Data Processing Techniques Research Articles

Quantifying Physical Activity and Sedentary Behavior in Adults with Intellectual Disability: A Scoping Review of Assessment Methodologies.

Background/Objectives: Methodologies for assessing behavior form the foundation of health promotion and disease prevention. Physical activity (PA) and sedentary behavior (SB) assessment methodologies have predominantly been developed for adults without an intellectual disability (ID), raising credibility concerns for adults with ID. The purpose was to synthesize the current state of assessment methodologies for quantifying PA and SB volume in the free-living setting for adults with an ID. Methods: Following PRISMA guidelines, eleven databases were searched through December 2023, yielding 8174 records. Data were extracted in Covidence (v.2.0), obtaining quantified PA and SB volume and assessment methodology characteristics across data collection and analysis, including tool(s) and technique(s) used, preparatory actions taken, instructions provided, and behavioral strategies employed during data collection. Results: Of the 8174 articles screened, 91 met the inclusion criteria. Common metrics included minutes/hours per day/week and steps per day/week. Despite 80% of the studies using objective techniques, substantial variation existed across studies regarding wearable models, sampling frequency and epoch length settings, calibration protocols, wearable placements, and data processing techniques. Limited studies provided instructions that did not exclusively rely on spoken language. Behavioral strategies varied, including self-monitoring, providing assistance or supervision, administering questionnaires verbally, issuing reminders, and offering monetary incentives. Conclusions: This review underscores the need for greater consistency and accessibility in PA and SB assessment methodology for adults with ID. Tailored preparation, instruction, and behavioral strategies may enhance assessment viability and suitability for adults with ID, with or without caregiver or researcher involvement in the free-living setting.

The correlation between the active/buried faults and aeromagnetic data in inner and inner-east Anatolia, Turkey

The paper focuses on investigating the active/buried faults in inner and inner-east Anatolia, Turkey based on aeromagnetic data and comparison with the active fault map. This region includes the major tectonic units of Turkey and the largest basins of Turkey (Tuzg.lü Basin (TB) and Sivas Basin (SB)). Thus, the region deserves a detail investigation to delineate the lineaments of the active/buried fault system or the causative sources located in the upper crust. To this end, the region has been divided into two parts as region-1 and 2 including TB and SB, respectively. In this context, the geophysical data processing techniques including power spectrum analysis, high‑pass filter, and second vertical derivative method (SVD) have been applied to the aeromagnetic data of the both regions. Firstly, by utilizing power spectrum analysis, the average depths of the deep and shallow causative sources in the region-1 have been calculated as 11.37 km and 5.01 km whereas those depths are 8.5 km and 3.24 km for region-2. Afterwards, to enhance the responses of the residual anomalies, the high‑pass filter and the SVD method have been applied to the data. Hereby, these potential lineaments produced from the SVD map have been compared with the active fault map to delineate the uncovering buried faults and their lineaments in the region. Contrary to the discussion in the literature, this study revealed for the first time that the Tuzgölü fault zone located in the eastern part of lake does not merge with the Ecemis fault (EF) zone. Furthermore, the study has shown that this fault zone extended from Kayseri city to Sivas city does not junction with North Anatolian Fault Zone in the north, as well.

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

The Influence of Transformational Leadership Style and Competency Development on the Professionalism Index of State Civil Apparatus IPASN ) in Government Environment

Civil servants who are State Civil Apparatus (ASN) as one of the state servants and public servants are required to be pioneers in all fields, have high quality, dedication, and work ethic. One approach in an effort to improve ASN p erformance can be done through leadership. The purpose of this study was to determine the effect of leadership style and competency development on the State Civil Apparatus Professionalism Index (IPASN) in the Jember Regency Government Environment. The num ber of respondents in this study was 72 people as leaders of Regional Apparatus Organizations (OPD). The sampling technique used was saturated sampling (census) where all populations/respondents were used as samples. The data collection technique used the questionnaire method through the distribution of google forms. The data processing technique used was descriptive statistics using a Likert scale. The results of the data analysis showed that transformational leadership style a nd competency development had a positive and significant effect on the State Civil Apparatus Professionalism Index (IPASN).

Smart surveillance: deploying drones for efficient and adaptive monitoring solutions

The growing need for real-time and adaptable surveillance solutions has prompted the investigation of cutting-edge technologies within security and monitoring frameworks. This paper introduces an innovative surveillance approach that utilizes drones to improve situational awareness and operational efficiency across various domains, including public safety, border security, and environmental monitoring. The proposed system integrates autonomous drones equipped with sophisticated imaging capabilities, data processing techniques, and machine learning algorithms, facilitating real-time monitoring, threat identification, and comprehensive data analysis. Notable features of this system include autonomous path planning, the ability to adapt to changing environmental conditions, and effective communication with ground control stations. By merging drone technology with AI-enhanced analytics, this approach provides a scalable and cost-efficient surveillance solution that minimizes the need for human intervention while enhancing accuracy and response times. In this paper, we detail the design, implementation, and performance assessment of our system, underscoring its potential to transform conventional surveillance practices.

Creativity Efforts of Sasirangan Fabric Artisans in Increasing Sales in Islamic Economics Terms

This research is motivated by the important role of sasirangan cloth artisan in preserving local culture with diverse creativity. The aim of this research is to determine creativity in an effort to increase sales in terms of Islamic economics and the obstacles faced by artisan. This research is field research, with a qualitative approach. The research location is Sasirangan Village, Banjarmasin City. Data was obtained using interviews, with data processing techniques, then analyzed qualitatively and referring to the theoretical basis. The research results obtained are first, the creativity of Sasirangan cloth artisan in Sasirangan Village in an effort to increase sales in terms of Islamic economics is painting their own motifs, adding creations, participating in exhibitions, modifying motifs, using natural materials. Second, the obstacles faced by sasirangan cloth business artisan in Sasirangan Village in carrying out creativity are labor resource constraints, policy constraints regarding time regulations and constraints on raw materials, synthetic and natural dyes used in dyeing sasirangan cloth.

Crystallographic Dependence of Field Evaporation Energy Barrier in Metals Using Field Evaporation Energy Loss Spectroscopy Mapping.

Atom probe tomography data are composed of a list of coordinates of the reconstructed atoms in the probed volume. The elemental identity of each atom is derived from time-of-flight mass spectrometry, with no local chemical information readily available. In this study, we use a data processing technique referred to as field evaporation energy loss spectroscopy (FEELS), which analyzes the tails of mass peaks. FEELS was used to extract critical energetic parameters that are related to the activation energy for atoms to escape from the surface under intense electrostatic field and dependent of the path followed by the departing atoms. We focused our study on pure face-centered cubic metals. We demonstrate that the energetic parameters can be mapped in two-dimensional with nanometric resolution. A dependence on the considered crystallographic planes is observed, with sets of planes of low Miller indices showing a lower sensitivity to the field. The temperature is also an important parameter in particular for aluminum, which we attribute to an energetic transition between two paths of field evaporation between 25 and 60 K close to (002) pole. This paper shows that the information that can be retrieved from the measured energy loss of surface atoms is important both experimentally and theoretically.

Analisis Profitabilitas Pelaku Usaha Gula Semut di Desa Serayu Larangan

Research objectives a) Identify the internal and external factors of ant sugar business actors in Serayu Larangan Village, Mrebet District, b) Find out the level of production, production costs, profits and profitability of ant sugar business actors in Serayu Larangan Village, Mrebet District. The research method used was a survey with location determination carried out purposively in Serayu Larangan Village, Mrebet District with a total of 3,831 productive coconut trees managed by 169 craftsmen with an average production reaching 115,050 kg. The sample was determined purposively as many as 20 ant sugar business actors who were considered to represent those who had been working in this profession for more than 3 years and also as their main livelihood. The type of data collected is primary and secondary data through interview, observation and documentation techniques. Qualitative and quantitative data processing techniques. Profitability is the ratio between profit/profit from sales of ant sugar. The results of the research show the identification of internal and external factors in the ant sugar business, including availability of land, availability of raw materials, skills of the perpetrator, availability of labor in the family and sources of capital, market demand, prices of ant sugar products tend to be high, technological developments, and the presence of competitors. from outside villages, and collaboration with outside. Ant sugar business actors in Serayu Larangan Village, Mrebet District have an average total cost structure of IDR 94,952.37,- with an average of IDR 159,732.50,-. The gain or profit was IDR 64,780.13, - with a profitability value of the ant sugar business actor of 68.22%, meaning that every time IDR 1.00 of production input is used, it will produce a profit of IDR 68.22.

Development and Comparison of InSAR-Based Land Subsidence Prediction Models

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

Efforts to Increase Learning Activity in History Subjects According to the Learning Styles of Class X-2 Students at SMA Negeri 3 Medan

This research was conducted because the learning activity of students in class X-2 of SMA Negeri 3 Medan in history subjects was very low. The aim of this research is to increase students' active efforts in learning history. This research was conducted in the odd semester of the 2024/2025 academic year. This research uses the Kemmis and Taggart model. Data collection techniques through student observation and questionnaires. The data processing technique in this research uses descriptive analysis with percentages. The indicators assessed in this research are problem solving, expressing opinions, cooperation and social relations, and discipline. The results of this research show an increase in student learning activity in class X-2 from pre-cycle, cycle I and a very good improvement in cycle II.

ANALYSIS OF ACCOUNTING TREATMENT OF BIOLOGICAL ASSETS BASED ON PSAK NO. 69 AT LATONANG FARM PAREPARE CITY

This research aims to determine the accounting treatment of biological assets based on PSAK No. 69 at Latonang Farm and to determine the accounting treatment of biological assets at Latonang Farm from a sharia perspective as well as the implications of implementing PSAK No. 69 to the financial statements of Latonang Farm's biological assets. This was done to analyze the suitability of the accounting treatment for Latonang Farm's biological assets with PSAK No. 69. This research uses qualitative methods with a case study approach and field research. The data collection and data processing techniques use observation, interview and documentation methods. The validity test of the data used in this research is the credibility test. Meanwhile, the data analysis techniques used are data reduction, data presentation and drawing conclusions. The research results show that the accounting treatment of biological assets is based on PSAK No. 69 at Latonang Farm is generally in line with the provisions of PSAK No. 69. However, there are differences regarding the measurement of fair value. Latonang Farm does not revaluate the value of biological assets, but instead uses the acquisition price with adjustments at the end of each period. This approach was chosen because of the difficulty of measuring the value of biological assets precisely. This action is permitted in accordance with the provisions of PSAK No. 69 paragraph 30. Apart from that, the accounting treatment of biological assets at Latonang Farm is also in accordance with the sharia perspective, where the principles of honesty, trust and benefit are applied.

A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance.

Statistical analysis of digital transformation and its incidence in reducing the use of paper in a higher education institution: A case study

Reducing the carbon footprint of higher education institutions has become a global trend in recent years, as they are the axis of society's scientific and technological transformation that must be carried out with a sustainable approach. Among the many approaches that can contribute to this problem are the digital transformation of academic and administrative processes and its impact on reducing paper use. This study details the statistical analysis of the digital transformation process based on the modules and computer services implemented by the ICT department in a higher education institution and its incidence in reducing the use of paper by its teaching and administrative staff. For this purpose, an initial instrument was proposed, consisting of 18 ordinal, three categorical and two numerical variables. After applying statistical data processing and validation techniques, it was reduced to 13 ordinal variables, three categorical and two influential numerical variables. In addition, this study has proposed an appropriate statistical protocol for the design and extraction of information applicable in the context of higher education institutions, consisting of the techniques: Mahalanobis distances, confirmatory factor analysis, Kruskal-Wallis, Mann-Whitney's U and Wilcoxon tests. The results showed the power of confirmatory factor analysis as a technique for obtaining models to measure indirectly observed latent variables, such as perceptions of digital transformation and paper reduction. In addition, it was found that the modules, systems, and computer tools implemented in the institution significantly reduced the execution times for performing administrative and teaching tasks in the institution, and significant differences were identified in the perception of digital transformation and reduction of paper use among staff.

Organizational Performance Improvement Model Based on Talent Management Through Religiosity Organizational Citizenship Behavior

Purpose: This research aims to determine the influence of talent management on organizational performance; the influence of religiosity organizational citizenship behavior as a mediator on organizational performance; the influence of digital literacy as a mediator on organizational performance; the influence of talent management, digital literacy and religiosity organizational citizenship behavior simultaneously on employee performance and employee performance. organization, and designing a model for improving organizational performance through digital literacy and religiosity organizational citizenship behavior as mediators Theoretical Framework: Drawing from the practice-based view theory, this research focuses on several constructs and relationships between talent management, digital literacy and organizational citizenship behavior in ASNs in government agencies which have not yet been widely studied. This research is an effort to develop a model for improving organizational performance through talent management and ASN digital literacy and also combines aspects of organizational citizenship behavior which are based on religious values, which are still rarely used by previous researchers. The research method used is an explanatory survey approach to explain the relationship between variables through hypothesis testing in the field. Research that uses descriptive surveys and explanatory survey methods is carried out by collecting information from samples directly at the scene of the incident (empirical) through questionnaires with the aim of finding out the opinions of the samples regarding the research problem. And this research uses the Partial Least Squares Structural Equation Modeling analysis technique (PLS-SEM), to model many variables in research Results and Discussion: To examine the influence of mediator variables, Partial Least Squares Structural Equation Modeling analysis technique (PLS-SEM),to estimate the causal relationship between variables (causal model) that has been determined previously based on the theory. Answering research questions requires correct statistical data processing. The results of this research will be more powerful if adequate statistical data processing is used. Scientifically based research shows that findings can be evaluated and proven, and if other researchers used the same procedures, the results would almost certainly be the same. As a result, researchers chose statistical data processing techniques to use in these calculations. The result of research. Based on the results of research regarding "Model for Improving Organizational Performance Based on Talent Management Through Religious Organizational Citizen Behavior", the following results were obtained: Overview of talent management, religiosity organizational citizenship behavior, digital literacy, employee performance and organizational performance in Regency/City Government Agencies throughout West Java Province are in the high/good category, which means they are running in accordance with agency procedures. Talent management has a significant positive effect on organizational performance in Regency/City Government Agencies throughout West Java Province. This is based on the results of direct effect hypothesis testing, the t-statistics results obtained were 2.482 and the significance results (pvalue) obtained were 0.007 (0.007<0.05), so Ho was rejected and Ha was accepted. Religious organizational citizenship behavior is unable to mediate the influence of talent management on employee performance. This is based on the results of testing the indirect effect hypothesis, obtaining a significance result (pvalue) of 0.053 (0.053>0.05). Meanwhile, digital literacy is able to mediate the influence of Talent Management on Employee Performance. This is based on the results of testing the indirect effect hypothesis, obtained a significance result (pvalue) of 0.000 (0.000<0.05). Digital literacy, religiosity organizational citizenship behavior and employee performance are able to mediate the influence of talent management on organizational performance. This is based on the results of testing the indirect effect hypothesis, obtained a significance result (pvalue) <0.05. There is an increase in organizational performance through digital literacy and religiosity organizational citizenship behavior in talent management. Implications: The research results show that religiosity organizational citizenship behavior, digital literacy and employee performance are variables that can mediate the influence of talent management on organizational performance in Regency/City Government Agencies throughout West Java Province.

DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation

The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user’s specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments.

Synthetic Aperture Radar for Geosciences

AbstractSynthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability.

Predicting non-suicidal self-injury among Chinese adolescents: The application of ten algorithms of machine learning

Background and aimsHigh non-suicidal self-injury (NSSI) prevalence among adolescents is a global health issue. However, current prediction models for adolescent NSSI rely on a limited set of algorithms, resulting in biased predictions. Therefore, the aim of this study is to develop multiple machine learning models to enhance prediction accuracy and mitigate biases among Chinese adolescents. MethodsA total of 4487 junior and senior high school students in China were recruited. Multiple algorithms were included, such as logistic regression, decision tree, support vector machine, Naive Bayes, multi-layer perceptron, K-nearest neighbors, and ensemble learning algorithm like random forest, bagging, AdaBoost, and stacking to build predictive models. Data processing techniques, including standardization and the synthetic minority oversampling technique, were employed to optimize the predictive model. The model was trained on 70 % of the data, reserving 30 % for testing. ResultsThe ten prediction models achieved a good performance, with area under the receiver operating characteristic curve (AUC) scores above 0.700 in the test set. The stacking and random forest models achieved AUC scores of 0.904 and 0.898, respectively. The prediction performance of the Naive Bayes model was relatively poor. The top five important variables were resilience, bully, suicidal ideation, internet addiction, and depression. ConclusionsThe ensemble machine learning algorithm showed promising results predicting NSSI among adolescents. Such algorithms should be recommended for future NSSI research to enhance predictive accuracy. Identification of important features in NSSI prediction can help develop screening protocols and lay a foundation for clinical diagnosis and intervention in adolescent populations.

Advanced classification of drill core rock type and weathering grade using detection transformer-based artificial intelligence techniques

Rock classification plays a crucial role in various fields such as geology, engineering, and environmental studies. Employing deep learning AI (artificial intelligence) methods has a high potential to significantly improve the accuracy and efficiency of this task. The paper delves into the exploration of two cutting-edge AI techniques, namely Mask DINO and Mask R-CNN (convolutional neural network), as means to identify rock weathering grades and rock types. The results demonstrate that Mask DINO, which is a Detection Transformer (DETR), outperforms Mask R-CNN for the aforementioned purposes. Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection, as opposed to the Mask R-CNN’s f-1 scores of 84% and 75%, respectively. These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states. Although the study examines only two AI models, the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field. By incorporating these advanced AI techniques, logging personnel can obtain valuable references to aid their work, ultimately contributing to the advancement of geological and related fields.

The linear canonical W-transform

The time-frequency spectrum of seismic data is commonly used as an attribute in the analysis and interpretation of nonstationary seismic signals. To improve the capability of the W-transform to analyze nonstationary data, we introduce the linear canonical W-transform (LCWT) by implementing an affine matrix along with scaling parameters to generate high resolution and energy-concentrated time-frequency spectra. More features of nonstationary signals can be explored in the time-linear canonical frequency domain by transforming the time-frequency plane, which makes the algorithm more applicable to seismic signals. For the completeness of the LCWT, we also develop the inverse LCWT to extend its applications. Examples of synthetic and field seismic data show that a highly resolved and energy-concentrated time-frequency spectrum can be estimated with the LCWT with negligible additional computational burden. In addition, our algorithm is promising for several seismic data processing and interpretation techniques, such as reservoir characterization and thin layer identification.

Analisis SWOT Dalam Menentukan Strategi Kompetitif Sentra Batik Tulis Al-Barokah

The batik shop "Al-Barokah" is one of the major batik companies in Sumenep, which independently carries out production and marketing activities. As a high-medium scale batik company, “Al-Barokah” has several characteristics which are also internal strengths of the company, including the company's trademark that has been registered and officially in Desperindag, namely “Al-Barokah” which is located in the village of Pakandangan Tengah, Kec. Bluto Kab. Sumenep and using SWOT as a tool to compete and introduce 4P. Thus, the purpose of this study is to determine the strategy and method of Swot in determining the competitive strategy of Sentra Batik Tulis Shop. This type of research is qualitative with the research approach used is normative and sociological. The data sources of this research are primary data and secondary data. The data processing and analysis techniques were carried out using the Milles and Heubermen method with three stages, namely data reduction, data display and conclusion drawing. The research results show that the competitive strategy in the Al-Barokah Batik Tulis Center is more dominant and more specific to the development of the company, so that the 4P strategy is used as the main reference in competition, so that the competitive advantage is more dominant. In addition, the company views competitive strategy as the basis for developing its company. After a SWOT analysis, the business has great opportunities and strengths so that it can take advantage of existing opportunities. The strategy that must be applied in this condition is to support an aggressive growth policy (Growth Oriented Strategy). This strategy indicates that the company is strong and able to continue to grow by taking existing opportunities or opportunities to achieve maximum turnover. In this strategy, it can improve the competitiveness of written batik businesses in Pakandangan Tengah Village, Bluto District, Sumenep Regency.