programming language, following the MDLC (Multimedia Development Life Cycle) method, which includes planning, data collection, implementation, and testing stages. The main features of this game include introducing different rose species along with their Latin names and characteristics, as well as interactive games such as quizzes and puzzles to enhance user engagement. Testing was conducted using the black-box method to ensure the game's functionality. Evaluations based on questionnaires indicate that this game is highly effective in improving users' knowledge of rose species. The results of this study are expected to contribute to the development of more engaging and educational technology-based learning media.

Gen Z Teacher Transformation : Self-Development Strategy to Improve Social and Personality Competence

This study aims to reveal the transformation of Gen Z teachers by highlighting the problem of improving social and personality competencies and identifying the strategies they use. This research used a qualitative approach with a case study method. Data collection was carried out using in-depth interview techniques with five Gen Z teachers in Lubuklinggau City who were selected using purposive sampling. The data analysis technique uses the Miles and Huberman model which includes the stages of data collection, data reduction, data presentation, and conclusions. The findings of this study reveal that Gen Z teachers face problems such as lack of self-awareness, dependence on technology, minimal training, and limited time and workload. As a result, Gen Z teachers are at risk of experiencing gaps in communication skills and emotional intelligence, being less prepared to face challenges involving collaboration in the workplace, and in their social lives. The strategies used by Gen Z teachers in their social and personality development include self-assessment, self-development, and utilization of technology. They also continue to learn and adapt in developing their personality and social interactions, in order to improve the quality of their teaching and social relationships.

PENGARUH AKAD PENDANAAN (FUNDING) TERHADAP KEPUASAN NASABAH PADA BANK ACEH SYARIAH CAPEM KOTA JUANG

Syariah institutions currently have sharia-based products and are in great demand by the public, because people gradually begin to know and realize that syariah funding products have characteristics and concepts that are different from other institutions. With the good quality of a funding product, this will also have a positive impact on customer satisfaction. So in this case the author wants to examine the relationship between good funding and customer satisfaction at Bank Aceh Syariah Capem Kota Juang. The hypotheses in this study are funding contracts and customer satisfaction. Based on the results of research conducted through the data collection stage by distributing questionnaires to 65 customers with various statements and questions and tested using the SPSS version 25 application. It can be concluded that in the t-Test Coefficient table (partial) it can be seen that the value of t counts the funding agreement variable (funding) is 9.673 > 1.998, and the significance value is 0.000 <0.05. then it can be decided that the funding contract variable (X) has a positive effect on customer satisfaction.

Assessment of land potential in Kon Tum province for agricultural use by remote sensing methods and geoinformation technologies

The article presents a methodology for assessing land suitability for agricultural use in Kon Tum Province using remote sensing (RS) and geographic information systems (GIS). The study is based on the integration of multispectral data, radar data, and unmanned aerial vehicle (UAV) imagery. The research algorithm includes stages of data collection and loading into the ArcGIS system, mathematical analysis of spectral channels for calculating NDVI and land surface temperature (LST) indices, as well as terrain analysis. Additionally, a sampling point network was created, statistical data processing, image clustering, and a scoring assessment of territories with subsequent interpolation were also performed. The study resulted in identifying clusters of the most favorable areas for agricultural activities. The outcome of the research is a land suitability map for agricultural use, which can be applied to optimize land resource management in the region.

Optimizing the compliance third-party supervision workflow of involved enterprises using artificial intelligence ant colony algorithm

This article optimized the compliance third-party supervision workflow of the involved enterprises based on the artificial intelligence ant colony optimization (ACO) algorithm. The basic principles and application advantages of ACO were introduced, and a heuristic information matrix was defined using ACO to optimize the data collection and analysis stage of the compliance third-party supervision workflow. During the experimental phase, a feasibility analysis was conducted on the optimization of third-party supervision workflows for compliance by ACO involved enterprises through simulation experiments. The experiments were evaluated from four aspects: data quality, model performance, scheme effectiveness, and supervision effectiveness. Among the metrics data for the ACO-optimized test set were 0.03 and 0.025 for MSE (Mean Square Error) and Γ, 0.8, 0.78, 0.79, and 0.88 for Accuracy, Recall, F1 Score, and AUC-ROC (Area Under the Curve–Receiver Operating Characteristic), and 0.28, 0.4, 0.88, and 0.12 for CER (Cost-Effectiveness Ratio), NPV (Net Present Value), SCR (Supervision Coverage Rate), and CRC (Compliance Rate Change), respectively. The experimental results showed that, in terms of data quality, model performance, scheme effectiveness, and supervision effectiveness, the evaluation indicators of the compliance third-party supervision workflow of the involved enterprises optimized using ACO were superior to those without ACO optimization.

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images

Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.

Potential Implementation of Cleaner Production in Surimi Manufacturing

Abstract Cleaner production is an effort to reduce waste in the production process and manage waste. It means reusing waste to generate value from the perspective of both the environment and the economy. The objectives of the research are to identify potential waste in surimi manufacturing and provide value added to the implementation of cleaner production. The data collection stage uses qualitative methods by interviewing and observing directly. The research uses data analysis from the implementation of cleaner production with the reuse concept approach and value-added calculation. The results of the research are: (1) The potential waste from the surimi manufacturing is solid waste that is fibre, fishbone, and fish scale. Based on the process of making 9000 kilogrammes of surimi that produce 318 kilogrammes of fibre waste and 1,838 kilogrammes of fishbone and fish scale waste. (2) The potential implementation of cleaner production on fishbone and fish scale waste has two alternatives. The first alternative is selling fish waste directly and the second one is processing fish waste into fish meal. Based on the value-added analysis, fish meal production can be chosen to handle fish waste compared to selling the waste directly. Fishmeal production is a good alternative for handling fish waste because it has positive value-added.

Using Big Data and Machine Learning to Predict Household Appliance Failures: A New Approach to Preventive Maintenance

This study explores the application of big data processing methods and machine learning algorithms for predicting household appliance failures, shifting from traditional reactive maintenance models to proactive preventive repair systems. The study is based on an analysis of historical data, error logs, and sensor readings, enabling the identification of hidden patterns that indicate potential malfunctions. The novelty of this study lies in the adaptation and comprehensive application of modern data analysis methods to the operational specifics of household appliances, as well as an assessment of the economic efficiency of such solutions. The applied methodology includes stages of data collection and preprocessing, feature engineering, machine learning algorithm implementation, and comparative economic analysis. The results demonstrate the potential of predictive maintenance in reducing downtime, optimizing repair costs, and improving service quality. The findings of this study are valuable for data analysts, household appliance engineers, predictive model developers, and companies engaged in appliance servicing and manufacturing, seeking to enhance preventive maintenance efficiency using advanced machine learning and big data analytics methods. This study aims to shift from reactive maintenance to proactive preventive maintenance, addressing real-world challenges faced by appliance manufacturers and service providers.

경기도 소규모 행복초등학교의 특성을 살린 교육 활동에 관한 사례 연구

Objectives The purpose of this study is to explore the main educational activities in the curriculum that take advantage of the small school characteristics of Happiness Elementary School in Gyeonggi-do and how students and teachers' autonomous educational activities that take advantage of the small school characteristics are operated. Methods The subjects of the study were a total of 70 students, 9 teachers, and 5 parents of Happiness Elementary School. Data were collected and analyzed by document data, participatory observation, and in-depth interviews. In the data collection and analysis stage, triangular verification and confirmation of participants in the entire research process, and peer teacher review were conducted to improve the validity of the study. Results The results of the study are as follows. First, the educational activities in the curriculum utilizing the characteristics of small-scale schools in Happiness Elementary School operated student-centered culture and arts education based on the Hanulim Ukulele Band and Sharing Education Theater Project, drone free flight and 3D printing education projects to foster future capabilities in the era of the 4th industrial revolution, and ecological experience garden classes where dreams grow. Second, student autonomy activities were activated by autonomous educational activities of students and teachers utilizing the characteristics of small-scale schools in Happiness Elementary School, various student clubs were operated autonomously, teacher badminton sports clubs were operated in connection with student sports badminton clubs, and teacher music band clubs were operated autonomously in connection with professional learning communities. Conclusions This study suggests that educational activities that reflect the environment of small schools have unique characteristics that differentiate them from large schools, and that there is a need to expand the perspective to understand the educational activities of small schools.

전공자율선택제에 대한 연구동향분석: 2008~2024년(11월) 발행된 국내 학술 연구물을 대상으로

Objectives The purpose of this study is to identify trends and derive future research directions based on the analysis data of previous studies on the college of liberal major. Methods According to the analysis data collection stage, 39 research papers published from 2008 to 2024 (November) were selected as analysis data. Research trends were analyzed using five analysis criteria. As research tools, ‘EXCEL’ and big data analysis tool ‘Textom’ were used to generate descriptive statistics and visualization data. Results First, research on the college of liberal major is rapidly increasing in line with the policy trend of the Ministry of Education. Second, most of the research is empirical, so theoretical research needs to be supplemented. Third, students and literature were mainly set as research topics. Fourth, literature analysis accounts for the largest proportion, and the proportions of other research methods (quantitative research, qualitative research, mixed research, and others) are relatively similar. Fifth, keywords related to the college of liberal major are mixed, so it is necessary to review the establishment of research directions. Conclusions First, the tendency of researchers in the field of education to depend on the flow of educational policies to set research topics should be improved. Second, the imbalance of research goals should be improved, and empirical research from various perspectives should be conducted based on supplementation of theoretical research. Third, balance in research subjects and research methodology should also be achieved. This study may be criticized for the limitations of the analysis criteria and the fact that the analysis data is limited to domestic research, but it is valuable in that it is the first analysis of research trends related to the college of liberal major, and it can be used as basic research data to activate future research.

Position Competency Development in Improving the Performance of State Civil Apparatus in the Organization Bureau of the Regional Secretariat of East Java Province

This study aims to identify and analyze how Job Competency Development in Improving the Performance of State Civil Apparatus in the Bureau of Organization of the Regional Secretariat of East Java Province. The dimensions of job competency in this study consist of task-performing skills, task-managing skills, action-taking skills, collaboration skills and adaptation skills. In this study, the author uses a descriptive qualitative method to find out more about job competency development in improving performance. Data collection methods are carried out through participatory observation, semi-structured interviews and documentation which are then analyzed using an interactive model with stages of data collection, data reduction, data presentation, and drawing conclusions. The results of this study indicate that the efforts made by the Bureau of Organization of the Regional Secretariat of East Java Province in improving skills in the competency dimension have succeeded in improving job competency. Furthermore, with the increase in State Civil Apparatus competency, State Civil Apparatus performance has increased. However, there are still several inhibiting factors that are expected to be resolved in the future by increasing close supervision by direct superiors and giving awards according to the competency and performance results of each State Civil Apparatus.

ANALYSIS OF MATHEMATICAL REASONING ABILITY IN TWO-VARIABLE LINEAR EQUATIONS BASED ON STUDENTS' EMOTIONAL INTELLIGENCE

T This study aims to analyze mathematical reasoning ability in terms of emotional intelligence in solving SPLDV material problems. The indicators used are proposing conjectures, manipulating mathematics, providing reasons or evidence for the correctness of the answer, and drawing conclusions. The research method used is qualitative, with research subjects consisting of 3 students of class VIII-G SMPN 1 Karangnunggal. The instruments used include interviews, emotional intelligence questionnaires, and one mathematical reasoning ability test question. The data collection stages consist of the results of interviews with teachers regarding emotional intelligence, emotional intelligence questionnaires, reasoning ability test results, and data from interviews with students who have filled out the reasoning ability questions. The data collected is simplified to make accurate conclusions. The results of the study show that students with high emotional intelligence can meet all indicators and can solve problems correctly. Students with moderate emotional intelligence can meet 3 indicators of mathematical reasoning ability, namely being able to propose conjectures, being able to manipulate mathematics, and being able to provide reasons or evidence for the correctness of the answer. Students with low emotional intelligence are only able to meet two of the four indicators of mathematical reasoning studied, namely being able to propose conjectures and being able to manipulate mathematics.

Survey sampling design in wave 1 of the Global Flourishing Study.

The Global Flourishing Study (GFS) is an international collaboration to develop a publicly accessible data resource to promote global research on human flourishing. These data include over 200,000 participants from 22 geographically and culturally diverse countries and one territory designed to be nationally representative of the adult population. The GFS is intended as a longitudinal panel study with recruitment and empanelment for Wave 1 occurring between April 2022 and December 2023. Future waves of data collection will invite participants to complete a survey annually. The annual survey covers a robust set of measures on well-being, health, social, economic, political, religious, spiritual, psychological and demographic variables. The current paper describes the sampling methodology and weighting approaches used to project the samples to be nationally representative. Details are provided on interviewer training and data collection, probability and non-probability samples, creating weights, design effects, and future data collection stages.

KRITIK SOSIAL DALAM CERITA KYAI AGENG GRIBIG SEBAGAI UPAYA PENANAMAN NILAI BUDAYA GENERASI MUDA

<p>Folklore contains social criticism that can be used as an effort to instill cultural values in the younger generation. The younger generation today is largely unfamiliar with folklore, especially folklore in their surrounding areas, whereas in these folklores there are noble values that can be used as guidelines for behavior. This study aims to describe the social criticism contained in the story of Kyai Ageng Gribig. This study is a qualitative study with data in the form of words or sentences containing social criticism and data sources in the form of informants and documents regarding the story of Kyai Ageng Gribig. This study uses a sampling technique in the form of purposive sampling. Data collection techniques are carried out by interviews and document analysis. The data analysis technique uses interactive analysis techniques according to Miles and Huberman in the form of data collection stages, data reduction, data presentation, and drawing conclusions. Based on the results of the story analysis, nine social criticisms were found according to Soekanto's theory. Social criticism was found based on social problems of poverty, crime, family disorganization, problems of the younger generation towards modern society, war, violations of norms in society, population problems, environmental problems, and bureaucracy. These results can be used as lessons for readers to act well in life.<em></em></p>