Business Process re-engineering to support the sustainability of the construction industry and sales commodities in large scale transaction during Covid 19 with integrating ERP and Quotation System

Covid 19 has become a pandemic that has hit all over the world. Almost all sectors are affected by this pandemic, not only in the health sector. The economic and industrial sectors have also suffered severe impacts due to the coronavirus pandemic. This spreading virus does not support economic growth either nationally or globally. Finally, it has an impact on various industrial sectors in the country, from manufacturing to finance. The industry was one of the largest contributors to Indonesia's Gross Domestic Product (GDP) last year. The contribution given from this industry to the 2019 GDP was recorded at 19.62%. Due to this pandemic, according to the Central Statistics Agency (BPS), during February 2020, the importance of all categories of goods decreased compared to January 2020. This stunted industrial development was due to the government's appeal for social distancing; social distancing made it difficult for industry players to make transactions. Several obstacles require re-engineering of business processes in the industry. In general, the industry already has an Enterprise Resource Planning (ERP) system used in the related production process. However, ERP does not enter into the realm of buying and selling or the process of procuring goods with consumers. Therefore we need a Business Process Reengineering as an engineering process to integrate the ERP and the bidding system or quotation system. Using the moving average method as a forecasting method, we can get sustainable sales even during the Covid period, even seen an increase in transactions in the new period, along with the implementation of the quotation information system applied in the re-engineering business process.

Usability Evaluation of Lecturer Information Systems Using Sirius Framework and Moscow Technique

An example of application technology in university is developing an information system so that it can support performance and facilitate the information flow. Institute of Technology and Business (ITB) STIKOM Bali is a technology-based university that has an information system, one of which is the Lecturer Information System (SID) considering the role of Lecturers as the main actors in the world of university. SID was developed to facilitate and streamline the work of lecturers. The implementation of SID should be usefull to institutions so that the resources spent on building this system are not wasted. However, until now there has never been an evaluation of the usability of SID, causing the relevant stakeholders not to know the usefulness of the system and not knowing the improvements that need to be made to the SID system. Based on these problems, an evaluation of the SID ITB STIKOM Bali usability was carried out using the Sirius framework and the Moscow technique. The results of the research are the global usability value for SID based on ten aspects of the Sirius framework usability and recommendations for improving the SID STIKOM Bali system based on the Moscow Technique ranking. This research is expected to be a reference material for stakeholders in determining policies related to improving the SID system at ITB STIKOM Bali. The research method used in this research is literature study and observation, SID system analysis, distribustion and processing questionnaire data, data analysis and drawing conclusions

Computational analysis of the interaction of limonene with the fat mass and obesity-associated protein

This study aimed to predict the binding affinity, orientation, and physical interaction between limonene and fat mass and obesity-associated protein. The mechanism of limonene and protein association was explored by molecular docking, a bioinformatic tool. The results of association were compared with the reported results of the anti-obesity drug such as orlistat and with the flavonoids. AutoDock Vina tools were used for the molecular docking of limonene with fat mass and obesity-associated protein. PyMol and Discovery Studio Visualizer were used to visualize the results of this docking. The binding affinity of limonene was higher (Least negative G) than the orlistat and flavonoids such as Daidzein, Exemestane, Kaempherol, Letrozole, And Rutin. It is conducted in this study that the Limonene can alleviate obesity by making an interaction with the fat mass and obesity-associated protein. This inhibitory interaction was greater as compared to other reported phytochemicals and drugs.

A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification

Keywords are the labels of your manuscript and critical to correct indexing and searching. MRI or Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. The method often used in the previous method is segmentation, but the process is quite heavy and the results that are less accurate are still the main obstacles. This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. The accuracy obtained is 91.78%.

Prediction of COVID-19 Using Recurrent Neural Network Model

The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.

Adversity Response Profile IT-assisted for Lectures Online during New Normal

The Distance Education implemented by the government during New Normal has an impact on student learning activities and student lectures. To find out the ability of students to face problems in current conditions, it can be seen from the Adversity Quotient (AQ) which can be measured by the Adversity Response Profile (ARP). The purpose of this study was to develop valid and reliable IT-assisted ARP in online lectures during New Normal. The research method used was Research and Development (R&D) which was conducted on 50 students of the UNNES Mathematics Department. This development research consists of three stages, namely the initial investigation, the prototype phase and the assessment phase which will then be analyzed using quantitative analysis techniques. The results showed that the IT-assisted ARP met the valid and reliable criteria. The results show that the reliability value is very high, namely 0.98. With the discovery of valid and reliable ARP, educators can use it to determine the level of student ability in dealing with learning problems during the New Normal.

Implementing Geo Positioning System for Children Tracking Location Monitoring based on Android

Parents' concerns in terms of monitoring the presence and monitoring of children's activities become one of the problems that are often faced. Training children to prepare themselves to face this sometimes dangerous world, first develop a sense of security in themselves. The objectives of this research are to generate a system or application to track the whereabouts of children through smartphones that can be used by both parents of the child and to design devices that can be used practically and children can be traced to the presence of wireless media by their parents. Improved supervision of children is needed without reducing the freedom of children to learn and explore their environment. One alternative that allows tracking the presence, location, and accuracy is quite good is to use a Geo Positioning System (GPS). This GPS is packaged in a flexible form so that it can help make it easier for parents to supervise their children in the range of areas that are still accessible to them and find the child's position when separated from their parents. The system can send the location information of children who are lost or lost on smartphone applications used by parents.

Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%

Assess of Forensic Tools on Android Based Facebook Lite with the NIST Method

The increase in social media use of Facebook lite by using Android-based smartphones is quite high. Activities when communicating through the social media network Facebook Lite Facebook lite can send a text message, image, or Video. Not a few users of Facebook lite social media abusing this app to commit fraud crimes, pornographic acts, or defamation actions from social media users Facebook lite. In such cases, it can be a digital forensic benchmark to get results from digital evidence from the Facebook lite application. In this investigation, National Institute of Standards and Technology NIST research methods with various stages, namely Collection, Examination, Analysis, and Reporting. While the forensic tools to be used are Magnet Axiom Forensic and MOBILedit Forensic Express Pro. Comparison and results of data conducted with forensic tools Magnet Axiom Forensic and MOBILedit Forensic Express Pro in the form of parameter data specified. Axiom Forensic Magnet data is 57.14% while MOBILedit Forensic Express Pro data is 85.71%. This data is the data of the performance results of both forensic tool applications in obtaining digital evidence on Facebook lite application.