Data Processing Problems Research Articles

Intelligent processing of UAV remote sensing data for building high-precision DEMs in complex terrain: A case study of Loess Plateau in China

The Loess Plateau in China is renowned for its dense gullies and complex terrain, with drastic changes primarily due to soil erosion and human activities, significantly affecting the evolution of the ecological environment. The complex terrains and dense vegetation make precise terrain measurement and modeling challenging. Although the development of Unmanned Aerial Vehicle (UAV) light detection and ranging (LiDAR) scanning and photogrammetry technologies has improved data acquisition precision, relying solely on one remote sensing technology struggles with accurately extracting bare earth information. This study adopted a method that fuses UAV lidar scanning with aerial photogrammetric imagery, generating detailed lidar point cloud data that includes coordinate, reflectance, true color, and texture information to enhance data classifiability and interpretability. Subsequently, a point cloud classification model based on the Transformer architecture (Stratified Transformer) is introduced to intelligently complete the initial ground point cloud extraction in complex gully terrains. Further, to address residual non-ground noise in the initial ground point clouds, a new point cloud classification optimization algorithm (MDD, Multi-scale C2M Distance Difference) is proposed. This algorithm, based on the characteristics of discrete and non-continuous with the ground surface of the noisy point clouds, effectively eliminates the discrete noisy point clouds by analyzing the distances between the point clouds and TINs (Triangular Irregular Networks) of different scales and their differences. This study effectively addresses the technical challenges of ground point cloud extraction in the mixed environment of complex terrain and vegetation, solving the problem of precise terrain measurement and intelligent data processing in complex gully terrains, and offering new technical pathways for detecting geomorphological changes.

Artificial intelligence detection system based on multi-sensor and wireless communication

In today’s world, Internet technology and wireless communication technology are becoming more and more mature, which has brought massive network information resources to all sectors of society. At the same time, the phenomenon of data loss caused by illegal network intrusion is becoming more and more common. Therefore, it is necessary to identify and deal with them in combination with IDS (intrusion detection system). The problem of data processing is also very important. Enterprises can build a data management system according to their own needs, and use this system to process data. With the help of science and technology, AI (Artificial Intelligence) technology has become more mature and applied in many industries. Therefore, this paper proposed to build an AI IDS, and combined the deep RL (reinforcement learning) algorithm to analyze the performance of the system. This paper tested and analyzed the system from the aspects of precision and recall. The experimental results showed that the average precision of the five data sets was 94.76%, and the average recall rate was 91.4%. From the above data, combined with the algorithm in this paper, the precision and recall of the system have been significantly improved. This paper also conducted benchmark energy consumption comparison experiments for different cloud data management systems. The results showed that in terms of loading, the benchmark energy consumption of HBase was the lowest, which was 86KJ. In terms of query, the benchmark energy consumption of GridSQL was the lowest, which was 56KJ. It can be seen that different systems have their own advantages in the benchmark energy consumption of loading and query.

Use of Creative Frameworks in Health Care to Solve Data and Information Problems: Scoping Review.

Digitization is vital for data management, especially in health care. However, problems still hinder health care stakeholders in their daily work while collecting, processing, and providing health data or information. Data are missing, incorrect, cannot be collected, or information is inadequately presented. These problems can be seen as data or information problems. A proven way to elicit requirements for (software) systems is by using creative frameworks (eg, user-centered design, design thinking, lean UX [user experience], or service design) or creative methods (eg, mind mapping, storyboarding, 6 thinking hats, or interaction room). However, to what extent they are used to solve data or information-related problems in health care is unclear. The primary objective of this scoping review is to investigate the use of creative frameworks in addressing data and information problems in health care. Following JBI guidelines and the PRISMA-ScR framework, this paper analyzes selected papers, answering whether creative frameworks addressed health care data or information problems. Focusing on data problems (elicitation or collection, processing) and information problems (provision or visualization), the review examined German and English papers published between 2018 and 2022 using keywords related to "data," "design," and "user-centered." The database SCOPUS was used. Of the 898 query results, only 23 papers described a data or information problem and a creative method to solve it. These were included in the follow-up analysis and divided into different problem categories: data collection (n=7), data processing (n=1), information visualization (n=11), and mixed problems meaning data and information problem present (n=4). The analysis showed that most identified problems fall into the information visualization category. This could indicate that creative frameworks are particularly suitable for solving information or visualization problems and less for other, more abstract areas such as data problems. The results also showed that most researchers applied a creative framework after they knew what specific (data or information) problem they had (n=21). Only a minority chose a creative framework to identify a problem and realize it was a data or information problem (n=2). In response to these findings, the paper discusses the need for a new approach that addresses health care data and information challenges by promoting collaboration, iterative feedback, and user-centered development. Although the potential of creative frameworks is undisputed, applying these in solving data and information problems is a minority. To harness this potential, a suitable method needs to be developed to support health care system stakeholders. This method could be the User-Centered Data Approach.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Abstract. Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.

Analysis of the effectiveness of clustering algorithms for multimodal samples using computer simulation of an educational experiment

Introduction. The article is devoted to the problem of primary data processing of pedagogical experiments having a multimodal character. The purpose of the study is to identify the most effective and universal clustering algorithms for pedagogical experiments. Materials and Methods. The study used the method of modeling a pedagogical experiment. The analysis of 5 clustering algorithms is conducted. The effectiveness of clustering algorithms was evaluated based on the proportion of observations with clustering errors at various tolerance levels and the Jacquard similarity coefficient. Regression analysis was used to assess the influence of modeling parameters of a pedagogical experiment and indicators of descriptive statistics on the effectiveness of clustering algorithms. Results. The assessment of the effectiveness of various data clustering algorithms is provided, as well as a correlation and regression analysis of factors affecting clustering efficiency indicators was carried out. Conclusions. The most effective clustering algorithms for multimodal samples include the K-means algorithm and the agglomerative hierarchical algorithm. The results obtained in this research can be used for statistical analysis of pedagogical, psychological, sociological, biological and medical research data.

Application of a data stratification approach in computer medical monitoring systems

The research object is the processes occurring in the data stratification subsystem in the medical monitoring computer system, which is part of the decision support system. Such a subsystem aims to solve data analysis and processing problems in the medical monitoring system. Among them, the problems of anomaly detection, data marking, state determination, selection of the most informative variables, and justification of decision-making are selected for solving. The paper proposes the structure and implementation of the data stratification subsystem in the decision support system. The subsystem contains modules for anomaly detection and an autoencoder, a clustering module using an advanced multi-agent clustering method, and a state detection module with a modified neural network training procedure. Modules of the stratification subsystem have been tested using diabetes monitoring data. The results showed that the clustering module provides 25.7 % lower accuracy than the achieved neural network. The accuracy difference is explained by the complexity of the data and the lack of adaptability of the proposed method to solving such problems. It is shown that the method of determining the overall informativeness of variables covers 90 % informativeness with 10 variables, comparable to the variability data. In general, the flexible nature of the proposed stratification subsystem allows for solving the problems. The proposed stratification subsystem offers a robust solution for improving treatment strategies and decision-making in computer medical monitoring systems. Its versatility allows it to be used in any system where decision support is needed, providing valuable information about informative variables and decision-making features for clinicians and researchers

A hybrid complex spectral conjugate gradient learning algorithm for complex-valued data processing

Complex-valued neural networks (CVNNs) have become a powerful modelling tool for complex-valued data processing. Because most of the critical points of CVNNs are saddle points, the gradient-based learning algorithms for CVNNs enjoy more chances to reach the global minima while suffering from slow convergence. To this end, we propose a hybrid complex spectral conjugate gradient learning algorithm for fast training CVNNs in this paper. The proposed algorithm combines the scaled negative gradient with a Barzilai–Borwein stepsize and an optimized conjugate term to define a new training direction, thus providing an accurate approximation of the second-order curvature of the objective function. The complex Wolfe conditions are employed to adaptively determine the optimal training stepsize. Under mild conditions, the descent property of the training direction and the convergence of the proposed algorithm are theoretically established. Simulation results on a number of benchmark complex-valued data processing problems demonstrate the efficiency of the proposed algorithm.

Low-frequency Radio Interferometric Array Imaging Pipeline Optimization Based on Distributed Execution Framework

The Square Kilometre Array (SKA) project is an international collaboration to build the world’s largest radio telescope, whose sensitivity and measurement speed will be an order of magnitude higher than those of all current radio telescopes. Radio continuum survey is one of the main observation mode of the SKA, and the establishment of a standard map of the survey area based on continuum imaging will provide an important foundation for subsequent astronomical science. The GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) is a project of the SKA pilot telescope Murchison Widefield Array (MWA) in 2018—2020. GLEAM-X is a new radio continuum survey project to be carried out with the MWA Phase II expansion array in 2018—2020. The experience of optimizing the imaging pipeline based on the distributed execution framework will help to solve the problem of massive data processing. In this paper, we describe the process steps of GLEAM-X imaging pipeline, integrate and improve it, and realize parallel processing of multiple pipelines on the China SKA Regional Centre Prototype (CSRC-P), and verify the deployment and test the correctness of the imaging pipeline system using GLEAM-X observation data. The GLEAM-X observations were used to validate the deployment of the imaging pipeline system and test its correctness. Then, to optimize the pipelines and improve the processing efficiency, the Data Activated Liu Graph Engine (DALiuGE) was used to integrate the imaging pipelines into the DALiuGE execution framework to automate the distributed parallel processing of the pipelines. Performance tests and results’ analysis show that the optimized imaging pipeline based on the DALiuGE execution framework has better performance, more flexible adaptability, and scalability than the traditional parallel approach, and can support future large-scale continuum imaging experiments during the first phase of SKA commissioning.

Tracking illegal activities using video surveillance systems: a review of the current state of research

The current state of research on the use of the neural networks under martial law to identify offenders committing illegal acts, prevent acts of terrorism, combat sabotage groups in cities, track weapons and control traffic is considered. The methods of detecting illegal actions, weapons, face recognition and traffic violations using video surveillance cameras are analysed. It is proposed to introduce the studied methods into the work of “smart” video surveillance systems in Ukrainian settlements.
 The most effective means of reducing the number of offences is the inevitability of legal liability for offences, so many efforts in law enforcement are aimed at preventing offences. Along with public order policing by patrol police, video surveillance is an effective way to prevent illegal activities in society. Increasing the coverage area of cameras and their number helps to ensure public safety in the area where they are used. However, an increase in the number of cameras creates another problem which is the large amount of video data that needs to be processed. To solve the problem of video data processing, various methods are used, the most modern of which is the use of artificial intelligence to filter a large amount of data from video cameras and the application of various video processing algorithms. The ability to simultaneously process video data from many CCTV cameras without human intervention not only contributes to public safety, but also improves the work of patrol police. The introduction of smart video surveillance systems allows monitoring the situation in public places around the clock, even if there is no police presence in the area.
 In the reviewed studies of video surveillance systems, neural networks, in particular MobileNet V2, YOLO, mYOLOv4-tiny, are used to track illegal actions, criminals and weapons, which are trained on large amounts of video and photo data. It has been found that although neural networks used to require a lot of computing power, they can now be used in IoT systems and smartphones, and this contributes to the fact that more video surveillance devices can be used to monitor the situation.

SISTEM INFORMASI PENJUALAN CAT BERBASIS MULTIUSER

Growth of computer technology which is fast to progressively give amenities for human being in executing life activity, this matter result usage of computer is alternative taken by various institution because assessed more giving advantages compared to existing loss. Computer are appliance assist to improve performance various area. In finishing the problem of data processing in CV. Karunia Abadi, pursuant to execution of [done/conducted] survey, writer try to alter data-processing program by manual with computerization program.. This change aim gives freshment in the running of him. With computer program in data input still need time old ones and if for the seeking of data also still have to write one by one since it cannot by automatic and presentation which must be sent to leader still walk tardy so that cannot be presented anytime,because of this existence problem the writer try to formulate problems that is " How to make data-processing program of sale by cash and automatically can give report at any times at] leader in CV.Karunia Abadi using program of Microsoft Fox Pros 9.0". For data-processing process of sale can be accessed swiftly, and precisely and also give more drawing appearance. In finishing the problems [of] writer try to collect data - data which is connect with cash sale data. In scheme of system which is just [done/conducted] [by] some steps cover scheme of data. Making of tables, making of ERD. In data processing of the sale expected can assist problems of arising out in CV. Karunia Abadi. Endless Grant from above and made program it is of course still has some insuffiency and weakness, this matter is enabled [by] because still a few/little knowledge and experience of writer in problem of program. Suggestion and criticism very assist writer to be more develop knowledge in the field of this

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

Multiuser Based Savings And Loans Information System

The increasingly rapid development of computer technology provides convenience for humans in carrying out life activities, this has resulted in the use of computers being an alternative taken by various agencies because it is considered to provide more advantages than disadvantages. Computers are tools to improve performance in various fields. In solving data processing problems at the KSU " DANA MANDIRI " Cooperative, based on the survey carried out, the author tried to change the manual or conventional data processing program into a computer-based data processing program, namely by using the Visual Foxpro 9.0 program. This change aims to provide comfort for operators in carrying out their work. With old programs, conventionally inputting data still takes a long time and when searching for data you still have to look for codes one by one because it cannot be done automatically. With these problems the author tries to formulate the problem, namely "How to make a data processing program to save borrow at KSU " DANA MANDIRI " using the Visual Foxpro 9.0 programming language. The savings and loan data processing process can be accessed quickly and precisely and provides a more attractive appearance. In solving this problem the author tried to collect data related to savings and loan data. In designing a new system, several stages are carried out including data design. Table creation, ERD creation, DFD and normalization. In processing the savings and loan data, it is hoped that it can help with problems that arise at the KSU " DANA MANDIRI " Cooperative and the program created of course still has several weaknesses and shortcomings, this is possible because the author still has little knowledge and experience in programming issues. Suggestions and criticism really help the author to further develop knowledge in this field.

CRUD Operation on WordPress Posts From C# over REST API

Purpose: WordPress is a sound content management system on the web. We can build a website with little effort. Around 40% of internet websites are powered by WordPress. Using the WordPress website, our researcher can store research data inside the website database without spending money on the cloud. We demonstrate how to run CRUD operations on a WordPress-powered website database using WordPress Posts. We observe the essential CRUD operation so that our researcher can integrate it easily into their project with complex procedures. We use REST API for communication between the WordPress and the client application. We created a GUI in Visual Studio using C# language for easy operation. An HTTP client is used for communication. The complete project code is available to download and continue research work. Design/Methodology/Approach: First, we install a “local” application in our working system. Then, we create a brand-new WordPress website. We activated the latest theme, which is now Twenty twenty-four. We make a custom route and add a posts operation handling module inside the theme folder. This is the server-side action. On the other hand, inside the Visual Studio 2022 community edition, using C# language, we created a GUI-based client application to interact with users. We added an HTTP client module to communicate with the server. Findings/Result: We experimented with practical, real-world examples. The overall performance is excellent and helpful in real-world scenarios without any issues. Network latency is sometimes present, creating problems in real-time data processing. The researcher creating a custom route in their online website can experiment. It will not have any adverse effect on keeping the website live. Originality/Value: WordPress uses the database to store post-type data and some WordPress-related configurations. Out of the traditional way, we can also reserve our research data as post-type content according to our project requirements. Keeping our data as the post-type of content from the C# application is the least available documentation on the web. We demonstrate here through Practical examples. Some valuable reference information can be found here. Paper Type: Experimental-based Research.

Analisis Kepuasan Tenaga Kesehatan terhadap SIMPUS (Sistem Informasi Manajemen Puskesmas) di Puskesmas Sibela Surakarta

The Sibela Surakarta Health Center has used the Management Information System of Health Center (SIMPUS) which is integrated in all sections as a flow of patient services starting from registration, polyclinic, laboratory, and pharmacy. During its implementation, SIMPUS at the Sibela Surakarta Health Center experienced various problems in data processing, one of which was a delay in the data input process to the computer caused by delays from program holders to SIMPUS officers or network delays. The purpose of this study was to analyze the satisfaction of health workers with SIMPUS at the Sibela Surakarta Health Center using the E-Servqual method. This research is a quantitative descriptive study with a cross-sectional research design using the E-Servqual method. The number of research subjects was 31 people, using quota sampling, and interviews with satisfaction questionnaires. Health workers were very satisfied with efficiency (gap value=0.01), not satisfied with reliability (gap value= -0.14), satisfied with compliance (gap value=0.00), very satisfied with privacy (gap value=0.02), dissatisfied with power responsive (gap value=-0.09), and dissatisfied with the contact (gap value=-0.08).Keywords: Satisfaction, Health Workers, SIMPUS.

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

AbstractThe presence of the air–water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or deghosting) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for two‐dimensional and/or three‐dimensional marine cases. We present a convolutional neural network approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models and make predictions using subsets of the Sigsbee velocity model. We show that the convolutional neural network predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our convolutional neural network–based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our convolutional neural network–based approach removes different orders of free‐surface multiples (e.g., first and second orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflection.

Development of Web-Based Goods Inventory Information System to Improve Efficiency and Effectiveness of Inventory Management at Batam Cable Vision Company

Batam Cable Vision is a company engaged in cable TV and internet installation services. This company has problems in data processing which results in different quantities of goods. So that a system is needed that can determine the amount of inventory in the warehouse. Therefore, a web-based inventory system is designed to find out the exact amount of inventory and can be accessed via the internet. This research is designed using the waterfall method, there are five stages, namely planning is the process of designing a system like what will be made, analysis is understanding the existing system with the system to be created, system design is carried out with the aim of helping to provide a complete picture of what to do, coding is the process of writing program code, then checking and testing the system as a whole to identify possible system failures and errors, in the last stage maintenance includes error correction, improvement of system unit implementation, and system improvement and adjustment according to user needs. While the modelling used uses the Unified Modeling Language (UML) which consists of a use case diagram, activity diagram, class diagram and sequence diagram. The result of this research is a web-based inventory information system at PT Batam Cable Vision is expected to help the admin in data processing, in order to minimise errors in recording goods. Then, the inventory information system can present the right goods report information according to company needs so as to minimise the use of paper.

Perancangan Sistem Informasi Penjualan Berbasis Web di PT. MARKTEL

Information systems are needed in every company to facilitate fast and accurate data processing. One of them is a sales information system currently used at PT. MARKTEL still has processes that have not been computerized. To meet these information needs, especially to deal with data processing problems, report generation and systems that have not worked as expected, companies need a computer-based information system. The system development method in this study uses the waterfall method which has a systematic and clear flow so that the system development is more detailed. The waterfall method is a structured modeling-based method consisting of Flowmaps, Context Diagrams, Level 0 Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD). In designing and implementing the program, the author uses the PHP programming language with MySQL database. The existence of a web-based sales information system at PT.MARKTEL is expected to simplify and speed up data processing and report generation and can minimize errors so that company goals can be achieved optimally.

Management, organization, coordination and prognosis of precast beams during construction

Abstract In this paper, we propose a method for monitoring the construction quality and progress of assembled buildings based on point cloud model feature extraction to achieve accurate identification and rapid detection of construction process information because of the existing problems in precast beam construction data collection and processing, combined with 3D laser scanning technology. Soil excavation and filling calculations are performed to determine the site design elevation before the main structure construction starts, the prefabricated components are simulated to be assembled and viewed for assembly errors during the construction process, and the verticality and flatness of the prefabricated beams are inspected after the construction is completed in combination with least squares. The construction quality problems and delays in construction progress were also corrected and adjusted to ensure a smooth construction process. After the main bridge was constructed, it was measured to have a damping ratio of around 0.011, which means it can dissipate external energy input.