Data Processing Research Articles

THE INFLUENCE OF SOCIAL MEDIA MARKETING AND BRAND IMAGE ON PURCHASING DECISIONS ON ERIGO STORE PRODUCTS

This study aims to analyze the influence of social media marketing factors (X1) and brand image factors (X2) on purchasing decisions (Y) for Erigo Store consumers in Tangerang City. This research is a quantitative research and survey research. The samples taken in this study were 100 respondents. Data obtained from distributing questionnaires. Primary data processing uses ms.excel and Statistical Package For Social Science (SPSS) version 25 as evaluation. While the data analysis used in this study includes: descriptive statistical analysis, classical assumption test, linear regression analysis, correlation analysis, coefficient of determination test and hypothesis testing. The results of this study indicate that the Social Media Marketing Factor (X1) and Brand Image Factor (X2) simultaneously have a positive and significant effect on Purchase Decision (Y) indicated by the Fcount>Ftable value of 319.453 > 3.09 with a significant value of 0.000 < 0.05..

Joint state and process inputs estimation for state-space models with Student’s t-distribution

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student’s t-distribution. The identification of UIs is accomplished through the recursive expectation–maximization (REM) approach. Specifically, in the E-step, a recursively calculated Q-function is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

The Effect of Extracurricular Education on the Motor Activity of School Children

Developing the population's physical activity (especially children and adolescents) is an integral part of the international strategy "Health for all in the XXI century". The purpose of our research is aimed at the development of children's motor activity, which is an integral part of the nation's health. Methods. 28 students aged 8-12 took part in the study (14 children who were engaged in go-karting - boys - 10 people and 4 girls and 14 children who were engaged in beadwork - of them 2 boys and 12 girls). The level of physical condition was assessed with the help of G. L. Apanasenko's express-diagnosis system, and the cognitive sphere of children (memory, attention, thinking) was studied according to L. Vygotskyi. Statistical methods were used for data processing and verification in the Statistica 6.0 program. During the research, it was established that in practice (karting and bead weaving) both mechanisms (educational/developmental and physical activity) are involved. In other words, moderate or high physical activity involving complex movements or rules will positively affect cognitive development. Conclusion. We found that children are influenced not only by sports but also by clubs that are part of the activities of extracurricular education institutions.

Decision Making on The Organoleptic Quality of Salted Egg Products using Analytical Hierarchy Process and Simple Additive Weighting

Chicken and duck eggs are highly nutritious food products that are popular among the Indonesian community. However, their perishable nature leads to a decline in quality if not further processed. The salting process is a simple preservation method that can also enhance the flavor of salted egg products. To produce salted egg products with high organoleptic quality preference, a combination of Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) is implemented as a quality decision-making method based on: (1) the experts viewpoints on the importance level of organoleptic criteria, and (2) the scores of organoleptic tests for the criteria of texture, taste, and appearance. In this study, chicken and duck eggs were processed into salted eggs using a salting mixture made of a blend of scouring ash and table salt with a mass ratio of 5:4 and 5:6, resulting in product combinations coded as CE-5/4, CE-5/6, DE-5/4, and DE-5/6. Data processing results on the importance level from the experts viewpoints using the AHP method resulted that the criteria for texture, taste, and aroma had final priority weights of 0.244, 0.617, and 0.139, respectively. Final decision-making using the SAW method indicated that the highest to lowest final preference scores were obtained by products CE-5/4, DE-5/4, DE-5/6, and CE-5/6, with the final preference scores of 0.979, 0.963, 0.931, and 0.906, respectively. The results demonstrated that AHP and SAW were successfully implemented to assist in making decisions regarding the quality of salted egg products based on their organoleptic characteristics. Keywords: salted egg, organoleptic, decision-making, AHP, SAW

Analysis of Allocative Efficiency of Watermelon Farming in Kapuas Regency, Central Kalimantan Province

The objective of this research is to analyze the costs, revenues, and profits of watermelon farming, to examine the impact of the use of production factors in watermelon farming, and to evaluate the allocative efficiency level of watermelon farming in Kapuas Regency, Central Kalimantan Province. The population of this research consists of 113 farmers, from which 55 were selected using simple random sampling techniques. The data processing method used to analyze the costs, revenues, and profits of watermelon farming includes using a multiple linear regression model, specifically the Cobb-Douglas function type. Additionally, to analyze the allocative efficiency of production factors used by farmers in watermelon farming, the comparison of a production factor’s Marginal Product Value (MPVx) with the price of the production factor (Px) is set to be equal to 1. Based on the results of the research and data processing, the average revenue obtained from watermelon farming in Kapuas Regency is IDR 75,619,181.82 per farm, and the average profit earned by watermelon farmers is IDR 45,541,259 per farm. The statistical test results of the Cobb-Douglas production function show that collectively (simultaneously), the production factors (land area, number of seeds, organic fertilizer, inorganic fertilizer, pesticides, and labor) significantly affect watermelon production. However, the partial test results indicate that the production factors of land area, seeds, organic fertilizer, inorganic fertilizer, and labor significantly affect watermelon production, except for the production factor of pesticides. The statistical test results for allocative efficiency (price efficiency) show that three production factors, namely organic fertilizer, labor, and pesticides, are used efficiently, meaning the marginal product equals the price of the production factor. The use of land area, seeds, and inorganic fertilizer is not yet efficient and needs to be increased to reach an efficient point.

A Missing Link on Semantic Interoperability.

Syntactic interoperability among health ICT systems is well-established, but achieving semantic interoperability requires more than just exchanging codes. We present a pragmatic, safe, and effective approach towards an ambitious goal: enabling any software to process a critical mass of routine clinical information in a replicable manner across various systems and local contexts. We advocate for the use of reliable, parsimonious coding to handle the most replicable aspects of data processing for routine patient information, while reserving the intricate interpretation of individual patient data nuances for skilled professionals, possibly supported by Artificial Intelligence tools. We suggest coping with routine tasks by focusing on a limited set of a few thousand data elements, named the 'Clinical Documentation Kernel' (CDK). This approach will provide direct benefits to users and assist in the human interpretation of other patient information. Our preliminary study focuses on the 'primitives' and 'qualifiers' that bring the highest value to the health ecosystem in various authoritative scenarios in the field of diabetes.

Pengaruh DER, Roa, NPM dan EPS Terhadap Underpricing Studi Kasus: Perusahaan Yang Melakukan Initial Public Offering di BEI

This study aims to look at the effect of each variable (DER, ROA, NPM, EPS) on underpricing in companies conducting an initial public offering on the Stock Exchange in 2016-2018. Testing is done by using a sample of 31 companies that conducted an initial public offering listed on the Indonesia Stock Exchange in 2016 -2018, using purposive sampling method. Data processing methods use SPSS with descriptive tests, classic assumption tests, and in the measurement of hypotheses using multiple regression analysis. The results in this study address the influence of ROA and EPS on underpricing, while DER and NPM have no effect on underpricing. It is expected that this research can be used as a material consideration for investors in analyzing financial statements that will be used as a reference in investing, while for academics can add knowledge related to the influence of several fundamental factors on underpricing.

Microwave photonics frequency scanning approach to absolute distance measurement

An absolute distance measurement approach based on microwave photonics is proposed to provide a high-resolution and non-ambiguity range measurement solution by microwave frequency scanning. The microwave interference spectrum containing optical path difference information can be generated through two rounds of intensity modulation. The approach combines the characteristics of easy focusing and collimation of light waves, as well as easy manipulation of microwaves, to achieve accurate absolute distance measurement. The experimental results show that high-precision measurements can be achieved within the frequency scanning range of only 2 GHz. The error of the distance measurement is less than ±0.079 mm. In addition, the effectiveness of the scanning pattern and the data processing algorithm are verified.

An edge computing and distributed ledger technology architecture for secure and efficient transportation

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture’s effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization.

Integrating human-centric automation and sustainability through the NAToRM framework: A neuromorphic computing approach for resilient industry 5.0 supply chains

Industry 5.0 supply chains face critical challenges in effectively managing the rapidly growing volume, variety, velocity, and veracity of big data while simultaneously ensuring sustainability, privacy, and ethical practices. The complex and interconnected nature of modern supply networks and the swift adoption of advanced technologies have created an urgent need for innovative frameworks to navigate these multifaceted challenges. Existing approaches often fail to adequately address the unique demands of Industry 5.0, lacking the ability to process data in real time, uncover deep insights, and enable dynamic, risk-informed decision-making. Moreover, there is a pressing need for frameworks that emphasize interdisciplinary collaboration and proactively address the potential negative impacts of emerging technologies. This paper introduces a novel, multidisciplinary framework that integrates cutting-edge techniques to tackle these challenges head-on, paving the way for more resilient, intelligent, and adaptable supply chains in the Industry 5.0 era.

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingredient for the inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process. Combining recent martingale central limit theorem with the new concentration inequality, we then characterize the convergence rate of the test statistics in a continuous time domain. Finally, to account for potential non-stationarity of the process in practice, we extend our statistical inference procedure to a flexible class of Hawkes processes with time-varying background intensities and unknown transition functions. The finite sample validity of the inferential tools is illustrated via extensive simulations and further applied to a neuron spike train data set.

Edge–cloud collaborative estimation lithium-ion battery SOH based on MEWOA-VMD and Transformer

The State of Health (SOH) of lithium-ion batteries significantly impacts the performance, safety, and reliability of the battery, making it a crucial component of the battery management system. Addressing the issues of inadequate accuracy and lack of robustness in current SOH estimation methods, this study introduces a novel methodology for estimating SOH in lithium-ion batteries. It leverages the multi-population evolution whale optimization algorithm optimized variational mode decomposition (MEWOA-VMD) in conjunction with Transformer architecture. This framework enhances the efficiency and accuracy of SOH estimation by leveraging the computational capabilities of edge devices for real-time data processing, as well as the robust data processing power and model training advantages offered by cloud computing. Specifically, MEWOA is utilized to optimize VMD parameters, enabling MEWOA-VMD to fully decompose the capacity signal of lithium-ion batteries. This results in a component showing a global attenuation trend and a set of fluctuating components that represent capacity regeneration, thereby minimizing the impact of capacity regeneration on SOH estimation. Subsequently, all components are collectively input into the Transformer, marking the first application of this method for input. To enhance convergence speed and training efficiency, the layer normalization (LN) layer within the neural network architecture is proactively advanced. Finally, various artificial neural networks are compared and validated on three publicly available datasets. Furthermore, Gaussian noise is introduced into the original data to validate robustness. To confirm the practical applicability of the proposed method, real-world vehicle data is used for SOH estimation. The results indicate that the proposed method achieves a maximum MSE of no more than 0.009% across three publicly available datasets, showcasing improved accuracy and stability in SOH estimation. The practical applicability is further validated using real-world vehicle data, proving the proposed method’s potential for application in edge cloud-based battery management systems.

Preparedness of Lab School Middle School Students Towards Earthquakes for Disaster Risk Reduction

The primary objective of this research is to observe and analyze the preparedness of Labschool Middle School students in Palu City for Disaster Risk Reduction. The population in this study consisted of all students at Palu City Labschool Middle School, using a total sampling technique, resulting in a sample size of 100 students. Data was collected using structured questionnaires that were directly filled out by the students. The questionnaires were designed to cover all aspects of the research variables and provide a comprehensive assessment of the student's preparedness. Data processing in this study utilized a computerized system via MS Excel. Knowledge: 95% of respondents rated their knowledge on the subject as not good. This indicates that the majority of respondents have a poor understanding of the topic. Early Warning System: 57% of respondents rated the early warning system as not good. Resource Mobilization: 73% of respondents rated resource mobilization as not good. This indicates significant issues in resource mobilization, with the majority of respondents feeling that improvements are needed in this area. The findings indicate a significant gap in disaster preparedness among Labschool Middle School students. The majority lack basic knowledge about natural disasters and have minimal exposure to disaster-related training or education. Despite the availability of disaster warning tools in schools, awareness and understanding of their use remain limited. An effective early warning system is crucial for community safety, yet many students exhibit low awareness and preparedness levels

Robust Left-Right Hashing Scheme for Ubiquitous Computing

Ubiquitous computing systems possess the capability to collect and process data, which is subsequently shared with other devices. These systems encounter resource challenges such as memory constraints, processor speed limitations, power consumption considerations, and the availability of data storage. Therefore, maintaining data access and query processing speed in ubiquitous computing is challenging. Hashing is crucial to search operations and has caught the interest of many researchers. Several hashing techniques have been proposed and Cuckoo Hashing is found efficient to use in several applications. There are two variants of Cuckoo Hashing: Parallel Cuckoo Hashing and Sequential Cuckoo Hashing. Cuckoo Hashing suffers from challenges like high insertion latency, inefficient memory usage, and data migration. This paper proposes two hashing schemes: Left-Right Hashing and Robust Left-Right Hashing that successfully address and solve the major challenges of Sequential Cuckoo Hashing. The proposed schemes adopt the Combinatorial Hashing technique after modification and use this with a new collision resolution technique called Left-Right Random Probing. Left-Right Random Probing is a variant of random probing and uses prime numbers and Fibonacci numbers. In addition, this paper proposes a new performance indicator, degree of dexterity to estimate the performance of hashing techniques. Sequential Cuckoo Hashing suffers from hidden switching costs which are identified and its estimation is given by a new performance indicator called, T.R.C./Key. Performance of Sequential Cuckoo Hashing is order dependent.

Low probability of interception radar overlapping signal modulation recognition based on an improved you-only-look-once version 8 network

Low probability of interception (LPI) radar is widely used in modern electronic warfare. With the increased radiation sources, multiple signals will arrive simultaneously. The traditional feature extraction method has too many features, which brings great trouble to the subsequent data processing. Most modulation recognition methods based on deep learning only consider the single signal after preprocessing, and the generalization ability is weak. This paper proposes a deep learning solution based on an improved you only look once version 8 (YOLOv8) network with a global attention mechanism (GAM), achieving recognition accuracy over 98% in a -10 dB signal-to-noise ratio (SNR) scenario to address the above problems. We improved the performance of the original network, which can be used in electronic countermeasures.

Probe the Regolith Characteristics of Asteroids from 9 yr Infrared Observations of WISE/NEOWISE: A Case Study of The Main-belt Object (656) Beagle

This work presents the data processing, fitting procedure, modeling, and analyzing of 9 yr infrared light curves provided by the WISE/NEOWISE telescope, by which the regolith characteristics of Main-belt Object (656) Beagle is studied. Beagle locates in the Themis family, and was proposed to be the parent of the first main-belt comet (MBC) 133P. We determine Beagle’s effective diameter Deff=57.3−2.2+4.5 km, geometric albedo pv=0.05−0.007+0.004 , mean roughness θ RMS = 44 ± 4°, mean grain size b=100−90+350μm , mean specific heat capacity c p = 173 ∼ 516 J Kg−1 K−1, mean thermal conductivity κ = 0.7 ∼ 1.3 × 10−3 W m−1 K−1, and mean thermal inertia Γ = 14 ∼ 32 J m−2 s−0.5 K−1. The albedo of Beagle is a little anomalous that the albedos of Beagle’s neighboring asteroids are more close to (24) Themis, rather than Beagle itself. The W1-band near-infrared (NIR) light curves do not reveal significant heterogeneous NIR features on the surface of Beagle, being inconsistent with the expectation of a family parent that has members with diverse NIR spectral types. These results add new clues of Beagle probably being an interloper or a sister, rather than the parent of its neighboring asteroids including the famous MBC 133P, hence may lead to new scenarios about the origin of 133P. Besides, we found that asteroidal shape models from inversion of optical light curves are imperfect for modeling infrared lightcurves, thus could mislead evaluations of both the heterogeneity of regolith reflectivity at NIR and thermophysical characteristics at thermal infrared.

Students Mental Readiness for Entrepreneurship at SMK Negeri 6 Padang

This research was motivated by the large number of students at SMK Negeri 6 Padang who graduated but faced difficulties in finding work or were even unemployed. This is due to a lack of desire to start a business. Students feel less confident in starting a business. The aim of this research is to describe the mental readiness of students for entrepreneurship at SMK Negeri 6 Padang. This type of research is quantitative with a descriptive approach. The population in this study were students at SMK Negeri 6 Padang, totaling 303 students. The sample consisted of 75 students. Sampling used the Proportional Random Sampling technique. The data collection technique uses questionnaires via Google Form which have been tested for validity and reliability. The data analysis technique was carried out using descriptive analysis. Data processing was carried out with the help of Microsoft Excel and the SPSS version 25 software program. First determine the frequency distribution, determine the percentage level and classification techniques. The research results showed that 26 students' mental readiness for entrepreneurship (35%) was in the medium category.

Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data

Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as “observed GPP”) retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m−2 d−1) and 0.83 (1.49 gC m−2 d−1), respectively, which increased (decreased) by 0.17–0.03 (0.58–0.12 gC m−2 d−1) and 0.11–0.02 (0.39–0.10 gC m−2 d−1) compared with the R2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R2 by 0.33 and decreasing RMSE by 0.97 gC m−2 d−1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.

Analisis Pemanfaatan Pinjaman Dana Bergulir Program Kota Tanpa Kumuh di Kecamatan Gayungan Kota Surabaya

This research is a quantitative study with data collection techniques using a questionnaire. The population used was all members of the self help group in Gayungan District, Surabaya City as many as 95 people. This study uses a sample with the census method. The census sample is the determination of the sample if all members of the population are used as the sample (Sugiyono, 2012). The data processing technique uses Partial Least Square (PLS) analysis, Outer Model, Inner Model, and Hypothesis Testing. The results of this study concluded: 1)Financial Literacy can contribute to the Utilization of Revolving Fund Loans; 2) Financial Experience can contribute to the Utilization of Revolving Fund Loans; 3) Income can contribute to the Utilization of Revolving Fund Loans; 4) Attitude of Managers Finance as a moderation of the Financial Literacy variable cannot contribute to the Utilization of Revolving Loans; 5) The attitude of the Financial Managers as a moderation of the Financial Experience variable cannot contribute to the Utilization of Revolving Loans; dan 6) The attitude of the Financial Manager as a moderation of the Income variable doesn’t contribute on the Utilization of Revolving Loans.

The Views of Medical Students On The Educational Processes In Pre-Clinical Period

The aim of the research is to reveal the opinions of medical students about the education process in the pre-clinical period in order to determine their educational needs. For this purpose, the problem of the research is; “What are the thoughts of medical faculty students about the education and training process before the clinical period?” It is defined as. A qualitative research design was adopted in the research and the case study method was used. For this purpose, third-year students studying at XXX University Faculty of Medicine in 2022-2023 constitute the sample of the research. Convenient sampling method was used to determine the students participating in the research. In addition to having experience in pre-clinical medical education; In-depth interviews were held with students who attended the courses determined by the "Circulatory and Respiratory Systems" committee, which provides the most methodological diversity possible in educational activities such as educational presentations, panels, laboratories, basic medical practices. In in-depth interviews, semi-structured interview technique was used to reveal the medical students’ views on their education processes. Descriptive analysis, one of the qualitative data analysis types, was used in the research. Descriptive analysis was carried out in four stages; creating a framework for descriptive analysis, processing of data according to the thematic framework, description of findings and interpretation of findings. The findings that students thought would have a positive impact on their professional lives were basic medical practices and case-by-case discussions. Non-standardized basic medical practices, limitations in measurement and evaluation, and inadequate mentoring were expressed by students as negative educational activities.