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The purpose of this research, "Designing a Database System for Senior High Schools/Vocational Schools in Nabire Regency," is to build an information system that will make the process of data processing and management for schools in Nabire Regency easier. In this era of rapid technological advancement, effective and open information systems are becoming increasingly important, especially for educational purposes. It is hoped that the Ministry of National Education (Kemendiknas) will develop an information system with school profile data across Indonesia. This will assist data-driven decision-making for various parties, including the government and the education department. With this system, it is hoped that the administrative process will become more effective and transparent, and policy-making will become easier. In addition, this research emphasizes the importance of ensuring that all stakeholders can access data in order to improve the quality of education in the area. This study shows that the application created not only makes data management easier, but also enhances clarity and integrity in public education services. Therefore, it is hoped that this system will become a useful tool to promote education and assist in better decision-making processes in Nabire Regency. This research significantly contributes to the development of educational information systems in Indonesia, particularly in terms of school data management.

This study compares the performance of two deep learning models, namely Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN), in the task of recognizing human activity from videos. Human activity recognition is an important field in computer vision with many applications, such as security monitoring, human-computer interaction, and social media-based video analysis. ConvLSTM is a model that combines convolution operations with long-term memory LSTM, thus capable of capturing spatial and temporal information simultaneously. This approach is ideal for processing video data sequences that have spatial and temporal dimensions. On the other hand, LRCN combines the power of spatial feature extraction from Convolutional Neural Network (CNN) and temporal sequence modeling through Recurrent Neural Network (RNN), specifically LSTM, to understand movement patterns in videos. The study used the UCF50 dataset consisting of 50 activity classes, but was limited to five classes for the focus of the experiment. The dataset was divided into 80% for training and 20% for testing, and the model was drilled for 50 epochs using early stopping to prevent overfitting. The results show that both models have high training performance. ConvLSTM achieved a training accuracy of around 98% and a validation accuracy of 90%, while LRCN achieved a training accuracy of 99.5% and a validation accuracy of 88%. Although ConvLSTM demonstrated good stability on the validation data, further testing using TikTok videos as real-world data showed that LRCN had a higher confidence level in recognizing activities, with most predictions achieving confidence scores above 80%. This difference in performance indicates that while ConvLSTM excels in generalizing on training data, LRCN is more robust to real-world data variations.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

Information technology is a technology used to manage data, including processing, acquiring, organizing, storing, and manipulating data in various ways to produce high-quality information—namely, information that is relevant, accurate, and timely. This information is used for personal, business, and governmental purposes, serving as strategic information in decision-making. To anticipate changes in weather conditions, particularly rainfall, a valid and accurate report is needed that can be useful for the public. So far, the correlation or relationship between the factors influencing weather conditions—especially rainfall—has not been precisely determined, making it mathematically difficult to create a model that can describe the correlation among all these factors. This is where Artificial Neural Networks (ANN) come into play: to create such models and map out the existing problems purely based on the input data provided. One of the capabilities of neural networks is to make predictions based on previously learned data using the backpropagation method.

This study aims to examine the extent to which social media is utilized in agribusiness learning among students at Universitas Adzkia. A quantitative descriptive approach was employed, using a Likert scale-based questionnaire as the research instrument. The collected data were tabulated and classified into score percentages to determine the category of social media utilization. The research was conducted from June to July 2024 using a census method, involving all 38 students enrolled in the Agribusiness Study Program at Universitas Adzkia. The findings indicate that overall, the use of social media in agribusiness learning falls into the moderate category, with a score percentage of 55.76%. When broken down into agribusiness sub-systems, the results show that the use of social media in the agricultural input supply sub-system was 55.76%, in farming activities 56.14%, agricultural product processing 49.67%, marketing 44.08%, and agribusiness support services 47.20%. These results suggest that although social media has been used in the learning process, its utilization is still at a moderate level and not yet evenly distributed across all agribusiness sub-systems. This presents both opportunities and challenges for lecturers and educational institutions to develop more innovative, technology-based learning strategies. To enhance the effectiveness of agribusiness learning, it is recommended that lecturers actively encourage students to use social media in a productive manner. This could be implemented through the assignment of structured tasks that require the use of social media platforms for academic discussions, information searches, and the creation or sharing of relevant agribusiness content. Such efforts are expected to make social media not only a tool that enriches students' learning experiences but also a means to deepen their understanding of the dynamic and ever-evolving field of agribusiness. Ultimately, integrating social media into learning can support the development of digital competencies and practical insights among agribusiness students in higher education.

This study aims to determine the effect of company size, company age, profitability, and leverage on the level of website-based corporate information disclosure. The population of this study was all companies listed on the Indonesia Stock Exchange. The sampling technique in this study used a purposive sampling method with a final sample of 688 companies. Data collection in this study used a content analysis method and data processing using the SPSS 27 application using multiple linear regression analysis. The results of this study indicate that search size has a positive and significant effect on the level of website-based corporate information disclosure. Meanwhile, the other three independent variables, namely company age, profitability, and leverage, do not have a significant effect on the level of website-based corporate information dissemination.

The implementation of Supply Chain Management (SCM) makes many businesses manage information, goods, services and money as good as possible so they can provide fast and high-quality services to customers. Therefore, this study aims to implement SCM in Kopi Bintang Salatiga SMEs to decrease production costs, increase revenue, improve operational efficiency and establish good relationships with customers. This study discusses the SCM process and the added value in Kopi Bintang Salatiga SMEs so in the future Kopi Bintang Salatiga SMEs can have good risk management and increase added value. The data was collected through literature study and interviewing the owner of Kopi Bintang Salatiga SMEs. This study found that weather, natural disasters, fires and coffee pests are the factors with the highest risk in the SCM process of Kopi Bintang Salatiga SMEs. The added value of farmers & post-harvest coffee processors of Kopi Bintang Salatiga Kopi Bintang Salatiga SMEs is already quite high at 99%, while at grade I coffee sales is 71% and grade II is 47% it appears that coffee with grade II has lower added value, so it is needing accuracy of coffee processing at farmers & post-harvest coffee processors so the grade I coffee product is increased.

Advances in science and technology have significantly improved and facilitated the work of health professionals. One example is the transition from manual medical records to Electronic Medical Records (EMDR). At Mitra Sejati Hospital, the implementation of EMR still uses manual and electronic methods, so it is not in accordance with the Minister of Health Regulation Number 24 in 2022. This study aims to analyze the implementation of EMR in the mitra sejati hospital on the regulation. The research method used is descriptive qualitative, with observations made using checklist sheets to evaluate the SIMRS application. The research subjects included doctors, nurses, radiology, IT officers, and medical record officers, each represented by one participant. The results of the study showed that the implementation of EMR at Mitra Sejati Hospital was in accordance with the Minister of Health Regulation Number 24 in 2022 about filling in clinical information, inputting financing claim data, and storing EMR. However, the implementation of patient registration, EMR data distribution, information processing, quality assurance, and transfer of EMR contents were not fully in accordance. This discrepancy indicates that the implementation of EMR in the hospital was not fully in accordance with the regulations. To address this gap, special efforts are needed to improve technical systems and staff training, so that it can ensure full compliance with regulations and optimize the function of the EMR.

This research aims to simplify the management of dr. Chasbullah Kota Bekasi    hospital outpatient service data. so that work becomes effective and efficient, in addition to the design of this application can also help solve work problems related to reports that can be completed quickly, precisely and accurately. The research method used in this Hospital outpatient service application system is Grounded Research which is based on facts and uses comparative analysis with the aim of conducting empirical generation, establishing concepts, proving theories, developing theories, collecting data and analyzing data at the same time. In addition, the author also collected data through direct observation, interviews, literature studies, and documentation to obtain the required information. After conducting the research, the author concluded that the Hospital Outpatient application system can help data processing become more effective and efficient.

The effectiveness of accounting information systems (AIS) plays a crucial role in sup-porting the smooth recording, processing, and reporting of reliable and timely finan-cial information. AIS effectiveness is a key concern for cooperative managers in en-hancing operational efficiency and decision-making quality. This study aims to empiri-cally examine how work experience, educational background, and managerial support influence the effectiveness of AIS implementation. The research was conducted at savings and loan cooperatives operating in the South Kuta District. The sampling method used was non-probability sampling with a purposive sampling technique, and data were collected from 54 cooperative employees. Multiple linear regression analysis was employed to process the data. The results indicate that work experience, education level, and managerial support have a positive and significant effect on the effectiveness of AIS implementation. These findings suggest that the higher the em-ployees’ work experience and education, and the stronger the support from manage-ment, the more effective the AIS implementation within the cooperative environ-ment.

This research is motivated by the existence of problems in the management of movable assets owned by Margajaya Village which are not yet optimal. The purpose of this study is to determine the optimization of movable asset management in Margajaya Village, Sukadana District, Ciamis Regency. The method used in this study is descriptive analysis. There are 6 informants. Data collection techniques are literature studies, field studies (observations and interviews) and documentation. The author uses qualitative data analysis techniques through processing data from interviews and observations to draw conclusions so that they can answer the problems in the study. Based on the results of the study, it is known that: Optimization of movable asset management in Margajaya Village has been implemented but has not been optimal in accordance with the principles of asset management, namely the functional principle, legal certainty, openness, efficiency, accountability, and certainty of value. This shows that the movable assets owned by the village have been used but have not been managed effectively to support the implementation of government duties and community services. There are several obstacles faced in the management of movable assets, including limited competent human resources, inadequate supporting facilities and infrastructure, lack of socialization and understanding of regulations, limited community participation in supervision, and budget constraints for asset maintenance and development. Efforts have been made by the Margajaya Village Government together with related parties to overcome these obstacles, including conducting training and increasing the capacity of village officials, implementing a digital-based asset management information system, socializing and assisting with asset management regulations, increasing transparency and community participation, and optimizing village budget management.

This study aims to review the literature on the effects of using ChatGPT on postgraduate students' competencies. The method applied in this study is a literature review of 420 journals from 500 journals. References were obtained from the Emerald Journal, Elsevier and Google Scholar by utilizing the Publish or Perish application. Data processing uses the VOSviewer application. With this literature review, it was found that.In the network visualization data, it was found that the widest link in the largest Red cluster was GPT, Chat GPT, Ability, Artificial Intelligent, Student, Chat, Higher education which was related to Postgraduate-student. In the overlay visualization based on the year, it was found that the projection of the year of collection at the time of the study. It can be seen from the darkest keywords, namely ability, student, skill, which have a longer research projection. Meanwhile, the research projections included in the latest research are with the keywords conversation, generative ai, education, and research. The results of Density Visualization show that the keywords ChatGPT, Ability, Higher Education, and Skill are included in the most frequently conducted research results. Meanwhile, the keywords generative AI, research, challenge, opportunity, conversation, artificial intelligent are included in the least conducted research. This shows that there is a significant relationship between the keywords mentioned.The recommendations given include strengthening the policies implemented in each university in each country for the use of AI as a medium for information and data processing. In addition, training on plagiarism in the preparation of various research journals and final assignments for postgraduate students needs to be intensified. The results of this study are expected to provide insight and breadth to related agencies that these opportunities can be further developed in Indonesia.  

This research is motivated by several factors that cause voter behavior in the regional head election in Banjar City. The purpose of this study is to determine voter behavior in the Regional Head Election in Banjar City. The method used in this study is descriptive analysis. There are 7 informants. Data collection techniques are literature studies, field studies (observations and interviews) and documentation. The author uses qualitative data analysis techniques through data processing from interviews and observations to draw conclusions so that they can answer the problems in the study. Based on the results of the study, it is known that: voter behavior in Banjar City is still dominated by the tendency of traditional voters and skeptical voters. This is evidenced by the problem of many voters who determine their choices based on following suit if most of their environment chooses a candidate pair, in addition, voters in determining their political choices are based on information obtained from their parents or from their environment, there are voters who are reluctant and lazy to pay attention to political contestation. There are obstacles to voter behavior in the regional head election in Banjar City, including unequal access to information so that people only get political information from unverified social media. In addition, the practice of money politics is still a reality that is difficult to avoid, where direct assistance or certain promises are the main considerations in choosing candidate pairs as well as past experiences. To overcome various obstacles, a number of efforts have been made, including the Banjar City General Election Commission (KPU) implementing a voter education program through direct outreach to the community, especially in villages and densely populated areas.

The development of the banking sector has led to an increase in the processing of customers' personal data, which can pose a risk of data misuse. One of the efforts to prevent this is the implementation of data minimisation, which emphasises that the personal data collected must be relevant, limited, and consistent with the legitimate purposes of processing, as well as transparent in accordance with the provisions of Article 27 of Law No. 27 of 2022 on Personal Data Protection (PDP Law). This study employs a normative legal analysis method with a descriptive-analytical approach, incorporating transformative legal theory and privacy rights protection to examine the application of the data minimisation by banks. The findings indicate that excessive data collection practices still frequently occur and may violate customers' privacy rights. Therefore, banks are required to disclose the types and relevance of customer data, conduct regular mapping and evaluation of the data required, and implement selective and secure information technology systems. This implementation constitutes banks' compliance with data protection principles, particularly security and accountability. Additionally, active customer involvement and oversight by authorities are crucial factors in ensuring the effectiveness of data minimisation implementation in the banking sector.

This study aims to design and develop a pipeline leakage detection system based on the Internet of Things (IoT) that provides real-time notifications and determines the location of leaks with high accuracy. Pipeline leakage is a serious issue, as it can lead to water wastage, environmental damage, and high maintenance costs. Therefore, a system that can detect leaks quickly and accurately is crucial for improving the efficiency of pipeline infrastructure management. The system developed in this study uses an ESP32 microcontroller, Waterflow sensor, and GPS module. The ESP32 microcontroller serves as the central processing unit that processes the data received from the Waterflow sensor and the GPS module. The Waterflow sensor detects changes in water flow that indicate a leak in the pipeline. When an abnormal reduction in flow is detected, the sensor sends a signal to the microcontroller. The GPS module then provides location coordinates, pinpointing the exact location of the leak, allowing the maintenance team to quickly address the issue. Additionally, the system is integrated with the Blynk application, which enables remote monitoring through a mobile device. The Blynk application provides a user interface that facilitates the monitoring of pipeline status and delivers notifications whenever a leak is detected. Testing results show that the IoT-based leakage detection system is capable of identifying leaks and sending real-time information with good accuracy. With this system, the process of identifying and addressing pipeline leaks can be done faster and more efficiently, ultimately reducing the losses caused by leakage. The system also offers a more effective solution for pipeline maintenance, improving the reliability of water distribution systems and reducing water resource wastage.

Manual stock management systems are still commonly used in electrical equipment sales businesses, often leading to problems such as inaccurate data recording, delayed reporting, and potential data loss. To overcome these issues, a web-based inventory management information system was developed to automate the recording of incoming goods, outgoing goods, damaged items, and lost items. A case study was conducted at Toko Dhanisa Mandiri Jaya, which previously relied on manual bookkeeping using physical logs. The system was developed using the Waterfall method, involving stages such as requirements analysis, system design, implementation, testing, and maintenance. Data collection was carried out through observation, interviews, and literature review. The system includes key features such as inventory data management, transaction processing, reporting, and user management. The implementation results indicate improved operational efficiency, reduced recording errors, and the availability of real-time inventory data to support managerial decision-making. This system is expected to help the store modernize its business processes with better accuracy and control.

This research seeks to develop a web-based tuition payment system to improve the efficiency of financial administration at SMK Techno Media.The Agile development method was employed, with data collected through observation, interviews, literature review, and documentation. Several challenges were identified, such as delays in processing, inaccuracies in payment information, and inefficiencies in financial reporting. The developed system includes functionalities for generating bills, tracking payment status, sending notifications, and producing financial reports. These features are designed to be utilized by six user roles: administrators, teachers, students, parents, finance staff, and the school principal. The implementation results show notable improvements in data accuracy, information transparency, and administrative efficiency. Furthermore, the system supports digital transformation in the school’s financial management processes.

In the context of improving the quality of early childhood education, educational management plays a central role. Effective and efficient management is able to manage various existing resources optimally so as to create a conducive and quality learning environment. In addition, management is also responsible for designing learning programs that are in accordance with the needs of child development, supervising, evaluating, and providing training and professional development for educators. Thus, the role of management is not only administrative, but also strategic in improving the quality of early childhood education services. This research is a type of literature study. Library Research refers to a series of broad and systematic activities that are closely related to various sophisticated methodologies used in careful library data collection, comprehensive reading and accurate recording of relevant information, and complex processing of various research materials. Based on the results of the literature, the data collected shows that the role of management in improving the quality of early childhood education (PAUD) is very complex and multidimensional. The studies reviewed underline the importance of comprehensive planning, including the development of relevant curricula, the provision of adequate facilities and infrastructure, and the management of quality human resources. In addition, efficient organization, effective program implementation, and structured supervision have also been shown to contribute significantly to improving the quality of PAUD.

This study aims to conduct an analysis of the management information system implemented at the community health center (Puskesmas). In fulfilling its operational functions, Puskesmas typically utilizes a management information system to support data processing and the delivery of healthcare services. To enable healthcare personnel to perform their duties more effectively and efficiently, the system must operate in an optimal condition. Ensuring such efficiency requires a systematic evaluation to identify existing issues, thereby allowing for targeted improvements and further system development. The evaluation was carried out using a heuristic evaluation approach focused on usability aspects, referencing ten standard indicators. Data were collected through the distribution of questionnaires to clinical staff at Puskesmas. The results of the analysis identified 15 usability-related issues distributed across 4 key usability aspects. These issues were subsequently categorized into low and medium priority levels for improvement. The findings from this study may serve as a foundation for system refinement, feature enhancement, and overall development of the management information system at Puskesmas Depok I, Sleman, ultimately aiming to improve user experience in terms of accessibility and operational convenience.

Personal data protection is a crucial issue in the digital era, which is characterized by the processing and widespread dissemination of information on the internet. In this context, the different legal approaches between Indonesia and the United States raise questions regarding the effectiveness and scope of privacy protection in each country. The focus of this research is to analyze the legal systems applicable in both countries to identify the strengths, weaknesses, and potential for cross-system policy adoption. Using a normative juridical method and a comparative law approach, analysis is conducted on key regulations such as Law No. 27 of 2022 in Indonesia as well as various sectoral regulations in the United States. The findings show that Indonesia has integrated regulations but faces challenges in implementation, while the United States has more established enforcement despite its sectoral and fragmented nature. The synthesis of these two approaches emphasizes the importance of finding a balance between regulatory comprehensiveness and enforcement effectiveness. In conclusion, efforts to strengthen personal data protection in Indonesia can be directed towards strengthening institutions and oversight, while the United States can draw lessons from its centralized regulatory model to improve consistency of protection across sectors.