“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.
The development of information technology is growing from year to year. To support the smooth flow of information, there are many internet service providers circulating in Indonesia to support their needs. Some of the largest internet service providers in Indonesia such as Indihome, First media, and Biznet Home definitely have their own advantages and disadvantages.At this time, internet provider providers only accept customer complaints or suggestions through the customer service (CS) call center. Meanwhile, many young Indonesians currently use one of the popular Social Media services, namely Twitter as a user-friendly microblogging service so that users can easily use it, especially in delivering messages in the form of tweets. Therefore, a sentiment analysis program was designed for several internet providers in Indonesia. Opinions or Opinions will be analyzed to determine public sentiment. These sentiments will be classified into 3 sentiments, namely negative, positive, and neutral sentiments. The sentiment classification process can be done manually, but if there is too much data, it requires a system equipped with a classification method, so that the determination of classification can be done quickly. The design of this program applies the Naive Bayes Classifier method. Because this method is supervised learning, it requires training datasets with labels. Labeling will be done automatically using the K-means method. K-Means will sort tweets into groups which are divided into 3 labels. The results of the K-means clustering accuracy are 73.4%. The results of this application are divided into 2 parts, namely a pie chart which is divided into slices that describe the results of the percentage of tweet classifications and a table of classification results containing the number, content of the tweet, and the results of the classification. The best level of accuracy in testing uses 220 training data, and 54 training data. The results of the accuracy of 83.3%.
Stock is an instrumentation that has the fundamentalsof a company. Owning a share means owning theshares of the company. Shares can be traded on theIndonesian Stock Exchange by the entire public. TheLQ45 stock grouping and recommendation applicationis a desktop-based application that aims to assist inmaking investment decisions in the Indonesian CapitalMarket. The application is designed using the Pythonprogramming language. The application applies the KMedoidsClustering method to group stocks andExponential Moving Average along with WeightedProducts to make recommendations or sort shares.Users can enter the desired share criteria and theapplication will recommend according to the inputcriteria from the user. Testing on this application iscarried out by the BlackBox testing method,questionnaire, K-Medoids Algorithm Testing and theoutput results of the program. From the results of theblack box testing, the application results can runaccording to plan. In testing the K-Medoids Algorithm,the results obtained that the number of clustersdetermined are in accordance with the number of 2clusters using the elbow method. And in testing theoutput results of the program, the results obtained arethe recommendation accuracy of 80.8%.
In Google Play Store there are lots of application ready to be explored and downloaded. Google Play Store is a place where many developers can sell applications that they have made. Apart from being a place for searching and downloading applications, Google Play Store can also be used to conduct a research. E-Wallet is one of a technological development that can be used to do many transactions. Doing transaction with e-wallet can be done anywhere you want. E-wallet in Indonesia is growing very rapidly especially in the present time where covid-19 is growing rapidly. This is one of the reasons why many people now using e-wallet for doing transcations. Many interesting promotions that were given is also one of the reason why people start using e-wallet. This research had the objective to visualize people’s emotion on e-wallet based on user opinion in Google Play Store. The research stage starts from scrapping data from Google Play Store, preprocessing data, classification with Support Vector Machine, evaluation with confusion matrix. Data were scrapped from google play store using google_play_scrapper API. This research uses OVO review of 500 data, DANA review of 500 data, LinkAja review of 500 data. The classification results will then be evaluated using a confusion matrix. The highest accuracy results will be used as a model for the classification stage. The classification results will be displayed in the form of tables and pie charts that describe the percentage results of sentiment classification.
Prediction credit card submission is a system that is able to provide an assessment of alternatives in order to help credit card applicants in making decisions. Many methods can be used in building a prediction system for credit card submissions. This research will compare two methods in predicting credit card submission, namely the naïve bayes method and c4.5 algorithm, case study of Credit Card Submission Prediction, the results of the research are, knowing the level of accuracy of the two methods. Criteria that form the basis of decision making include age, sex, recent education, marital status, number of dependents, type of company, monthly income, and salary slip. The final result found that the naïve bayes method and the c4.5 algorithm are relatively the same.
Creating an information system for a store becomes an important part in a business process as an effort to facilitate the running of the business process. Therefore, the existence of this information system will make it easier for the store to get information on products, recording the number of products entering and leaving, supplier information and can predict the number of sales in the coming month. Prediction of sales in the coming month is very important to facilitate the store in preparing stock of product in the coming month. The design of this program applies the automatic clustering method and fuzzy logical relationship that manages the historical data of the number of sales each month in the previous period, so we get a prediction of the number of sales in the coming month, which is useful for the store in preparing the amount of stock in the coming months. Based on the results of tests conducted, this program can provide predictions of the number of sales in the coming months. Tests are carried out on two product samples, where the products tested have an accuracy level of 86.20% and 90%, respectively.
Nowadays, phone or smartphone and internet are something that cannot be separated with human. One of negative effects of using internet is cyber crime like a phishing url. Phishing url is usually used to collecting personal information like pin number, credit card etc. There are many type of classification algorithm, two of them is Naïve Bayes and C4.5. Both of the alogithm is good for recognizing a phishing url. This website created are used to classify a unkown url with Naïve Bayes and C4.5 algorithm. The accuracy of C4.5 algorithm is 87.11% and 78.48% for Naïve Bayes. The average time needed to processing one url is 21.78 second for C4.5 and 23.31 second for Naïve Bayes.
In an effort to get competitive prices, one must be able to organize the planning of the availability of the goods it owns so that it can maintain a balance between demand and the existing stock of goods (supply). This application aims to create a precise forecasting system that is useful for determining the inventory of goods in stock that must be done in accordance with the old sales data that have occurred. The method used in this research is forecasting to predict or predict the inventory of goods, then calculating the Economic Order Quantity (EOQ) to return the number of items ordered which will ultimately reduce the cost of inventory. The forecasting method used is Single Exponential Smoothing (SES), Double Exponential Smoothing (DES). The results of data testing on the Forecast method using Forecasting used are Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES). The smallest MAD results were obtained from each of the Forecast methods. After forecasting, the best forecasting method will be chosen which has the value of Mean Absolute Deviation (MAD), mean quality squared (Mean Square Error), the proportion of mean absolute error (Mean Absolute Percentage Error). This Economic Order Quantity (EOQ) method helps to determine the optimal purchase frequency. How to determine and the optimal frequency of purchases.
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