Manual Recording Research Articles

Airport flight ground service time prediction with missing data using graph convolutional neural network imputation and bidirectional sliding mechanism

Accurate flight ground service time prediction plays a key role in the efficient operation of airports, which can help managers make better resource scheduling optimization decisions, reduce flight delays and improve airport operation capabilities. However, since flight ground service data is typically acquired by sensor equipment or manually recorded, data missing and abnormalities caused by sensor failure or manual recording errors are an unavoidable occurrence, making the prediction problem challenging. Although missing values can be imputed, the traditional methods usually fill in missing values and then make predictions separately, which leads to a two-step process and increases the prediction error. Besides, the existing one-step methods still suffer some limitations and shortcomings: failure to integrate bidirectional spatial–temporal dependencies leads to unsatisfying model prediction accuracy and poor robustness under high data missing rates, which cannot be directly used in the study of flight ground service time prediction with missing values. To solve this problem, we presented a sliding bidirectional graph convention network model framework that capable of dealing predicting with missing values. First, we proposed a new adjacency matrix construction method to capture more comprehensive spatial–temporal dependencies and developed a graph convention network based missing value imputation unity (GCNM) to inference and filling missing features. Then, multiple GCNM units are stacked to develop a bidirectional module BDGCNM to realize the filling and prediction into one step. Finally, we introduced a variable sliding window mechanism to improve the model’s robustness. Extensive experiments on real airport data sets and PEMS traffic data sets show that our method is more accurate and robust than the current state-of-the-art.

Rock point load test (PLT index) and its correlation with rock coring rate of bored pile machine

Rock socketed cast in-situ bored pile has getting more common in the local foundation industry due to high load carrying capacity to support the increasingly large superstructure load. The estimation of bored pile production rate based on rock strength is vital for piling contractor in terms of project planning, site work done forecasting and management of resources. Uniaxial compressive strength (UCS) of rocks is a useful parameter needed for analytical and designing purpose for rock structure, as well as classification of rock strength. Owing to the time-consuming, tedious and expensive testing procedure of UCS, point load test (PLT) on irregular lump samples has appeared to be the best alternative test to ascertain rock strength, which can be performed on-site or off-site. This research was conducted to establish the correlation between rock PLT index and rock coring rate of bored pile machine, where the PLT results and rock coring data were collected from manual recording at respective project site. Graphical relationship was derived for PLT index against rock coring rate for several pile size categories. As rock UCS is typically adopted for foundation design purpose, the conversion factor between PLT index and UCS was derived for the typical bedrock formation encountered in Kuala Lumpur, namely Granitic rock and Limestone. This study also depicts the estimation of mobilized rock unit shaft friction with respect to the corresponding UCS value, using a recommended empirical formula. From this research study, it was justified that PLT could serve as a cost-effective and straightforward field test which provides invaluable guidance for piling production rate estimation as well as better engineering judgement in foundation design.

Perancangan Sistem Informasi Pengelolaan Data Penjualan Pada UD Memeri

UD Memeri is one of the SMEs that sells various types of ducks. As long as this UKM is running, the quality of the ducks is sold by UD Memes are never bad. Currently, UD Memeri's sales transactions per day can reach 50 to 100 individuals. start recording sales of ducks. With manual sales and recording, it is possible for errors can occur in recording transactions. Transaction activities and product data management are still carried out in a conventional way which makes the work process take a lot of time because in the transaction process in sales, therefore an information system is needed to manage sales transaction data at UD Memeri. Making an information system cannot be developed immediately before there is a needs analysis and system described in the form of a design. The results of the analysis of user needs, there are three users of the information system, namely admin, owner, and staff. The system design is based on the analysis of user needs by designing Data Flow Diagrams (DFD) and designing databases using Entity-Relationship Diagrams (ERD), and designing an information system interface that will be built based on a website. Interface design includes admin page interface design, staff page interface design, and owner page interface design. With this design made, UD Memeri has an overview of the information system that will be built and adjusted to needs

Analysis of Clinical Outcomes of Pregnant Patients Treated With Nirmatrelvir and Ritonavir for Acute SARS-CoV-2 Infection

Pregnant people are at increased risk of poor outcomes due to infection with SARS-CoV-2, and there are limited therapeutic options available. To evaluate the clinical outcomes associated with nirmatrelvir and ritonavir used to treat SARS-CoV-2 infection in pregnant patients. This case series included pregnant patients who were diagnosed with SARS-CoV-2 infection, received nirmatrelvir and ritonavir, and delivered their offspring within the Johns Hopkins Health System between December 22, 2021, and August 20, 2022. Treatment with nirmatrelvir and ritonavir for SARS-CoV-2 infection during pregnancy. Clinical characteristics and outcomes were ascertained through manual record review. Forty-seven pregnant patients (median [range] age, 34 [22-43] years) were included in the study, and the median (range) gestational age of their offspring was 28.4 (4.3-39.6) weeks. Medication was initiated at a median (range) of 1 (0-5) day after symptom onset, and only 2 patients [4.3%] did not complete the course of therapy because of adverse effects. Thirty patients (63.8%) treated with nirmatrelvir and ritonavir had a comorbidity in addition to pregnancy that could be a risk factor for developing severe COVID-19. Twenty-five patients [53.2%] delivered after treatment with nirmatrelvir and ritonavir. Twelve of these patients [48.0%] underwent cesarean delivery, 9 [75.0%] of which were scheduled. Two of 47 patients [4.3%] were hospitalized for conditions related to preexisting comorbidities. In this case series, pregnant patients who were treated with nirmatrelvir and ritonavir tolerated treatment well, although there was an unexpectedly high rate of cesarean deliveries. The lack of an increase in serious adverse effects affecting pregnant patients or offspring suggests that clinicians can use this drug combination to treat pregnant patients with SARS-CoV-2 infection.

A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials.

To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque. Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley-Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations. The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r=0.92; p < .001) and TM-QHPI recordings (r=0.88; p < .001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p > .05, ANOVA). The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

Design and Construction of PDAM Water Use Monitoring Based on Internet of Things (IoT)

Water is a natural resource that is very important for life in the world for humans, animals and other living things. In a settlement, there are two ways to provide clean water, namely piped and non-piped systems. The piping system itself is managed by the Regional Drinking Water Company (PDAM), which is a local government company that carries out the service function to produce drinking water and clean water for the community. The current problem occurs in the community and is detrimental to the community itself, namely the manual recording system of water usage meters, often the fees charged by the community are not in accordance with the usage meter used. For this reason, an automatic monitoring of water usage was designed. This tool uses a Waterflow to detect the amount of water consumption which is controlled with NodeMCU Esp8266, later monitoring of usage can be seen on the LCD and Thinger.io as a database. Testing this final project using a measuring cup as a comparison of the real water volume with the volume of the results read by the sensor. The test results of the final project will read the volume of water and the rate for usage fees with an error percentage of 0.3%. The resulting data can be monitored in real time through the thinger.io website and smartphones to make it easier to monitor remotely.

Penerapan Enterprise Resource Planning Berbasis Odoo pada Modul CRM (Studi Kasus UMKM Credeva)

UMKM Credeva is one of the UMKM website design and programming services located in the Duduk Sampeyan area, Gresik City. Enterprise Resource Planning (ERP) software is a system that integrates all business processes, from inventory, accounting and finance, resource management, and so on. By using ERP software, companies get several benefits, including: increased efficiency &amp; productivity, planning &amp; information management, process standardization, and of course better business integration &amp; data accuracy. The accuracy of this data is very important to see the amount of data that is in the business processes of a company, especially medium and large companies. The Odoo UMKM Credeva implementation project was carried out because it saw problems in its business processes. The problem is in Relationship Management between customers because it is still not optimal when there are many overloads on incoming orders because they are still using manual recording. Then the problem that often occurs is the negligence of not recording the order deadline to the customer. As well as other problems, lack of understanding of the use of customer management where employees are still minimal. This results in a gap between reports and delays in accessing information to customer requests. So that Odoo is very much needed in carrying out business processes going forward. In this plan, will provide a module on CRM (customer relationship management)

Design of an unattended ore grading measurement system in a uranium mine

Abstract The unattended ore grading radioactivity measurement system is an important part of the digital uranium mine. However, with the promotion of the digitization of mines, the radioactivity measurement system for the truck checkpoint used to measure the grade of ore for hydrometallurgical process still maintains the backward processing method of offline measurement and manual recording. To improve the situation, an unattended ore grading measurement system using gamma detectors is developed. Through unattended identification, weighting and radioactivity measurement, the values of the grade of ore and the uranium content are calculated and uploaded automatically. The system has been put into practical use at a uranium mine in southern China in September 2021. Through the field use, the system can realize automatic measurement, reduce labor costs, and establish a production database, which lays the foundation for further application in the construction of digital mines on a large scale.

Assessment on Vaccine Supply Chain and Logistics Systems in the Case of Ethiopian Pharmaceuticals Supply Agency JIMMA Branch

Immunization helps save lives, protects serious illness and is recognized as one of the most effective public health intercessions and available today. However, the success of the programs depends heavily upon the immunization supply chain management including storage, transportation, and handling of vaccines in a proper manner. It was, therefore, important to assess the immunization supply chain and logistics practice. The study was conducted at the EPSA Jimma branch. Purposive sampling methods were used because the hub has completed the Woreda level vaccine transition and have a better experience in vaccine management. Moreover, all staffs (7) who directly engage in vaccine management were a respondent in this study. The primary data was collected using structured questionnaires and Standard checklist observation. In addition, secondary data was collected from documents and recordings from the manual and electronic recording tools. Qualitative and quantitative data were collected and analyzed according to their type. For the analysis of the quantitative data descriptive statistics supported by SPSS software version 20 was applied, and for qualitative data, document analysis was done. SPSS mean and the standard deviation was used to calculate and show the expert's experience on immunization supply chain practices, and frequency and percentage were considered to present the respective practices. The result showed that there are rooms for improvement to ensure recommended good storage. The branch has sufficient positive (+20C to +80C) storage capacity, but the available negative storage volume only covers 65% of the negative storage capacity needed. Respondents were agreed on bundling practice during the distribution of vaccines, Disagree on Refrigerated vehicle pre cold before loading of vaccines, whereas strongly disagree on the temperature of vaccine is always monitored during transportation and availability of an adequate number of the vehicle for transportation of vaccines for which respondents remained neutral. The study strongly indicates the need to improve vaccine logistics supply chain management practices, especially in vaccine storage and handling and temperature monitoring. EPSA and development partners working on vaccine logistic and supply chain management should have intensified their technical and material support to improve the vaccine management practices.

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal.

One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ≥65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F1-score, sensitivity, specificity, and positive and negative predictive values. After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ≥65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. DERR1-10.2196/40456.

Improved Angle Steel Character Detection Algorithm Based on DBNet

Aiming at the problems of false detection and missed detection in manual recording of industrial angle steel characters, an improved angle steel character detection algorithm based on DBNet is proposed. First, the angle steel character dataset is collected. The algorithm uses MobileNetV3 as the backbone network, and replaces the attention mechanism in the backbone network with a new coordinate attention mechanism to obtain larger regional information and enhance the ability to extract complex features; secondly, weighted bidirectional The feature pyramid network improves the efficiency of multi-scale fusion and enhances the detection ability of small features; finally, a new data augmentation method and learning rate optimization strategy are used to enhance the robustness and convergence accuracy of the model. The experimental results show that, compared with the original DBNet algorithm, the detection index of the improved DBNet algorithm is increased from 81.42% to 99.06% on the angle steel character data set, which meets the needs of angle steel character detection in the industrial environment.

A qualitative analysis of the COVID-19 vaccination rollout in Lagos, Nigeria: Client and provider perspectives on the plan, the process and the progress.

Mass vaccination has proven useful in the control of COVID-19, though vaccine rollout has met major challenges. The learning curve of this process has been valuable. This qualitative study aimed to assess the plan, the process and the progress of the COVID-19 vaccination rollout in Lagos, Nigeria. This study was conducted at vaccination centers in eight of the 20 Local Government Areas in Lagos State from May to July 2021 among healthcare administrators, health workers and vaccine recipients. Data were collected by conducting three key informant interviews, 24 in-depth interviews and eight focus group discussions to explore the vaccination experiences of participants and the challenges facing the vaccination plan and process. The interviews and discussions were recorded, transcribed verbatim and analyzed using the thematic approach. The four-phased plan for the vaccine rollout was clear to all the key informants because the vaccination process was preceded by training. The process was strengthened by the electronic registration system, though riddled by the frequently unstable electronic and internet data capturing. This was mitigated by a stopgap manual registration and recording of client details. Challenges in the logistics of maintaining supplies of the disposable materials required for the vaccination process were overcome by the creativity of the health professionals. Vaccine hesitancy, fueled by misinformation, myths and misconceptions about the vaccine and its side effects, played a huge role in the community response. The reported vaccine side effects were mild; fever, headaches, pain at the injection site, excessive eating and sleepiness. Though the COVID-19 vaccination process appeared to have largely made progress, the future of vaccination in Nigeria is predicated upon a bottom-up approach to programmatic planning, health education and local vaccine production. Collaborations such as public-private partnerships have the potential of boosting vaccine provision for Nigeria's large population to ensure equitable access to vaccines.

Abstract 253: Decline In Initial Shockable Rhythm In Patients Treated With Public Access Defibrillators Over A 10-year Period

Introduction: Previous analysis of emergency medical services manual defibrillator recordings suggests that the proportion of patients with initial shockable rhythms during out-of-hospital cardiac arrest is decreasing over time. This analysis aims to determine if this decline also occurs in public access defibrillator data. Methods: Worldwide post-market data from HeartSine SAM PAD public access defibrillators was collected between 2012 and 2022. Presence of an initial shockable rhythm (ventricular fibrillation or ventricular tachycardia) was determined by the device algorithm decision and clinical overread. The proportion of patients who presented an initial shockable rhythm each year was calculated. Data was analysed using Minitab. Summary statistics were calculated for patient age and gender, and a logistic regression model was used to examine the proportion of initial shockable rhythm over time. Results: A total of 5404 patient events were collected. Seventy-four percent were male, and mean (SD) age was 61.2 (18.2) years. Proportion of initial shockable rhythm as a response to year was assessed, and occurrence of shockable rhythms decreased by approximately 6% per year [OR: 0.94, 95%CI (0.91, 0.96), p&lt;0.001]. Conclusion: In line with findings from emergency service resuscitation attempts, the proportion of initial shockable rhythms in patients treated with a public access defibrillator declined over a ten-year period.

Internet of Things (IoT)-Integrated Embodied Carbon Assessment and Monitoring of Prefabricated Buildings

Buildings contribute significantly to carbon emissions over their life cycle. Recently, embodied carbon (EC) accounts for an increasing share of life cycle carbon emissions of new buildings as their energy efficiency is improving. However, traditional methods of data collection and communication from diverse carbon sources such as manual recording lead to low-efficient and prone-to-error estimation of embodied carbon emissions. Therefore, this paper aims to propose an Internet of Things (IoT)-integrated embodied carbon assessment and monitoring system (ECAMS) for prefabricated buildings. This system involves three layers, i.e., of data collection, data communication, and data analysis. To provide a theoretical foundation, the EC assessment model is firstly modified to distribute carbon emissions into twelve construction statuses and at five levels of analysis of prefabricated buildings. IoT sensors including radio frequency identification (RFID), acceleration transducer, and global positioning system (GPS) are employed for automated real-time data collection. IoT data will be communicated with building information modelling (BIM) and carbon assessment software through application programming interfaces (APIs). Laboratory tests were conducted to demonstrate the feasibility of sensor-based data collection and communication. The proposed system facilitates more efficient and accurate estimations of prefabricated buildings’ embodied carbon, which should help practitioners to explore effective carbon reduction strategies.

Rancang Bangun REST API Aplikasi Sistem Informasi Gardu Distribusi berbasis Android dan Web

Today, the use of information systems is important, especially in archiving important data. Advances in technology have made conventional recordings begin to switch to digital. The case study in this research is to make an information system on one of the electric power systems, namely the distribution system. In this distribution system there is a substation that contains important components in the distribution of electricity from transmission to consumers. Generally, data collection at distribution substations uses manual recording on paper. This becomes less effective when it finds conditions of archiving data that have been in the past. Therefore, an application is designed that can help the authorities to record android-based data and websites. This information system is made using Rest API which facilitates communication via the internet. From the research that has been done, the performance between the mobile application and the website is good. Website performance produces applications on the web that have good results, namely 99, while Android is 93 from a maximum value of 100.

Degitalization of Medical Record Administration Services at Dian Rapha Medika Toraja Clinic

In the world of health, the management of administrative documents, especially medical records, is a very important thing to do. Management of medical records in collaboration with information technology can improve services effectively and efficiently in managing information data quickly and avoiding delays in service. Until now, the Dian Rapha Medika Toraja Clinic still uses manual medical record administration management with several existing obstacles. This causes health services that should be handled quickly cannot be carried out. Therefore, the researcher is interested and aims to design a website-based medical record administration management system at the Dian Rapha Medika Clinic Toraja. This study uses the waterfall model design method, which consists of 1) Needs Analysis, 2) System Design, 3) Implementation, and 4) Testing. The data collection techniques in this design are 1) Interview and 2) Observation. The results of this study indicate several advantages when using digitization of administrative services for medical colleagues at Dian Rapha Medika Toraja clinic, namely: 1) assisting in digitally documenting patient medical record data; 2) input of patient medical record data is easier and faster; 3) facilitate doctors in analyzing the patient's medical records; 4) searching for patient medical record data is easier and faster; 5) storage of medical record data is more concise and well-structured due to the integration of patient data and medical records so that it can accelerate administrative management at the clinic.

Information Systems at Irfan Jaya Stores Using the Rational Unified Process Method

Irfan Jaya's shop is a shop that sells Papuan-patterned clothes, bags, and sweaters since 2011. However, as the shop's business grew, there were several problems experienced in its business processes, such as information and store promotion systems which were only limited to shop banners. Another problem faced by the owner is the collection of goods and sales history is relatively long because they are still doing manual recording on the book. Based on the problems that occur, it is necessary to create a system such as an online shop that can facilitate the owner in managing and controlling his business. The purpose of this study was to design and determine the system requirements of the Irfan Jaya store using the Rational Unified Process method. the software is programmed using the PHP programming language and the implementation of the user interface display uses HTML and CSS. The software is built based on a website, with the hope that the device will be a solution to existing problems and able to simplify the business processes that run at the Irfan Jaya store. The results of this study are twofold. First, the system architecture design is at the inception and elaboration stages. Second, system implementation and testing at the construction and transition stages.

Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI‐Based Ceramic Material Analysis

AbstractResearch interest in ceramic materials increased due to their extensive environmental, biomedical, and electronic applications. Increased demand for ceramics with specialized experimental conditions and limited resources has resulted in a higher cost for scientific practices and applications. Enormous material data is accumulated from traditional and high‐tech experimentation, but the manual recording process has shown inconsistencies in the analysis results. Recently, application based on artificial intelligence (AI) and machine learning has been able to address the issues of traditional scientific experiments in material science. However, no machine learning mechanisms are proposed for sophisticated data preparation and AI‐based discovery application of ceramics. This paper proposed an intelligent material data preparation mechanism based on ensemble learning for AI‐assisted material screening and discovery. The current method can potentially resolve the problems of missing and inconsistent material data. As a case study, a material data preparation platform for ceramic material data pre‐processing is developed. For performance evaluation of the proposed mechanism, machine learning regression models are trained before and after the imputation techniques applied to the data. Performance analysis shows that the ensemble model of deep learning network (DNN) and automated machine learning (autoML) performed better as compared to previously reported imputation approaches.

Long-Term Risk for Mood and Anxiety Disorders After Pediatric Traumatic Brain Injury: A Population-Based, Birth Cohort Analysis.

To determine whether exposure to traumatic brain injury (TBI) before 10 years of age is associated with development of a mood or anxiety disorder by 25 years of age, and whether sex or injury severity influences this risk. Olmsted County, Minnesota. A total of 5518 persons born from January 1, 1976, through December 31, 1982. Population-based, birth cohort study. Children sustaining TBI before 10 years of age (index date) were confirmed by manual record review and classified by injury severity using the Mayo Classification System. Each TBI case was age- and sex-matched to 2 referents from the same birth cohort without a history of TBI at the index date. Cox proportional hazards models were fit to compare the risk of a subsequent clinically diagnosed mood or anxiety disorder by 25 years of age between TBI cases and referents. Separate analysis was performed stratified by sex and injury severity. Incidence of mood and anxiety disorders determined through clinical diagnostic codes and manual record review. The study included 562 children (238 females [42.3%] and 324 males [57.7%]) with TBI before 10 years of age (mean [SD] age at TBI: 4.7 [2.8] years). At least 1 mood or anxiety disorder was diagnosed for 115 persons with TBI and 215 referents. No statistically significant association existed between childhood TBI status and anxiety disorder (adjusted hazard ratio [aHR], 1.01 [95% confidence interval (CI), 0.71-1.43]; P = .97) or mood disorder (aHR, 1.16 [95% CI, 0.92-1.47]; P = .21). However, females who sustained TBI had a significantly increased risk of a subsequently diagnosed mood disorder compared with age-matched female referents (aHR, 1.40 [95% CI, 1.04-1.89]; P = .03). This study suggests that isolated TBI before 10 years of age is not significantly associated with an increased risk of anxiety or mood disorder by 25 years of age, though females may be at an increased risk.

ARTI PENTING MANAJEMEN DAN PENCATATAN PIUTANG PADA USAHA MIKRO BERDASAR AKRUAL (ACCRUAL BASIS)

A business will decide to carry out credit sales transactions with the aim of stimulating the interest of consumers / customers, increasing sales volume, winning the competition strategy and increasing market share as well as increasing the company's net profit. immediately paid off, there is also a risk of uncollectible receivables if they are not selective in selecting customers who are entitled to receive credit purchase facilities. Internally, it is necessary to record receivables properly and correctly so that the collection process and circulation of company funds embedded in receivables are smooth. Mitra Desa Indonesia is a laying hens business, still using manual recording, using notebooks, ballpoints and calculators. Cash and credit sales transactions are recorded in one book, cash sales are in the Debit column and credit sales are in the Credit column, an explanation of the receivables paid is in the Remaining column. Of course this is not a correct recording, transaction information is not conveyed in the financial statements or in the management of credit sales transactions which eventually become receivables. With observations for 4 (four) months, with an average transaction of 9.6 tons of eggs per month, of which 86.22% are credit sales, the author provides assistance in understanding the recording using the accrual basis, making cash flow and recording accounts receivable using a computer. simply (excel format). This is done by the author because with manual recording, there are many weaknesses, multiplication and wrong numbers. Making credit cards is very important because 86.22% of transactions are credit sales. Mitra Desa Indonesia should manage its receivables properly, supported by proper recording methods and credit cards as control of each customer's receivables.