Perform Data Analysis Research Articles

PERAN LITERASI DIGITAL DALAM MENINGKATKAN PEMAHAMAN SISWA KELAS XII (STUDI KASUS DI SMA NEGERI 1 TEGALOMBO)

This study aims to reveal: (1) the form of digital literacy used in SMA Negeri 1 Tegalombo (2) The causes of the implementation of digital literacy in SMA Negeri 1 Tegalombo (3) The role of digital literacy in improving the understanding of class XII students of SMA Negeri 1 Tegalombo. The research approach used in this field research is a qualitative approach, with the type of case study research. The data collection in this research was conducted by using interview, observation and documentation techniques. In order to perform data analysis, the technique used is the data analysis technique of the Cresswell model. To check the validity of the data so as to obtain valid and reliable data, the researchers used technical triangulation and data source triangulation. The results of the research show that at SMA Negeri 1 Tegalombo there are several forms of digital literacy, but the one that has the most existence and is often used is one of the aids in the form of an Independent Learning Pavilion (ABM) called kipin school. The cause of the implementation of digital literacy in the form of kipin school is due to the limitations of printed references in schools and also obstacles to accessing wifi due to frequent power outages. Kipin school has a big role for class XII high school students because there are many cross-level references and covers books with various scientific fields. So that it can support the process of students understanding the learning material.

Real-time mode of operation data analysis to catch the thread-tip denotes the failure cause of the grid-tie PV central inverter

The inverter is considered the core of the PV power plant. The inverter’s failure leads to generation loss and decreases plant availability. So, it is required to investigate a clear Root Cause Analysis (RCA) to deduce the failure causes and implement the required corrective action in addition to the preventive action to avoid more inverter failure, hereby maintaining the plant available to a certain value. This paper discusses real-time mode operation data analysis of the PV grid-connected inverter due to real central inverter incidents in Benban solar park located in Egypt.The central inverter plays an important role in the Mega-Scale PV power plant. The main function of this inverter is to convert the DC power produced by the PV modules to AC power to be injected into the utility grid by considering specific characteristics based on the grid code. The availability of any PV power plant directly depends on the healthy inverter’s operation. The more increases for the installed inverters, the less availability loss in the case of inverter partial or catastrophic failures. So, it is required to focus on the failure causes of the central inverter by implementing a technical analysis using the available operational data. The monitored data of the central inverter in the PV power plant is classified into two types. The first type is the continuous time data stored in the memory. It represents the waveforms of inverter outputs like voltage, current, frequency, …. etc. Unfortunately, in case of a catastrophic failure, the central inverter is completely charred, and the continuous time data is lost due to storage memory damage. The second type is the operation data that is recorded by the SCADA system (per one-minute interval). Hereby, the operation data is the sole data in the case of the completely charred inverter. The representation of the operational data in curves indicates symptoms that can be used for the RCA processes. The investigation outcomes include three results. The first result is detecting the signature of the IGBT thermal stress on the voltage balance of the DC link capacitor. The second result is verifying a scenario for the cause of the IGBT failure by implementing a technical mathematical model based on the detected symptoms that denote the fault signature which is considered the thread-tip for detecting the failure cause. The third result is the simulating scenario for the interpretation of a DC link capacitors explosion due to the short circuit fault that occurred due to IGBT failure. The investigation in this paper is performed based on operation data analysis of the PV grid-connected inverter (central type) due to a real incident. The analysis methodology is based on mathematical calculation for the IGBT junction temperature using the measured heatsink temperature. The study concludes that after the IGBT failure occurred, it was a short circuit for a while and closed the terminals of the DC link capacitors. So, the DC link capacitors exploded and produced heavy sparks that led to enough fire to burn the inverter container completely.

Cumulative fatigue damage theories for metals: review and prospects

PurposeScholars mainly propose and establish theoretical models of cumulative fatigue damage for their research fields. This review aims to select the applicable model from many fatigue damage models according to the actual situation. However, relatively few models can be generally accepted and widely used.Design/methodology/approachThis review introduces the development of cumulative damage theory. Then, several typical models are selected from linear and nonlinear cumulative damage models to perform data analyses and obtain the fatigue life for the metal.FindingsConsidering the energy law and strength degradation, the nonlinear fatigue cumulative damage model can better reflect the fatigue damage under constant and multi-stage variable amplitude loading. In the following research, the complex uncertainty of the model in the fatigue damage process can be considered, as well as the combination of advanced machine learning techniques to reduce the prediction error.Originality/valueThis review compares the advantages and disadvantages of various mainstream cumulative damage research methods. It provides a reference for further research into the theories of cumulative fatigue damage.

Pengaruh Terpaan Iklan Spaylater “Belinya Sekarang - Bayarnya Nanti” di Youtube terhadap Perilaku Konsumtif Mahasiswa di Surabaya

The purpose of this study is to see what influence exposure to Spaylater adverts "Buy Now - Pay Later" on Youtube has on the purchasing habits of students in Surabaya. This is an explanatory study, and the method employed is a quantitative approach. This study employed a purposive sample strategy using the following criteria to determine the population: 1) 19-24 years old; 2) Surabaya University students; 3) Have seen advertising on Youtube for "Shopee Paylater Later - Buy Now, Pay Later"; and 4) Actively utilize Shopee Paylater services and have had difficulty managing costs. In this study, 97 persons were sampled, and primary data was gathered through the distribution of questionnaires. Furthermore, the gathered data were evaluated using the SPSS test tool, which performed data analysis in the form of multiple linear regression analysis. The findings of this study reveal that advertisement exposure characteristics, such as frequency, length, and intensity, have a positive and substantial impact on the purchasing behavior of students in Surabaya.
 Keywords: Advertisement, Youtube, Advertisement Exposure, Consumptive Behavior

Kejenuhan Belajar Sejarah Kebudayaan Islam (SKI) Siswa Kelas X Di MAN 1 Agam Kecamatan Tanjung Raya

The background of the authors conducting this research, because in learning the history of Islamic culture students are not eager to listen and pay attention to the teacher in the learning process, students receive learning from the teacher using the lecture method. It was at this time that the signs that students were bored with learning SKI began to be seen, when learning took place there were students who were lazy to study, students tended to be busy, sleeping in class, and talking with their desk mates when the teacher explained the material so that students did not focus on learning. The purpose of this study was to find out the characteristics of the saturation and the factors that cause the boredom of learning SKI experienced by students of class X at MAN 1 Agam, and to find out how to overcome the saturation of learning SKI for students of class X at MAN 1 Agam. This study uses a qualitative approach that is field research (field research) with data collection techniques through observation and interviews. The key informants were class X students and the supporting informants were class X Islamic Cultural History subject teachers and school principals. Then the writer performs data analysis with data reduction, displays data, and then provides conclusions from a study. Based on the research results it was found that; (1) The characteristics of students who experience learning burnout in Islamic Cultural History (SKI) at MAN 1 Agam are: decreased motivation to learn, feel weak and get tired quickly, and have difficulty concentrating and easily forget in learning. (2) Factors that cause boredom in studying the history of Islamic culture are physical factors/physical fatigue, motivational factors in learning, methods that do not vary/monotone, inadequate facilities and infrastructure, study hours and lack of recreation and entertainment. (3) Ways to overcome the saturation of learning Islamic cultural history can be done in various ways, namely: approaching students, applying various learning methods, evaluating at the end of each lesson, and making physical changes to the room.

Improve Employee Key Performance Indicator through Establishment of High Performing Team Model (Case Study: TNP)

TNP, a company in cargo transportation and logistics, is grappling with challenges such as high employee turnover and struggles to meet performance goals, both on an individual and team level. The company’s turnover rate exceeds its target, and the overall Key Performance Indicator (KPI) achievement by High-Level Management remains below the company’s standard of 80%. This study aims to improve the situation by analysing the current KPI framework. High turnover disrupts operations and suggests employee satisfaction problems, while inconsistent KPI achievement indicates operational inefficiencies. The study utilizes various methods, including interviews with High-Level Management, questionnaires for employees, and analysis of historical performance data. The analysis includes external and internal evaluations, leading to a SWOT analysis, which contributes to the research findings. These findings highlight several areas for enhancement. Proposed solutions involve implementing a High-Performing Team (HPT) model, drawing inspiration from Wageman and Hackman models. This model thrives on collaboration, utilizing collective skills to surpass individual contributions. It emphasizes open communication, knowledge sharing, and complementary skills to address turnover and improve KPI achievement. The study’s significance lies in its potential to guide TNP’s transformation. Through refining the KPI system and addressing underlying concerns, the study aims to provide actionable recommendations. Ultimately, the study suggests that adopting a high-performing team model could bridge existing gaps. By fostering a collaborative culture and implementing team coaching, the company could potentially revolutionize its cargo transportation and logistics services while also improving employee retention.

Technology Focus: Field Development (September 2023)

The integration of machine learning and physics-based simulation modeling offers a comprehensive approach to optimizing unconventional development and addressing parent-/child-well depletion issues. By combining the predictive capabilities of machine-learning models with the insights gained from physics-based modeling, operators can develop customized solutions tailored to the specific characteristics of the reservoir. Machine-learning models can optimize well-spacing, -placement, and -completion designs, while physics-based simulation modeling provides a deeper understanding of reservoir behavior and performance. Machine learning has emerged as a powerful tool, leveraging large volumes of data to provide data-driven insights and predictive capabilities. By training machine-learning models on geologic, completion, and spacing data, operators can predict production performance and make informed decisions to enhance production and cost-efficiency. Parent/child interactions, where production from a parent well negatively affects subsequent child wells, pose challenges in unconventional development. Benchmarking, through the comparison and analysis of performance data, plays a crucial role in understanding and mitigating these issues. By benchmarking production data, completion designs, and reservoir properties, operators can identify best practices and develop strategies to minimize the interference between parent and child wells. By harnessing the power of data-driven insights and the knowledge gained from physics-based simulation modeling, operators can make informed decisions regarding well spacing, completion designs, and operational strategies. This integrated approach enhances decision-making, reduces uncertainties, and increases overall efficiency in unconventional development projects. As technology continues to advance, the use of machine learning and physics-based simulation modeling will further improve the optimization of unconventional development, driving the industry toward greater efficiency and sustainability. The selected papers and suggested additional reading provide valuable insights into the evolving field of development optimization using physics-based simulation models and machine learning. Recommended additional reading at OnePetro: www.onepetro.org. SPE 205651 Digital Field Development Planning: A Collaboration Between Technology and Process To Enable Fast and Efficient Field Development Planning by Mohd Aminuddin Bin Md Karim, Petronas SPE 207899 Artificial Intelligence for Infill Well Placement and Design Optimization in Multilayered/Stacked Reservoirs Under Subsurface Uncertainty by Shi Su, SLB, et al. SPE 208529 A Comprehensive Machine-Learning Approach for Quantitatively Analyzing Development Performance and Optimization for a Heterogeneous Carbonate Reservoir in the Middle East by Ruijie Huang, Research Institute of Petroleum Exploration and Development, et al.

Development of Marketplace Product Data Mining and Analysis Application as a Reference in Running a Business

<p><strong>Marketplaces has become the first choice as a third party that provides online selling and payment services that bridge sellers and buyers. The increasing need to shop in an efficient and convenient way has driven the popularity of using the marketplace. Moreover, with a variety of products and a fast transaction process, and more competitive prices, the use of the marketplace has shown significant growth in activity by the wider community. </strong><strong>With the increasing use of marketplaces, data analysis from marketplaces is becoming a key element to understand market trends and make informed transaction decisions. Data analysis systems based on bot technology and Web-Scrapping libraries, such as Puppeteer, allow users to perform data analysis more efficiently and quickly, minimize the need for a data analysis team, and reduce time and costs. Given these factors, this research focuses on developing a web-based application that can analyze data coming from marketplaces and present it to users quickly and accurately.</strong><strong></strong></p>

Applying the Delphi Approach to Prioritize Social Factors Affecting the Development of Children Under Six Years

BackgroundSocial determinants of health have a key role in the growth and development of children, particularly in early childhood which is mentioned from infancy to the age of six years old. These factors might cause disparities in living conditions and consequently bring about inequities regarding different aspects of development such as emotional, psychological, social, psychological, and intellectual. This research aimed to provide a model for prioritizing social factors affecting the development of children under six years.MethodsWe used quantitative–qualitative (mixed) method to perform data analysis. The statistical population included 12 medical experts and professionals in the field of children’s development and social determinants of health that were selected using the snowball method. In the quantitative section, a Delphi technique was applied to screen the extracted indicators. Then through applying a decision-making trial and evaluation laboratory (DEMATEL) method, the cause-and-effect interactions among main social determinants were identified. To analyze data, super decision software was used.ResultsAccording to literature review and the results obtained from focus group discussions, five dimensions including individual factors, family factors, environmental factors, governance, and global factors were identified. Based on the study findings, the criterion of “family factors” was mentioned as the most important priority affecting childhood development. Furthermore, the sub-criterion of “International Programs and Policies” received the greatest priority among other sub-criteria with a profound impact on children's healthy growth and development.ConclusionDespite the current knowledge about social determinants of health, it is required to identify the most influential socioeconomic factors on childhood development. In such a manner, political strategies for improving the health condition of children can be implemented based on scientific evidence. Due to the crucial role of family factors, environmental factors and other socio-economic conditions, health policy makers and public health practitioners should be informed of the importance of these factors in shaping the health condition of children.

Nanosized LiFePO4 Reveals the Electrochemical Kinetics of Electrolytes in Porous Li-Ion Battery Electrodes

Electrified transportation is critical for a decarbonized future, but high cost and range anxiety hamper the adoption of electric vehicles (EV). For low-cost EVs, extreme fast charging of lithium-ion batteries (LIB) is an ideal solution to range anxiety, but this requires significant improvements to rate performance. The measurement of electrochemical kinetics in LIBs is central to understanding and optimizing their rate performance. However, while LIB rate performance is typically limited by the electrolyte, the behaviors of electrolytes during high-rate (dis)charge remain elusive to electrochemical characterization. It is commonly assumed that the effects of ion transport in the electrolyte are negligible in sufficiently thin electrodes (e.g., ≤ 5 μm), allowing measurement of the intrinsic electrochemical kinetics of LIB active materials (AM). On the contrary, we suggest that when the AM is not rate limiting (i.e., with nanosized AMs), electrochemical characterization of LIB electrodes reveals only the kinetics of the electrolyte, making the AM kinetics impossible to measure. In this work, we use LiFePO4 (LFP) as a model system to investigate the fundamental electrochemical kinetics of porous battery electrodes in which the electrolyte is the sole rate limiting component.We first synthesize a nanosized, carbon-coated LFP active material (LFP-C) and fabricate electrodes with both a practical composition (95% AM) and extremely low electronic resistance. Electrochemical impedance spectroscopy shows that charge-transfer resistance is immeasurably small compared to pore resistance, while cyclic voltammetry proves that semi-infinite Li+ diffusion in the electrolyte is the rate limiting process. We develop a novel pseudo-steady-state extrapolation (S3E) technique for quantification of charge-transfer kinetics in porous battery electrodes, which shows that LFP obeys Butler-Volmer kinetics, even at a current density of > 100 mA cm-2. Unexpectedly, we find that the apparent transfer coefficient of LFP-C is ~ 1.5, rather than the commonly assumed value of 0.5. Furthermore, the remarkably low electrode-level exchange current (< 1 mA cm-2) suggests that only a small fraction of particles (~ 1%) are actively reacting, even at extreme rates. These results show that a moving reaction front resulting from rapid interfacial kinetics is the root cause of the widely observed electrode-level reaction heterogeneity of phase-separating AMs. Using LFP three-electrode cells, we conducted rate performance testing on the electrolyte, comparing the conventional LP57 electrolyte to highly conductive 2M LiFSI in acetonitrile (LiFSI-AN). LiFSI-AN achieves up to 70% charge capacity at a 600C rate and 35% discharge capacity at a 3000C rate, exceeding all other reports of LIB rate performance. A novel resistance vs. capacity analysis of the rate performance data enables direct measurement of electrolyte resistance growth, revealing how the complex interaction between a moving reaction front (i.e., increase in conduction path length) and concentration polarization (i.e., decrease in electrolyte conductivity) controls electrolyte rate performance in LFP electrodes.Overall, this work shows that the electrochemical kinetics of nanosized AMs in porous battery electrodes can only be understood by explicitly accounting for the behaviors of the electrolyte. Consequently, the common a priori assumption that the effects of the electrolyte are negligible for electrochemical kinetic measurements in thin electrodes is shown to be, in many cases, fundamentally misleading. Nanosized AMs are, therefore, a powerful tool to investigate the electrochemical kinetics of electrolytes in porous battery electrodes. Particularly, all-LFP three-electrode cells were developed as an ideal platform for rate performance testing of electrolytes, enabling direct measurement of electrolyte resistance growth during high-rate battery operation.

The Effect of Asset Procurement Planning, Regulation, Asset Inventory, Information Systems and Human Resources Competence on Fixed Assets Optimization

This study examines the influence of asset procurement planning, regulations, asset inventory, information systems and human resource competence on the optimization of fixed assets (studies on asset administration at the Financial Services Authority). The research population is the employees of the Financial Services Authority. Determining the number of samples can be done by means of statistical calculations, namely by using the Slovin formula. This formula is used to determine the sample size of a known population, namely 3,134 Financial Services Authority employees. Furthermore, to perform data analysis, multiple linear regression through the use of SPSS software as a tool is used in this study. The results showed that (H1) was accepted with a significance value of 0.000 which is less than 0.050, needs planning and asset valuation had a positive and significant effect on asset optimization. (H2) is accepted with a significance value of 0.024 which is less than 0.050. An important aspect of asset optimization is the regulatory aspect. (H3) is accepted with a significance value of 0.000, which is less than 0.050. Asset inventory and legal audit have a positive and significant impact on the optimization of fixed assets. (H4) is accepted with a significance value of 0.000 which is less than 0.050, the management information system assists the process of managing finances and assets and reduces the risk of fraud from certain individuals. (H5) is accepted with a significance value of 0.003 which is less than 0.050, fixed asset management is weak human resource competence and lack of responsibility from related parties in managing fixed assets. So that competent human resources are needed in asset management

A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data

Optimization of ship energy efficiency is an efficient measure to decrease fuel usage and emissions in the shipping industry. The accurate prediction model of ship energy usage is the basis to achieve optimization of ship energy efficiency. This study investigates the sequential properties of the actual voyage data from a VLOC. On this basis, a model for predicting ship energy consumption is established by adopting a LSTM neural network that has better prediction performance for sequential datasets. To further enhance the performance of the established LSTM-based model, the network structures and hyperparameters are optimized by using Genetic Algorithm. Lastly, the application analysis is conducted to validate the established GA-LSTM-based model for ship fuel usage prediction. The established model for ship energy usage shows a significant improvement in prediction accuracy, compared to the original LSTM-based model. Meanwhile, the developed prediction model is more accurate than the existing BP, SVR, and ARIMA-based energy consumption models. The prediction errors for the ship's operational energy efficiency adopting the established GA-LSTM-based model can reach as low as 0.29%. Therefore, the established model can effectively predict the ship fuel usage under different conditions, which is essential for the optimization and improvement of ship energy efficiency.

Implementation of Multicultural Education Values in Senior High School

This research aims to describe and analyze the implementation of multicultural education values at Kharisma Bangsa Global Education High School in South Tangerang City and know the obstacles and challenges of implementing multicultural bathing there. This research uses a qualitative approach with this type of case study research. Data sources are obtained from primary data sources (Principals, PAI Teachers, Academic Staff, and Kharisma Bangsa Learners) and secondary data sources from several books and others that support primary data. Data collection techniques are used through observation, interviews, and documentation. Then from some of the data that has been obtained the author performs data analysis techniques. The results of this study show that 1). The implementation of multicultural education values at Kharisma Bangsa Global Education High School in South Tangerang City is the value of democracy, humanism, and tolerance, and the learning of multicultural Agama Islam (PAI) Education. These values are implemented through a whole school approach, dan 2). Obstacles and challenges in the implementation of multicultural educational values at Kharisma Bangsa High School are the adaptation of early learners into school, digital multicultural literacy, and awareness of understanding of multicultural education.

KAJIAN EKSPERIMENTAL PENGARUH PENGGUNAAN ELECTRONIC CONTROL UNIT AFTERMARKET PADA DAYA, TORSI, EMISI DAN KONSUMSI BAHAN BAKAR SEPEDA MOTOR 150CC

To improve motorbike performance, simple modifications are often applied using an aftermarket Electronic Control Unit (ECU). However, scientific studies on this particular topic are still limited. Therefore, the purpose of this research is to examine the effect of the aftermarket Juken 5 ECU on the power, torque, emissions, and fuel consumption of the Yamaha R15-V3 bike. The results of the dynamometer testing showed that the motorcycle with the Juken 5 ECU experienced an average increase of 8.09 ± 1.30% in power and 7.79 ± 1.31% in torque, respectively, at 5500 – 11000 rpm. However, the concentrations of Carbon Monoxide and Hydrocarbon emissions also increased by 170% and 360%, respectively. Particulate emission measurements also revealed that the Juken 5 ECU usage resulted in a significant increase in particulate counts. Compared to the standard ECU, motorcycles equipped with the Juken 5 ECU produced 89% and 145% more particulates in the size range of 0.3 – 0.374 μm and 0.374 – 2.685 μm, respectively. The performance and emission data analysis results indicated that the increase in power and torque achieved by the Juken 5 ECU is simply due to more fuel being injected into the combustion chamber. This finding was supported by the results of fuel consumption testing, where using the aftermarket ECU increased motorcycle fuel consumption by 63.6%.

The Effect of Customer Engagement on Purchase Intention in Kimia Farma Services in Surabaya

purchase intention in Kimia Farma services in Surabaya. Data were collected through a survey with 106 respondents. The data used in this study is primary data obtained from online questionnaires. The respondents of this research are members of Kimia Farma who shop in in Kimia Farma. The sampling technique used was non-probability sampling. This research is quantitative research with statistical testing using Structural Equation Modeling (SEM) with Partial Least Square (PLS) is used to perform data analysis. In this study it was found that there is a positive influence between customer engagement and perceived value. There is a positive influence between customer engagement and customer satisfaction. There is a positive influence between customer engagement and purchase intention. There is a positive relationship between perceived value and customer satisfaction. There is a positive influence between perceived value and purchase intention. There is a positive influence between customer satisfaction and purchase intention. There is a negative influence between customer uncertainty and purchase intention.

The Influence of AI-Powered Adaptive Learning Platforms on Student Performance in Chinese Classrooms

This study delved into the impact of AI-powered adaptive learning platforms on student performance within the context of Chinese classrooms. Utilizing a comprehensive retrospective approach, the research examined the effects of these innovative platforms on academic achievements, engagement levels, and overall learning outcomes. In the past decade, the integration of artificial intelligence (AI) into educational technology has revolutionized traditional teaching methods. Adaptive learning platforms, driven by AI algorithms, customize learning experiences to cater to individual student needs and learning paces. This study examined a diverse range of Chinese classrooms, encompassing various grade levels and subjects, to ascertain the widespread influence of such platforms. Data collection involved the analysis of historical student performance data from multiple academic years. The study assessed both quantitative metrics, such as test scores and grade improvements, and qualitative indicators, including student feedback and engagement patterns. Through rigorous statistical analyses, the study established correlations between the utilization of AI-powered adaptive learning platforms and enhanced student performance. The findings revealed a significant positive association between the integration of AI-powered platforms and student outcomes. Students exposed to personalized learning experiences exhibited improved academic achievements compared to those in traditional classrooms. Furthermore, the study identified heightened levels of engagement and motivation, as the adaptability of these platforms catered to individual learning preferences. In conclusion, this research underscores the transformative role of AI-powered adaptive learning platforms in Chinese classrooms, highlighting their ability to elevate student performance and engagement. As China continues to embrace technological advancements in education, this study offers valuable insights into the evolution of pedagogical approaches and their positive outcomes on student learning in the past. Keywords: AI-powered, Adaptive learning platforms, Student performance, Chinese classrooms, Influence

Role of Public Procurement Preference Scheme on the Growth of Small and Medium-sized Enterprises in Nyamira County, Kenya

The focus of the study is to investigate the role of public procurement preference schemes in enhancing the growth of small and medium-sized enterprises. The general objective of the study was: To investigate the role of public procurement preference schemes in enhancing the growth of small and medium-sized enterprises in Nyamira County, Kenya. The specific objectives included; establishing the role of 30% preference on the growth of small and medium-sized enterprises, to investigate the role of waiver of bid bond on the growth of small and medium-sized enterprises, to establish the role of local firm preference on the growth of small and medium-sized enterprises and to assess the role of easy access to procurement opportunities registration on the growth of small and medium-sized enterprises. The study was guided by agency theory. A descriptive research design was applied to the study. The study population included 35700 registered and licensed SMEs, from where a sample of 391 was arrived at by Yamane’s formula. Data collection involved the use of self-administered questionnaires. Both descriptive and inferential statistics were used to perform data analysis. The results of the model summary indicate that R2 equals 0.498 at a 0.05 significance level, thus revealing that 49.8 % of the SME growth can be accounted for by the public procurement preference scheme. The study concluded that there was a positive influence of public procurement preference schemes on the growth of small and medium-sized enterprises. The study recommended that SMEs may team up with one another under joint bidding in order to get higher chances of preference and also compete with large-sized firms for public procurement opportunities

IIoT based framework for data communication and prediction using augmented reality for legacy machine artifacts

Machine data automation, process feedback, and visualization are critical elements in monitoring and improving system performance. Legacy machines constitute a large part of the factory floor assets, especially in small and medium enterprises (SMEs). Digitalization enables them to seamlessly connect within the smart factory framework to exchange product and process information. This paper extends our work on extracting digitized data from legacy machines using computer vision in the Industrial Internet of things (IIoT) environment. The proposed framework includes data communication through the cloud for process automation, performing data analytics, interpretation, and visualization using Augmented Reality (AR) based smart devices through human-in-the-loop feedback. Real-time process recommendations are provided on an AR app by implementing a Bayesian Network to enable the user to input desired quality metrics. A case study of capturing injection molding data represented through different process parameters is presented to evaluate the performance. Further, its extension to using other machine learning (ML) models for process feedback, the latency of real-time data presented, communication with different IoT protocols, and authentication are discussed. This AR-based Digital Thread framework has promising applications for visualizing machine process parameters and performance, real-time feedback, troubleshooting, and maintenance activities for legacy and smart machines.

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.

Pembangunan Web Modul Prestasi Sebagai Alat Analisis Data Prestasi

Achievement is one of the critical aspects in the world of education that must be considered continuously. To improve the quality of education and make the right decision, a tool is needed that can analyze achievement data more efficiently. This study aims to develop an achievement web module that functions as a comprehensive achievement data analysis tool. The development of the achievement module web is carried out using a needs-based development approach and active participation from various related parties, such as teachers, lecturers, and administrative staff. This module is designed with an intuitive and responsive interface, allowing users to easily access and analyze performance data in real-time. The developed achievement module presents academic and non-academic achievement data visually in the form of graphs, tables and reports. Complete data analysis features allow users to identify achievement trends, patterns of increase or decrease in achievement, as well as factors that influence student or participant performance. The results showed that the achievement web module has increased the efficiency of achievement data analysis, provided more in-depth information, and supported more informed decision making. The use of this module has assisted related parties in formulating strategies and intervention programs aimed at improving overall performance.