Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.

The exchange of information on the Internet requires increased protection to avoid potential threats to privacy and security. This study identified the main issues in this regard: the need for simple and effective tools for encoding and decoding messages, and the need to understand Base64 encoding algorithms and concepts. However, to overcome this problem the author developed an application to encode and decode messages/text using the Base64 algorithm and the Python programming language. This application allows users to send secret messages/text securely via and convert the data into Base64 format for secure transmission via text media. It also covers the basics of cryptography, Base64 algorithms, and how to use the Python programming language to develop secure applications. The result of this research is a simple and effective encryption and decryption application. This application provides a solution for users to protect messages or text when they want to change confidential information by converting it to Base64 format. With this application, you can send secret messages or texts with the confidence that only authorized parties can read them. Implementing message encryption and decryption using the Base64 algorithm using Python is an important step in maintaining message privacy and security in the current digital era. This research succeeded in developing an application suitable for this purpose. Therefore, the next step is to improve the security of your application by implementing stronger encryption algorithms. Additionally, we provide a more comprehensive user guide to help users better understand cryptographic concepts. Further research may focus on integrating applications with broader Internet security protocols to address increasingly complex security threats.

PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.

Public Street Lighting (PJU) plays an important role in transportation infrastructure, especially at night. Currently, complaints about PJU damage are only made using social media. This research designs and builds a web-based PJU complaint system with real-time notifications via WhatsApp in Pemalang Regency. Data was collected through interviews, observations, and questionnaires. This system is built with PHP and MySQL, with WhatsApp notification integration to ensure accurate and real-time complaint information. The system trial involved the community, showing the system's effectiveness in increasing reporting efficiency and officer response. The system provides easy online reporting and real-time notifications via the website and WhatsApp. This system is expected to improve community services and PJU management. The results can be a reference for the development of similar systems in other areas.

Streetboba & Gildak Kediri outlet is a restaurant that serves a variety of Korean food menus and various kinds of drinks with boba and jelly toppings that are sold at low prices that suit the student's budget. This restaurant is located in East Java province which is precisely on Jalan Yos Sudarso No.43, Kediri City. With technological advances that continue to grow to affect various aspects, especially in the business and industrial world. Sentiment analysis is a technology that extracts or manages text to be expressed using text that can also be classified into positive and negative polarity. Consumer reviews are a form of communication that occurs in the sales process, the stage where potential buyers receive an explanation of the product posted and buyers receive reviews that explain the advantages or disadvantages of purchasing the product. In this study, sentiment analysis was conducted based on consumer opinions regarding social media accounts. The study aimed to use social media data to assess the service, cleanliness and quality of products offered by categorizing companies as having positive and negative reviews. To classify sentiment, the Naive Bayes method is used, which combines survey data collection methods, questionnaires, and observation data.

Poverty is a fundamental problem that has become the center of attention in several aspects, for example from the government. The government needs poverty data and analyzes it to determine which poverty alleviation programs should be delivered to the right target or the poor. The aim of this study is to determine the accuracy of the classification of poverty in Batang District using the Neural Network method using the chi square feature selection. The dataset used in this study uses poverty data sourced from the Batang district BPS based on the results of the Susenas survey (National Economic Survey) for the 2022 time period. The results of this study indicate that the accuracy obtained for poverty classification using a neural network is 96.38% , with a precision value of 100%, and a recall value of 89.38%. Whereas when using a neural network with feature selection chi square, it gets an accuracy value of 93.68%, with a precision value of 91.07%, and a recall value of 90.26%. The contribution of this research is to develop a neural network method using feature selection chi square to improve the results of the accuracy of the classification is not poor or poor.  

Penelitian ini bertujuan untuk memperhatikan perkembangan sastra di dunia kesusasteraan. Dimana sekarang berkembangnya teknologi yang mempengaruhi pola hidup dan banyak merubah sistem kehidupan. Begitupun dengan dunia sastra yang mulai muncul dan berkembang kembali memanfaatkan media yang ada melalui teknologi digital. Metode penelitian ini menggunakan metode kualitatif yang dikumpulkan melalui data-data yang ada. Hasil dari penelitian ini adalah memperlihatkan bahwa sastra tetap bisa mengikuti dan berkembang dengan perubahan zaman yang terjadi, tidak pasif dan bisa menjadi trobosan baru dalam dunia kesusasteraan. Bahkan, media sosial yang biasanya hanya untuk tempat bersosialisasi online bagi penggunanya, bisa dijadikan tempat bertumbuhnya sastra cyber.

Project-oriented learning design is an effective pedagogical approach in increasing student involvement and developing critical thinking and problem solving skills. The research method used is library research, which involves collecting information from various sources such as books, notes, magazines, and related articles. Data collection techniques are carried out by reading, reviewing and analyzing various existing literature. The data analysis technique used is content analysis. This article discusses the importance of integrating formative and summative assessments in project-oriented learning design to improve the quality and effectiveness of learning. Formative assessment, which functions as continuous feedback, allows educators to monitor and improve the learning process in real-time. Meanwhile, summative assessment provides a comprehensive evaluation of student learning outcomes. The results show that this strategy not only increases student engagement but also significantly improves learning outcomes. Thus, this article suggests that educators need to adopt an integrated assessment approach to achieve higher quality and effective learning design.

Purposes: With profitability acting as the mediating variable, the study’s objective is to evaluate the impact of implementing green accounting and its environmental implications on firm value in manufacturing companies listed on the Indonesia Stock Exchange between 2020 and 2022.
Methods: The approach used in this study is a quantitative approach. The research sample was 171 cases. Data analysis was performed using path analysis using Eviews 10, and the Sobel test was used to conduct the mediation test.
Findings: This study indicates that green accounting and environmental performance impact can enhance firms’ value. Employing green accounting practices and improving environmental performance impact enhances profitability. The greater the profit, the higher the firm’s value. Profitability may play a role in determining the effect of green accounting and environmental performance impact on firm value.
Novelty: This research supports the idea that profitability can be a mediator in understanding the effect of green accounting and environmental performance impact on the firm value of Indonesian industries listed on stock exchanges. Profitability as a mediating variable requires further investigation, as green accounting practices and improved environmental performance impact can increase operational efficiency, reduce costs, and ultimately improve a business’s profitability. Increased profit makes a company more valuable to investors and other stakeholders.

Inefficiencies in health expenditure result in an estimated 1–1.4% of global Gross Domestic Product (GDP) being wasted across various regions, presenting a significant challenge for countries striving to optimize healthcare spending. This study aims to assess the efficiency of government health expenditure in six ASEAN countries with the lowest per capita government health spending, using the Data Envelopment Analysis (DEA) method. In addition, Tobit regression is employed to identify factors that influence the efficiency of government health expenditure. The results indicate that most countries have not achieved maximum efficiency scores over the years, underscoring the need for improved budget management and reforms in health policy implementation. The Tobit regression analysis reveals that economic growth, urbanization rate, and the proportion of government health expenditure in current health expenditure have a positive and significant effect on efficiency scores. This suggests that increased public investment and urban development contribute to improved efficiency in healthcare spending. Conversely, total carbon emissions have a negative impact on efficiency, emphasizing the influence of environmental factors on health system performance.