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This research is motivated by the importance of critical discourse analysis in the Representation of Indonesian Leaders Post Covid-19. This study aims to reveal the representation of the Indonesian government after covid-19 through covid-19 news. This research method uses a critical paradigm that focuses on multilevel methods. Discourse analysis in research also focuses on the ideology carried by online media. The approach in this study uses an approach developed by van Dijk. Research data is sourced from COVID-19 news throughout 2020. The accuracy of this research is also maintained because it uses layered search sources according to the critical discourse analysis approach in accordance with Fairclough's theory. The results of the discourse analysis show that CNN and BCC have similarities in representing the handling of Covid-19 in Indonesia, namely: the Indonesian government, under Jokowi's leadership, is not able to handle Covid-19 properly. From the point of view of critical discourse analysis, such representations contain partiality. This strengthens van Dijk's (2013) opinion that a text contains dominance and partisanship. In fact, according to Holmes & Castañeda (2016), representation can be considered as truth. The representation provided by CNI and BCI is also motivated by social elements, as stated by Fairclough (2013) that discourse is always related to social elements, including power, ideology, institutions, and other identities.

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

This research aims to evaluate the impact of Information Technology (IT) on Modern Accounting Systems. The research background reflects a shift in the traditional accounting paradigm towards the increasingly rampant application of information technology. The research method used is literature analysis and case studies. Research findings reveal that IT implementation in modern accounting systems not only increases the efficiency and accuracy of data processing, but also makes a significant contribution to strategic decision making and risk management. The implications of this research include the need to develop IT competencies for accounting professionals, as well as the importance of information security policies in managing financial data. Thus, this research provides insight into the crucial role of IT in the transformation of modern accounting systems and highlights important aspects that need to be considered in facing technological developments in the accounting field.    

This research uses the Support Vector Machine (SVM) algorithm to predict Persebaya Surabaya's ranking in BRI Liga 1. The data used includes goals scored, goals given away, total end-of-season points, and status as champions. The results of the analysis using Orange software show that Persebaya Surabaya does not necessarily become a champion if it has a point value of 42 and an SVM value of 41. To become a champion, Persebaya Surabaya must score 69 points or more in a season and achieve an average of more than 54 goals per season. The suggestion of this research is to have more data so that the results of data processing using Orange software are more optimal and accuracy is more precise.

This webinar discusses the importance of technological innovation in accounting information systems to enhance the efficiency and effectiveness of financial management among Micro, Small, and Medium Enterprises (MSMEs). By leveraging technologies such as cloud computing and integrated accounting information systems, MSMEs can improve transparency, accuracy, and speed in financial reporting, ultimately supporting better strategic planning and decision-making. The findings from this webinar reveal that many MSMEs still rely on traditional methods to assess financial health, which are inadequate to face the current economic challenges. The speakers emphasized the importance of good risk management and effective branding strategies to achieve sustainable business growth. By adopting new technologies and implementing prudent financial management, MSMEs can maintain their competitiveness in the dynamic global market.

One form of text that can express emotions is lyrics. Lyrics are a type of literary work expressed in the form of words, the contents of which can express the songwriter's personal feelings, thoughts, and emotions. Therefore, the lyrics can be used as an object of research on the classification of emotions. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results. This research was carried out systematically and the results were measurable. Descriptive qualitative research was used in this research. The results of identification based on case studies and statistics show that the reviews of popular topics are identical. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results.

Fraud detection is used in various industries, including banking institutes, finance, insurance, government agencies, etc. Recent increases in the number of fraud attempts make fraud detection crucial for safeguarding financial information that is confidential or personal. Many types of fraud problems exist, including card-not-present fraud, fake Marchant, counterfeit checks, stolen credit cards, and others. An ensemble feature selection technique based on Recursive feature elimination (RFE), Information gain (IG), and Chi-Squared (X2) in concurrence with the Random Forest algorithm, was proposed to give research findings and results on fraud detection and prevention. The objective was to choose the essential features for training the model. The Receiver Operating Characteristic (ROC) Score, Accuracy, F1 Score, and Precision are used to evaluate the model's performance. The findings show that the model can differentiate between fraudulent transactions and those that are not, with an ROC Score of 95.83% and an Accuracy of 99.6%. The F1 Score of 99.6%% and precision of 100% further sustain the model's ability to detect fraudulent transactions with the least false positives correctly. The ensemble feature selection technique reduced training time and did not compromise the model's performance, making it a valuable tool for businesses in preventing fraudulent transactions.

Digital Population Identity (IKD) is a digital-based location data innovation through a mobile application with a photo or QR Code. The government's objective is to reduce the physical prints of KTP as well as the use of blank KTP-el in the hope of administrative efficiency. ICT is integrated with health services, education, banking, and taxation, facilitating public access in the era of technological development. However, in remote areas, limited internet access and minimal socialization raise concerns about the security of digital identity data being considered. Social media, especially YouTube, is a channel platform used by the public to convey opinions, opinions and comments about ICTs. So that's why sentimental analysis is needed using the Naive Bayes algorithm to help understand public opinion. The tests were conducted using Orange on 1,561 data showing accuracy, precision, recall, and F1 above 90%. The results of this analysis can serve as a guide for staff in interacting with the community for the implementation of Digital KTP through IKD, as well as improving services regarding the applications provided.

This research aims to develop and modernize the employee payroll administration system at PDAM Batiwakkal Berau through the implementation of the Payroll System Website (SIMPEG). Previously, the process was conducted manually, leading to various challenges in the efficiency and accuracy of payroll data. In addressing these issues, we designed and implemented SIMPEG using the PHP programming language. The results include a significant improvement in the efficiency of the payroll process, minimizing human errors, and ensuring data accuracy. The use of SIMPEG also provides easy access and data management, making it an effective solution for modernizing employee administration at PDAM Batiwakkal Berau. It is expected that the findings of this research will contribute positively to enhancing performance and transparency in employee payroll data management.

The aim of this study is to find out how to implement the use of Artificial Intelligence (AI) in the formation of laws and regulations in Indonesia. This study uses a normative juridical method with a statutory and regulatory approach as well as secondary legal materials, namely books on Legislation, other books related to this case and scientific journals from previous research. The study results show that Al can still be utilized in the process of forming regional regulations without eliminating or even replacing the role and function of the regional regulation forming organs. The use of AI in the process of forming regional regulations is only limited to tools that can be used during the harmonization process in the stages of drafting regional regulations. The use of AI is said to have better accuracy and speed in predicting potential disharmony between regional regulations and various other laws and regulations.

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.

Indonesia is entering a critical period for mental health. Research results from the The Indonesia National Adolescent Mental Health Survey (2022), around 15,5 million Indonesian teenagers experience mental health disorders. Students are part of late adolescence and are vulnerable to mental disorders. The binary logistic regression model is used to examine in more depth what variables have a significant effect. So, this research aims to predict mental health of students in the Faculty of Health Sciences, Pekalongan University. This type of research is observasional with a cross-sectinal design. Data were collected using the SRQ-20 via the Google Form platform using simple random sampling of 186 students. There were 130 students who indicated mental health disordes (69,9%). Simultaneously age, gender, major, semester level, mother’s educational level, father’s educational level, social support and dependence on using smartphone influence student’s mental health status (P Value<0,05). Even though only a few variables were partially significant, the precision percentage of the model that could be predicted correctly was 71,5%. The accuracy of the predicted model is quite good, namely student mental health status (y) = -3,720 + 2,403 (Major) – 1,980 (Mother’s Educational Level) + 1,444 (Father’s Educational Level) + 0,888 (Dependence on using Smartphone). Promotive and preventive interventions such as further screening and education to support student’s healthy mental health.  

The purpose of this study was to determine the effect of Self-Efficacy, Hardiness, and Learning Environment on Academic Stress in Students of SMKN XX. The research method used is a survey method with a quantitative approach. The population in this study were students in grades X, XI, and XII of SMKN XX totaling 210 students. The sampling technique used is the probability sampling method with the Slovin formula with an accuracy of the difficulty level of 5% so that a sample of 100 students is obtained. The data analysis technique used in this study uses validation and reliability tests which are calculated using IBM SPSS (Statistical Package for Social Science) software version 24. The results of this study indicate that there is a positive and significant effect on interest in learning, there is a positive and significant effect between the effects of self-efficacy, hardiness, and learning environment together on interest in learning.

This research aims to evaluate the effectiveness of linear regression as a forecasting tool to estimate the Provincial Minimum Wage (UMP) in Indonesia. Utilizing UMP data from various provinces during the period 2002-2022, this study employs linear regression to analyze the factors influencing UMP determination. The predicted UMP for North Sumatra in 2023 demonstrates a high level of accuracy (R-squared = 0.9678), affirming the potential of linear regression as an effective tool to understand regional economic dynamics. The research provides a crucial foundation for policymakers in regional economic planning and suggests avenues for further investigation, including exploring alternative prediction methods and analyzing the impact of UMP regulation policies.

Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.

The use of standard language is used in writing news. This research aims to identify and analyze writing errors made by journalists in writing news on internet media, especially on CNBC Indonesia. The research method used is descriptive qualitative, focusing on Indonesian writing errors from the aspects of spelling and diction. The research results show that there are several errors that often occur in news writing on CNBC Indonesia, such as inappropriate use of punctuation, letter writing errors, and word writing errors. These errors can confuse readers and create ambiguity in understanding. Recommendations that can be given are to increase accuracy in the use of punctuation marks, letters and words. Journalists need to pay attention to correct writing rules and double-check before publishing news. Apart from that, the editorial team also needs to provide training and guidance to journalists to improve the quality of news writing. It is hoped that the results of this research can contribute to improving the quality of CNBC Indonesia's online news writing and reducing ambiguity in reader understanding. This is important to maintain media professionalism and provide clear and accurate information to readers. In this way, it is hoped that CNBC Indonesia's online news can become a reliable source of information that is easy for readers to understand.

This research allows an automated system for detecting classified means of transportation in Medan City traffic using the YOLOv8 algorithm. The YOLOv8 algorithm is used to detect transportation objects with accuracy that is many times better than other object detection algorithms and with good accuracy after training with various data sets. The use of this algorithm provides an effective solution for handling congestion in the form of increasing the number of vehicles and less orderly traffic users in the city of Medan. The placement of each transportation object in the image to be tested by the system has an influence on the shape accuracy of the object detection results by the algorithm.

This community service was conducted on Jalan Jembatan Merah I and Jalan Wisata Babarsari. The purpose of this community service is to improve the quality of financial record-keeping, ensuring accuracy and correctness, to provide an accurate overview of the financial condition of UMKM Siomay Indul and Gudeg Jogya Mak Karti. This community service is focused on enhancing the quality of the financial reports of these UMKMs through understanding and implementing proper accounting practices in financial record-keeping. With the provision of training and mentoring, it is expected that the stakeholders of traditional culinary UMKMs can optimize their business potential. The results of the community service indicate that improvements in transaction recording systems and a better understanding of accounting standards can significantly enhance the quality of financial reports, providing the clarity needed for better decision-making in business management.

Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.

This research explores the use of Text Mining Methods as an innovative approach to extract important information from research report text. With a strong conceptual foundation from the literature review, this research details the key concepts of Text Mining, such as tokenization techniques, sentiment analysis, and entity extraction. The steps of applying Text Mining Methods to research reports are explained in depth, with a focus on using such techniques to improve the efficiency and accuracy of information extraction. Through the evaluation of the method's performance, the research demonstrates a significant improvement in analysis speed and information extraction accuracy compared to conventional methods. The research conclusions provide a holistic picture of the potential of Text Mining Methods in improving the effectiveness of the research report text analysis process. The implications of this research stimulate thoughts on the applicability of this technology in various disciplines that rely on research reports as a primary source of information. Thus, this research makes a positive contribution to the understanding and development of text analysis techniques to support more efficient decision making.