DEVELOPMENT OF MANUFACTURING INVENTORY MANAGEMENT SYSTEM USING MATERIAL REQUIREMENT PLANNING METHOD

Inventory has an important role in business activities. This is because inventory has an effect on changes in the production market and anticipates price changes in the demand for many goods. PT. Barkah Jaya Mandiri is a company engaged in manufacturing where the management of inventory at the company is still done conventionally. This causes various problems such as the occurrence of discrepancies in the stock of goods, discrepancies in data and final reports as well as obstacles in the production process in the event of a shortage or excess of raw materials. (Material Requirement Planning) in order to overcome the problems that occur in the company. The combination of the SDLC model and data collection techniques including observation, interviews and literature study were also carried out in this study in order to achieve the system that will be built to suit the targeted needs. With this system, the management of inventory data at this company can be done easily and accurately and save time compared to the previous system, so that the procurement of manufacturing raw materials is optimal and employee performance is better.

PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

DESIGN OF WEB-BASED UYU STORE ATTENDANCE INFORMATION SYSTEM USING COLORING METHOD

Attendance in the world of work is sometimes still done manually. At the Uyu shop, there are still problems that occur in recording and making attendance reports manually, such as mistakes in biodata, forgetting to record the date. Because the Uyu store still doesn't use a computerized employee or staff absence information system in managing data, so the information that can be processed is quite long and storage is not guaranteed safe. The purpose of this researcher is to create an information system that can manage attendance data for employees who work at the computerized Uyu Store and also implement a coloring method on attendance reports that generate reports based on the attendance coloring method according to employee attendance hours that are easy to understand. This method uses the coloring method as a solution to solving problems that can be solved in the greedy method, namely the color problem and the waterfall model as a system development process that uses UML design. The result of this research is that the system can operate attendance data collection as well as report employee data more efficiently and integrated.

VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.

ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION

During the pandemic, most schools, campuses, and places of education conducted online teaching and learning activities. Many teaching and learning activities are carried out using the Zoom, Google, WebEx, or Microsoft Teams applications. All of this can be done through a PC or laptop, or using a cellphone, so the need for PCs and cellphones increases, both new and used goods. Even though during the pandemic the economic situation was declining, many companies suffered losses, resulting in a reduction in employees and causing a high unemployment rate, the need for Android phones remains high. In addition to online distance learning facilities, Android phones can also be used for online sales through e-commerce, market places, social media, and other digital platforms. Currently, Android phones have many choices and according to the funds we have, with various brands and specifications. Many brands issue android cellphone products with pretty good specifications and affordable prices, so that even though purchasing power has decreased due to the pandemic, sales of android cellphones are still high. In this study, the author predicts the highest sales of android cellphones using the Naïve Bayes method and the K-Nearest Neighbor method based on Particle Swarm Optimization accuracy of 81.33%.

STRUCTURAL EQUATION MODEL OF ARTICLES COVID-19 ON SOCIAL MEDIA TO HEALTH LITERACY AND BEHAVIOR AMONG HEALTH INFORMATION STUDENTS

The COVID-19 infodemic is spreading through social media and website requires people to be able to evaluate information during a pandemic. Health literacy is the key to evaluating the infodemic and to get the decision making on health behavior. Health information students primarily use digital technologies such as social media to get information about covid-19 so that students need to have good health literacy to evaluate infodemic. The purpose of this study was to determine the effect of covid-19 article content on health literacy and health behavior among health information students. Data were collected using a questionnaire on 142 health information students then analyzed descriptively and using the structural equation modeling (SEM) method. The results of the descriptive analysis show that 70% of students access covid-19 information through social media mainly using Instagram, where more than half of students access covid-19 article content <= once a day by reading and liking the article. Based on the results of SEM, it is known that the most important factor of the article content is trustworthy content, the most important factor of health literacy is adherence to prevention and the most important factor of health behavior is reducing contact with other people. From the results of the SEM effect test, it is known that there is a direct effect of content articles on health literacy of 51%. On the other hand, there is a direct effect from health literacy to health behavior of 63,4% and there is no significant effect from content articles to health behavior.

AN EDUCATIONAL GAME APPLICATION TO ASSIST THE COMMUNITY IN REMEMBERING ROAD DIRECTIONS AND PLACES IN PALEMBANG

Visuospatial processing is one of the important aspects of everyday life, whether it is to travel or to recognize the location of objects around. Visuospatial processing can be improved in various way, one of which is by using repetition. WorldVenture app was created to help its users improve their visuospatial capability by remembering landmarks in the city where they live. WorldVenture was created using flutter framework with five steps, namely literature study, data and information collection, application designing, testing, and evaluation. This app will have three main features: Flashcard, Quiz, and Notes. WorldVenture testing result, using questionnaires, showed 64,5% people out of 30 respondents rated WorldVenture excellent for improving visuospatial capabilities and as many as 35.5% of respondents rated it good.

ANALYSIS AND DESIGN OF MOBILE WEB-BASED MENU E-ORDER SYSTEMS USING THE PIECES METHOD (CASE STUDY: CAFÉ 50/50 COFFEE)

Café as a place to relax or chatter where visitors can order the menu available. In general, a café often has difficulty in serving customers, especially for menu ordering facilities. This is also experienced by café 50/50 Coffee which still makes menu reservations manually. Based on these problems, a system of e-order menus of web-based mobile applications is designed. The study aims to produce a mobile web ordering system that is then analyzed with the PIECES indicator to determine the level of user satisfaction. Design of this system using the waterfall model System Development Life Cycle (SDLC) development method and then analyzed the level of user satisfaction with the PIECES method. System testing uses usability testing with the USE Questionnaire method. System implementations are created with the help of the CodeIgniter framework and use the PHP programming language. The results of the study in the form of a menu e-order system at the 50/50 Coffee café with the conclusion of the analysis that the users of the e-order system were “SATISFIED”.

SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECENTRICITY PARAMETERS

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.