IMPLEMENTATION OF THE SCRUM MODEL IN THE DEVELOPMENT OF ONLINE SALES SYSTEMS OF MSMEs DURING THE COVID-19 PANDEMIC

A global pandemic or epidemic indicates a covid-19 infection that is very fast spreading throughout the world, including Indonesia. This has an impact on several sectors, one of which is the economic sector. There are various things that have caused the economic sector to be touched by the impact of the covid-19 virus, including government policies at both the central and regional levels that issued several regulations relating to restrictions on community mobility. Indirectly, things related to mobility restrictions or what is currently known as Pembatasan Sosial Berskala Besar (PSBB) have an impact on consumer behavior to switch to making purchases online. To address this, the online sales system is considered to be a solution for MSMEs to continue the buying and selling process. Using the Scrum model as a more efficient system development, feedback between users and developers who can work better to create a more interactive system. The results of this study are a website that can be used by UMKM as a means of selling their business products amid the Covid-19 pandemic.

DIAGNOSIS OF HEART DISEASE USING AUTOMATA FINITE STATE ALGORITHM

The heart is an organ of the human body that has an important role in human life and is certainly very dangerous if our heart has problems remembering that many deaths are caused by heart disease. But with minimal knowledge and information, it is impossible to be able to maintain heart health. Therefore we need an expert who is an expert on the heart and various diseases. Based on the facts above, this research can help us to diagnose heart health and anticipate if there is a risk of heart disease by designing and implementing. This application was created using the web-based Finite State Automata algorithm which is still in the form of pseudocode. In this system several questions will be asked. After all the questions are answered, the results of the diagnosis will appear along with suggestions that can help anticipate the heart disease.

ANALYSIS OF QUALITY OF SERVICE BANDWIDTH MANAGEMENT ON COMPUTER NETWORK USING MIKROTIK RB951Ui-2HnD

Computer networks are now part of every human activity. The use of computer networks is currently experiencing a significant increase due to the Covid-19 pandemic, however, there are still many problems with computer networks, such as what often happens is the slow pace of computer networks in transferring data caused by limited bandwidth or maximum limits. owned by the computer network. For that, we need a computer network bandwidth management method called the QoS (Quality of Service) method. In this method, a tool called the Mikrotik Router-board RB951Ui-2HnD is used to conduct this research. In implementing this system the steps are taken starting from data collection, requirements analysis, system design with flowcharts and system depiction using a star topology, then implementation and unit testing on the user or Client, and testing the computer network system on the Mikrotik Router-board. The results of this study will be explained using statistical tables obtained from testing using the Wireshark application

APRIORI ALGORITHM FOR DETERMINING THE DEMAND LEVEL OF STATIONARY PT. MAIN GAFA INDONESIA

PT Gafa Utama Indonesia is one company that provides services in teaching and writing. Until now, PT Gafa Utama Indonesia already has 30 well-known branches in Jabodetabek. In the teaching and learning process, Gafa needs some stationery and teaching aids. The high demand for office stationery, and the mismatch of inventory in the warehouse, affects the fluency in the teaching and learning process. The data used in this study is the report data on the demand for office stationery for the period January-December 2018. This study uses a priori algorithm method and assessment with tanagra tools. The results of manual calculations with Microsoft Excel are the same as those using the tanagra tool. The final result shows the 2 items with the most demand, namely an eraser and a sharpener with at least 50% support, and 50% confidence. These results can be used as a reference for PT Gafa Utama Indonesia in the supply of office stationery

THE DEVELOPMENT OF A WEB-BASED INTEGRATED FINANCIAL INFORMATION SYSTEM AT PT. PURA BARUTAMA KUDUS

Pura Barutama Inc. is a printing and packaging company based in the city of Kudus, where its financial information such as pre-order reports, accounts receivable balance reports, invoice information, and income tax article 23, must be inquired manually to the finance department. So there are issues with access to the financial information that is directly linked to consumers and vendors, namely a lack of flexibility and productivity in marketing, tax, and purchasing departments. Therefore, this study aims to provide solutions to the problems by designing and building a web-based financial information system. This information system was designed using object-oriented modeling methods, namely the Unified Modeling Language (UML) method, and was built with the PHP programming language using the CodeIgniter framework and Oracle database which was directly connected to Pura Barutama Inc. This information system is expected to increase flexibility and efficiency in the marketing, taxation, and purchasing departments of the company’s financial data management process and can obtain financial details without requesting the finance department.

A BLOCKCHAIN-BASED ONLINE REVIEW SYSTEM OF TOURISM PRODUCTS USING ETHEREUM

Rapid technological advances have made blockchain technology applicable not only to digital money, but in various fields. One of the areas that can be implemented by blockchain is digital tourism, specifically in the online review system of tourism products. The current online review system has several problems due to its centralized nature. The problem faced is the manipulation of review data which can be in the form of review deletion by a centralized party. This research proposes a decentralized online review system using the Ethereum blockchain technology, Smart Contracts, and IPFS to provide a secure, transparent, and trustworthy online review system platform. The purpose of this research is to implement a permission-less blockchain as a storage for reviews (review forms and log notes) and develop a web application as a user interface. The data used is data from travel sites which contain details about hotels and restaurants in Bukhara. The results displayed are the development of a web application that implements a permission-less blockchain using Ethereum and the system performance is displayed based on system testing, which comprised of unit testing and Black-Box testing.

GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST

Clothing also means clothing is needed by humans. Besides the need for clothing in terms of function, clothing sales or business is also very potent. About 75 million people worldwide are directly involved in textiles, clothing, and footwear. In this case, a common problem in this industry is that the actual productivity of apparel employees sometimes fails to reach the productivity targets set by the authorities to meet production targets on time, resulting in huge losses. Experiments were conducted using the random forest model, linear regression, and neural network by looking for the values ​​of the correlation coefficient, MAE, and RMSE. This aims to predict the productivity of garment employees with data mining techniques that apply machine learning and look for the minimum MAE value. The results of testing the proposed algorithm on the garment worker productivity dataset obtained the smallest MAE, namely the random forest algorithm, namely 0.0787, linear regression 0.1081, and 0.1218 neural networks

PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes

DIAGNOSIS DETECTION OF ACUTE RESPIRATOR INFECTION WITH FORWARD CHAINING METHOD

Many acute respiratory infections or ARI are caused by viruses that attack the nose, trachea (breathing tube), or the lungs. It can be said that ARI is caused by inflammation that disrupts a person's breathing process. If not treated quickly, ARI can spread to all respiratory systems and prevent the body from getting proper oxygen, moreover it can cause the loss of a person's life. This research aims to diagnose ARI as an early step in practicing artificial intelligence in medicine, designing and apply an expert system that can diagnose ARI. The procedure used in this study uses forward chaining with tracking that begins with input data, and then creates a diagnosis or solution. The expert system used to diagnose acute respiratory inflammation uses the Forward chaining procedure with a data-driven approach, in this approach tracking starts from input data, and then seeks to draw conclusions, so that it can be used. diagnose the type of disease associated with the ARD disease experienced by showing the existing signs.

SELECTION OF THE RIGHT MARKETPLACE FOR SALE OF ORNAMENTAL PLANTS USING ANALYTICAL HIERARCHY PROCESS (AHP) METHOD

Basically, each marketplace has its own market, therefore as a seller who will start an online business, especially those who use the marketplace, they must know which marketplace is right to start their online business according to what category of goods to sell. This research is needed to help business people, in this case ornamental plant traders, choose the right marketplace for their online business activities. Decision Support System (DSS) is a computer-based system used to help make decisions from structured and semi-structured problems. Analytical Hierarchy Process (AHP) is a method that can be used to select from the specified criteria. This method can simplify the criteria that are considered in making decisions for marketplace selection to be simpler and easier to understand. The results of the study show that the decisions made using the AHP method are very effective, and are expected to help make objective decisions. After processing data and analyzing respondent data, with the criteria for the number of visitors, transaction system, features, and searc engine optimization (SEO), it was found that Lazada (0.428) has the highest priority, Tokopedia (0.235) with second priority, Shopee (0.204) with priority third, and Bukalapak (0.133) with the fourth priority