Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979.

The government modernizes the state revenue system by launching the State Revenue Module (MPN), which connects the billing code generation system with the payment system at the collecting agent. This study aims to develop an information system on how to pay state revenues to assist the public in depositing state revenues through payment channels at the collecting agent by applying the Finite State Automata (FSA) modeling concept. The system development uses the waterfall model while collecting the agent's data. The FSA design and testing phase uses the JFLAP application, while the application design stage uses the Laravel framework. System testing uses the black box testing method to determine how far the functioning of the components or menus on the system has come. In addition to making it easier for the public to deposit state revenues, this study also shows that automata theory can help design a payment information application system. This application design offers a website display of information on how to pay state revenues that can run well. Every menu on the system, when used, has no errors so that this system can be utilized by users and developed in applications based on Android and iOS.

The purpose of this study is to apply a linear regression algorithm to predict the accuracy of product shipments using administration, production and delivery as attributes that will be compared with existing targets which are the results of agreements between companies and customers used. as the dependent variable. In this study it was found that a linear method is feasible and effective for predicting administration, production and delivery of targets for accuracy of product shipments in the company. From the results of the prediction formula obtained, the prediction formula is implemented in the July 2018 data as test data. The results obtained from the prediction formula have an accuracy of 91.67%. From previous studies, which say that linear regression is feasible and effective for data use, they must make predictions. Likewise with this research where the results obtained in testing data are feasible and effective for the company.

The pandemic that occurred in Indonesia has not yet subsided and far from under control. Indonesian Ministry of Health is most appropriate person to responsible for providing an explanation of actual situation and extent to which state has handled it. However, he has rarely appeared in public lately to explain about handling of Covid-19 pandemic. In response, many people are pros and cons come to give their opinions and feedback. The increasing use of internet during pandemic, especially on social media, where one of them is Twitter, which is a means of expressing opinions. Posting tweets is a community habit to assess or respond to events, as well as represent public's response to an event, especially Ministry of Health steps and policies in handling and breaking chain of Covid-19 pandemic.The tweet posts were taken only in Indonesian-language and also related to performance of Government, especially Ministry of Health. After that, a label is given so that sentiment of tweets is known. To test results of these sentiments, an algorithm is used by comparing two methods of Support Vector Machine (SVM) and Naïve Bayes (NB). Validation was carried out using k-Fold Cross Validation to obtain an accuracy value. The results show that accuracy value for NB algorithm is 66.45% and SVM algorithm has a greater accuracy value of 72.57%. So it can be seen that SVM algorithm managed to get the best accuracy value in classifying positive comments and negative comments related to sentiment analysis towards Ministry of Health. Keywords—Support Vector Machine, Naïve Bayes, Analisis sentimen, K-Fold Cross Validation

Pandemic covid-19 yang terjadi hampir 2 tahun melanda negeri ini yang disebabkan oleh adanya mutasi oleh virus SARS-CoV, membuat perubahan sikap dan prilaku masyarakat menjadi lebih peduli akan kebersihan dan kesehatan. Dalam hal ini penggunaan masker dan Hand Sanitizer menjadi hal yang sangat mendasar dan menjadi kebutuhan primer pada masa pandemi ini. Vending Machine merupakan salah satu bentuk perkembangan teknologi yang digunakan untuk menjual atau menyediakan berbagai macam produk. Finite State Automata (FSA) diterapkan pada Vending Machine masker dan Hand Sanitizer. FSA adalah model matematika yang dapat menerima input dan output dari keadaan yang sama. Metode yang digunakan dalam penelitian ini terdiri dari empat tahap, yang pertama pengetahuan tentang FSA, yang kedua adalah perancangan sistem diagram dalam hal ini peneliti menggunakan aplikasi JFLAP dalam pembuatan diagram FSA, tahapan ketiga adalah pengujian FSA dengan menggambarkan tabel transisinya dan untuk pengujiannya masih menggunakan JFLAP dan yang tahapan yang terakhir adalah proses perancangan desain VM, dalam hal ini peneliti mencoba untuk desain VM menggunakan tampilan yang mudah digunakan oleh pembeli dan merancang sistem pembayarannya dengan 2 metode yaitu tunai dan uang digital. Kesimpulan yang didapat dari penelitian ini adalah penerapan konsep FSA pada VM masker dan Hand Sanitizer dapat melakukan transaksi sebanyak delapan produk yaitu lima jenis produk masker dan tiga jenis produk Hand Sanitizer, penelitian ini juga mencoba mengembangkan dari penelitian VM sebelumnya yang terbiasa menjual satu jenis produk dalam rancangan VM kali ini mencoba untuk menjual dua jenis produk yang berbeda