Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed.
The general election of Indonesia in the upcoming 2024 will be an interesting topic for social media users, especially Twitter. Currently, Twitter is very influential in building sentiment, preferences, and public politics. So that people's Tweets can be used to see a picture of public opinion. There are various opinions of Twitter users with positive, neutral and negative sentiments. However, classifying the sentiments of Twitter users requires quite a lot of time and effort due to the large number of tweets found. The large number of incoming tweets regarding the election encourages the need for a method that helps to view public opinion effectively. By providing the textblob library, Python, which is a programming language, is able to classify tweet data and can be used to answer these problems. The tweet data is preprocessed first where there are two processes in the initial data, namely the cleaning and stemming processes. After that, a sentiment analysis was carried out to find out how the results of the classification related to public opinion from the 2024 elections and classify them into three classes, namely positive, neutral and negative using Python. The results of this study show that Python performs sentiment analysis with the results of the proportion of positive class sentiments of 40%, 52% neutral and 8% negative about the 2024 elections so that it can be concluded that Python can classify tweets from Twitter so that we can identify public opinion about elections. The general public of Indonesia in 2024 will have neutral opinions tend to be positive
Dar El-Amir Islamic Boarding School is an Islamic boarding school that has been established since 2008 with a total of 140 students over time, a problem arose. Problems that are often encountered are related to the management of student data, teacher data, employee data, homeroom data and report cards which are still conventional, namely by writing using this paper, it can result in the emergence of larger duplicate data and damage to good data such as blurry writing or missing data. To solve these problems, an integrated academic information system must be made in order to facilitate data management and can be web mobile, meaning that it can be used on any device such as a laptop or mobile phone. This study uses the waterfall technique, this technique can be interpreted like a waterfall because there are stages that must be passed. The effect of studies that have been carried out by applying the waterfall technique shows that this academic information system can be useful for the management of student data, teacher data, tu staff data, homeroom data and report cards so as to make the data management process faster, more efficient and effective and help in the process dissemination of information related to the academic activities of Islamic boarding schools.
The Community Service Program which was carried out aimed to train students and teachers of SMPIT Al-Mustofa to use e-learning media Google Classroom during the Covid 19 pandemic. With this training, students and teachers can optimize the use of Google Classroom in the learning process. The implementation of this service consists of three stages, namely: preparation, implementation, and evaluation. The preparation stage was carried out to explore problems related to the potential for e-learning during the Covid 19 pandemic. At this stage, e-learning material was delivered and training on how to access and use Google Classroom. The evaluation phase is carried out to find out obstacles during training. This activity resulted in an increase in the knowledge of students and teachers of SMPIT Al-Mustofa about e-learning and skilled in using Google Classroom as an interactive and fun online learning medium in the process of teaching and learning activities during the Covid 19 pandemic and e-learning products, namely Google Classroom as an easy-to-use online learning medium.
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