ABC Gift Shop provides ordering and purchasing of souvenirs for weddings, birthdays, graduations, and other events. So far, customers are served through online orders via Instagram and offline orders that come directly to the store location. This study aims to look at the implementation of ongoing business processes and see how far the results of the evaluation can identify opportunities for improvement and recommendations for improving ABC Shop's business processes. The research method uses descriptive qualitative through interviews with informants and observations of online and offline ordering services at ABC Stores. The results of the study show that there are deficiencies in the online ordering process, such as online ordering data that is entered manually and recapitulated so that the queue process and order completion take a long time and the queues can even be swapped. Opportunities for improvement and recommendations for improvement, in this case, are being able to improve existing business processes and providing an information system for ordering souvenirs that is more flexible than social media Instagram and according to the needs of Toko ABC..

Di era digital saat ini, kemajuan teknologi informasi mengalami pertumbuhan yang pesat. Salah satu perkembangan yang signifikan terjadi dalam bidang kecerdasan buatan (Artificial Intelligence), yang telah diterapkan luas di berbagai sektor, termasuk analisis data dan pengambilan keputusan. Para peneliti di bidang data mining telah menciptakan beragam algoritma klasifikasi yang meningkatkan proses klasifikasi dengan memanfaatkan atribut numerik dan nominal. Klasifikasi adalah suatu proses analisis data yang menghasilkan model untuk mewakili kelas-kelas dalam data tersebut, seperti yang terjadi pada Decision Tree yang digunakan untuk menganalisis klasifikasi dan pola prediksi data serta menggambarkan hubungan antara variabel atribut  dan variabel target  dalam bentuk struktur pohon. Python, sebagai bahasa pemrograman populer dalam pengembangan kecerdasan buatan, menyediakan pustaka dan framework yang mendukung implementasi algoritma Decision Tree. Dengan menerapkan algoritma Decision Tree untuk klasifikasi data evaluasi mobil menggunakan Python, kita dapat memanfaatkan kekuatan AI untuk memberikan solusi efektif dan efisien dalam pengambilan keputusan terkait evaluasi mobil. Menggunakan dataset Evaluation Car dari UC Irvine Machine Learning Repository, hasil penelitian menunjukkan akurasi sebesar 81%. Confusion Matrix dan laporan klasifikasi menunjukkan performa model yang baik dalam melakukan prediksi.

This research aims to elucidate the design of metaverse learning media with the goal of improving the quality of education. Metaverse is a virtual environment that enables interaction among users through 3D avatar representations. In the context of education, the use of metaverse offers a different learning experience, optimizing the current technology and educational media, and supporting the development of soft skills and building confidence through the simulations offered. This research also discusses the potential and limitations of metaverse educational applications, as well as the challenges and opportunities for their use in the education world. The research findings indicate that the use of metaverse in education can overcome existing limitations, such as classroom capacity, distance, and time constraints. However, the use of metaverse also poses some challenges, such as user security and privacy. Therefore, the design of metaverse learning media needs to consider these aspects to enhance the quality of education.

This study aims to investigate the influence of macro fundamental variables (such as inflation, interest rates, and exchange rates), micro fundamental variables (such as DER, DPR, DR), as well as capital structure variables, financial performance, and ownership structure as intervening variables on firm value in the mining sector on the Indonesia Stock Exchange. This study uses a quantitative approach with secondary data from the financial statements of mining companies listed on the Indonesia Stock Exchange for the period 2010 to 2019. Data collection is done by random sampling of these companies, and data analysis uses the SmartPls 4 application to test the relationship between the variables studied. The results of this study are expected to provide a deeper understanding of how macroeconomic and microeconomic fundamental factors, as well as capital structure, financial performance, and ownership structure, contribute to firm value in the context of the mining sector in Indonesia. The practical implications of this study are expected to provide guidance to stakeholders, including company management, investors, regulators, and academics, in optimizing investment strategies and decision-making in this highly potential sector. This research can also make a theoretical contribution in expanding the understanding of the factors that influence firm value in the mining sector

The background of this study is based on the challenges currently being faced by the Indonesian financial sector in terms of transparency and trust. Various cases that often occur such as data manipulation, fraud, and lack of accountability in the financial transaction process have drastically reduced public trust in financial institutions. Blockchain technology here is present as one of the potential solutions to overcome this problem, by offering a decentralized, transparent, and immutable financial recording system. This study aims to analyze the implementation of blockchain technology in increasing transparency and trust in the Indonesian financial sector. The research method used is qualitative with the Systematic Literature Review (SLR) approach. This research was conducted by collecting and analyzing relevant literature related to the implementation of blockchain in the financial sector, especially in Indonesia. The analysis process was carried out using the content analysis method in order to identify the benefits, challenges, and impacts of blockchain implementation on transparency and public trust. The results of the study show that blockchain has great potential in increasing transparency in the financial sector, by reducing the risk of data manipulation and increasing operational efficiency. However, several challenges are still faced, such as inadequate regulations, uneven technological infrastructure, and low digital literacy in the community. This study emphasizes the need for collaboration between the government, financial institutions, and the community to create an ecosystem that supports the widespread adoption of blockchain technology. With a collaborative approach, blockchain is expected to contribute significantly to strengthening a more transparent, accountable, and highly competitive financial ecosystem in the future. In addition, this study provides an understanding of the readiness of the Indonesian financial sector in facing technological changes and innovation opportunities in the future.

Companies in making a decision require good consideration, therefore we need a system that helps provide information from good data processing so that it becomes the basis for decision making. This analysis aims to create a decision support system that can help the company to provide an overview of the decision to procure a new fleet that the company wants to buy.This decision support information system is implemented using the Hypertext Preprocessor (PHP) programming language and MySsql as the database application so that the desired features are created and neatly stored in operating the information system.This decision support information system also uses the Weighted Product method which is used to assist in decision making in determining the procurement of new fleets.The end result of this decision support information system is to combine a programming language with the Weighted Product method which can be used for decision making in determining which new fleet to buy, where the calculations are based on several predetermined criteria so that their use is on target.

Executive Information Systems (ISE) have become an integral part of modern corporate management, providing executives with relevant and immediate information to support decision making. This journal discusses the implementation of the Executive Information System in the employee payroll process using the Microsoft Visual Studio 2010 development environment and SQL Server database. This research includes the design, development and implementation of an Executive Information System application for the employee payroll department. The system development methodology focuses on requirements analysis, application design, coding, testing, and implementation. Microsoft Visual Studio 2010 is used as the primary development environment, providing powerful tools for building intuitive and efficient user interfaces. SQL Server database is used as an integrated and secure data storage. This application is designed to manage payroll information, including employee personal data, salary history, salary deductions and bonuses. The advantages of this system include the ability to provide real-time information to executives, increase the efficiency of human resource management, and support faster and more accurate decision making in the context of employee payroll.

In the digital era, universities play an important role in developing policies and technology to ensure students' academic integrity. This research aims to explore and analyze holistically the contribution and effectiveness of universities in maintaining academic integrity in the digital era. The research method used is a literature review with a semi-systematic review approach, which is then expanded with bibliometric analysis using the VOSViewer tool. Of the 115 papers identified in the bibliometric analysis with the keyword “college plagiarism”, it was found that the term “plagiarism” was often associated with “student”, “college”, “academic”, and “plagiarism”. Key relevant terms include “training”, “locus of control”, “rabin-karp”, “text mining”, “hash”, “levenshtein distance algorithm”, “similarity”, and “publication”. Universities should conduct training and strengthen students' locus of control to instill integrity values. The concept of “similarity” in plagiarism detection helps maintain the originality of academic work, while “rabin-karp” and “text mining” technologies are used to recognize plagiarized text and find suspicious patterns. The “hash” concept and “Levenshtein distance algorithm” are important in maintaining academic integrity, and “publication” ensures research meets academic integrity standards. Universities leaders are responsible for preventing plagiarism by implementing code of ethics and promoting awareness and understanding within the entire academic community.

This study aims to develop, determine the feasibility of android-based learning media. This type of research uses research and development (Research and Development) methods with the Borg & Gall development model consisting of 5 main stages which include: a) conducting preliminary research,   b) developing the initial product with Kodular Creator, c) conducting product validation, d ) conducting trials, and e) making the final product. Data collection techniques used are questionnaires and interviews
 Keywords: Borg & Gall, Android-Based Learning Media, Informatics

Nowadays, advances in information and communication technology have had a major impact on various sectors of life, including in the field of trade and e-commerce. However, as it happens in the scope of social media, TikTok Shop is also dealing with challenges and changes. One of the issues that is sticking out and is currently hot is the permanent closure of the TikTok Shop feature that has occurred in Indonesia. This research will carry out several processes starting with data collection, then data labeling, data preprocessing, data sharing, weighting training data, using the Naive Bayes method, and ending with testing. In this study, the results of the implementation that has been made or built are discussed. The method used is the Naive Bayes Classifier Method to classify training data as much as 800 data, which is 80% of the total data. Then, testing is carried out using 200 testing data, which is 20% of the total data. The evaluation results show an accuracy value of 73%. In addition to the accuracy value, this research also recorded the precision, recall, and F1 score values. The classification that appears most often and contributes the highest in these values is the Positive classification as much as 420 data or 42% of the total data used..