This study aims to analyze the implementation of circular economy in Micro, Small, and Medium Enterprises (MSMEs) in the Food & Beverage (F&B) sector. The focus of the research is on the implementation of circular economy practices that include waste management, use of sustainable raw materials, environmentally friendly packaging, energy and water efficiency, and food waste reduction strategies. The research method used a descriptive quantitative approach with a research instrument in the form of a questionnaire distributed through Google Form.

Crowdfunding has emerged as an innovative fundraising solution, leveraging digital technology and social media to connect donors with beneficiaries. This study investigates the influence of religiosity, social media engagement, and viral marketing on millennials’ online donation decisions in Jakarta. Religiosity, reflecting spiritual values that motivate donations, emerged as the dominant factor, encouraging contributions as acts of worship and moral responsibility. Social media engagement enhances participation through active interactions like sharing and commenting, while viral marketing supports trust-building with engaging and informative content. Using a quantitative approach, data were collected via an online Likert-scale questionnaire from 196 purposively sampled respondents and analyzed using SPSS version 25. Results show all three factors significantly influence donation decisions. This study highlights the importance of integrating spiritual values into online campaigns and utilizing creative communication strategies to foster donor trust and engagement, emphasizing the synergy between technology and social values for sustainable online donation platforms.

Climate change has negative impacts throughout the countries in the world, especially Indonesia, which has a tropical climate. Successful aging can be achieved by building individual self-confidence through prevention and skin aging. Indonesia is a producer of milkfish waste, reaching 5.4 tons/year, which is in line with the increasing center of "Otak-otak" milkfish souvenirs found in Gresik. The waste, especially fishbones, can be synthesized into collagen. The characteristic fishbone collagen waste are in the form of large porous sheets, while particle size is an essential factor in the absorption effectiveness of cosmetics. Therefore, Nanoemulgel is able to overcome this problem. This research aims to produce best collagen nanoemulgel from milkfish bone by physical evaluation with varying ratios of surfactant: cosurfactant in collagen nanoemulgel. The research method was done experimentally by comparing the components Tween 80: PEG 400 F1 (30:30), F2 (40:20), and F3 (35:15) through qualitative and quantitative physical characteristics using one-way anova. Evaluations whose results differ significantly are pH on the 7th day, spreadability, and viscosity. Research results show F2 is the best formula and meets physical stability specifications : particle size 3.89 nm, PDI 0.1906, zeta potential -0.2 mV, pH 4.7±0.04, viscosity 890.1± 4.26 cps, spreading 5.93±0.23 cm. 

This research aims to determine the role of the Sound System in the liturgy of Sunday services at church. The sound system in a church is an electronic device that is used to amplify and regulate sound so that it can be heard clearly by the congregation. The method used in this research is descriptive qualitative method. The stages of research implementation were carried out through observation, application, interviews and documentation. The object of this research was carried out at the HKI Perumnas Pagar Beringin Sipoholon, North Tapanuli church. In the research process, the author carried out research on the sound system in the church for 3 months, namely from June to September 2024. The stages carried out by the researcher to regulate the role of the Sound System so that it functions according to its use are: recognizing the devices in the Sound System, finding problems with the Sound System , organizing and arranging the Sound System according to its role. The results of this research show that the implementation of the use of the Sound System at the HKI Perumnas Pagar Beringin Sipoholon North Tapanuli church is functioning and can be heard clearly and well, where previously the sound system often occurred storing or buzzing. Based on this research, the author suggests that sound system operators in churches prepare sound system equipment before the Sunday worship program begins.

UPTD Puskesmas Subah adalah salah satu puskesmas yang ada di jajaran Dinas Kesehatan Kabupaten Batang yang selalu berkembang seiring kondisi dan dinamika masyarakat dengan upaya keras untuk menyediakan pelayanan kesehatan yang bermutu, merata dan terjangkau. Seiring perkembangan teknologi yang cepat dan informasi yang dibutuhkan sangat penting, sebab harus dapat diakses dimana saja. Salah satu fasilitas pelayanan kesehatan di UPTD Puskesmas Subah Batang menyelenggarakan pelayanan kesehatan kepada masyarakat, salah satu kegiatannya yaitu pendaftaran pasien yang berfungsi sebagai menerima kunjungan pasien untuk berobat rawat jalan. Selama ini Puskesmas Subah melakukan pelayanan pendaftaran pasien rawat jalan dilakukan bersamaan dengan pendaftaran pasien baru langsung ditempat loket, sehingga terkadang para pasien harus sabar menunggu giliran dan antri untuk mendapat pelayanan masing-masing. Solusi untuk permasalahan tersebut dengan dibuatnya sistem informasi pendaftaran pasien rawat jalan berbasis web dengan menggunakan metode hot-fit. Dengan adanya sistem ini memudahkan pasien dalam proses pendaftaran, memudahkan petugas dalam hal pencatatan dan juga sistem berbasis web memberikan informasi yang cepat dan akurat tanpa mengenal tempat dan waktu.

The background to this research is the importance of finding an integration pattern between the project learning model and Ethno-STEM based inquiry to explain scientific knowledge regarding the aroma of herbal tea.  The fundamental research objective is to design an integration pattern for the project and Ethno-STEM learning model and reconstruct scientific knowledge based on public knowledge of the aromatic volatile compounds of Indonesian and world herbal teas. This research method and approach is mixed research. This qualitative approach is to find the integration of the project learning model and Ethno-STEM (Ethno-STEM PjLM) and its syntax. A quantitative approach regarding analyzing tea aroma volatile compounds using an Arduino gas sensor: research data from literature studies, interviews, reconstruction, and data on identifying volatile compounds in the aroma of herbal tea analyzed descriptively, qualitatively, and quantitatively. The results of the research concluded: (1) the reconstruction and integration pattern between PjLM Ethno-STEM is an integrated pattern with SUDARMIN syntax, (2) the reconstruction of scientific knowledge based on public knowledge regarding the process of isolation, extraction, and bioactivity of secondary metabolites, (3) herbal tea aroma volatiles compounds are identified by Arduino sensor: carbon dioxide, methane, propane, butane, acetone, alcohol, and ester, (4) the implementation of Ethno-STEM PjLM received a positive response from students.

Capital markets play a strategic role in the global economy; however, high volatility often poses challenges for investors in making optimal investment decisions. Stock price prediction has become one of the key elements in effective portfolio management, but the complexity of market data demands more advanced analytical approaches. This study aims to explore the potential of Long Short-Term Memory (LSTM) models in predicting stock price movements and integrating these predictions into portfolio optimization strategies based on modern portfolio theory. The research methodology utilizes historical stock data from the S&P 500 and IDX Composite indices with daily frequency. The LSTM model is compared with Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy. The LSTM model demonstrated superior performance with an MSE of 0.0018, RMSE of 0.042, and prediction accuracy of 91.5%, significantly outperforming RNN (87.3%), SVM (80.2%), and ANN (82.7%). The prediction results were then integrated into portfolio optimization using the Mean-Variance Optimization approach, resulting in an increase in expected returns from 8.5% to 12.3%, a reduction in risk from 6.2% to 5.8%, and an improvement in the Sharpe ratio from 1.37 to 2.12. This study demonstrates that the LSTM model not only excels in stock price prediction accuracy but also contributes significantly to enhancing portfolio management efficiency. This Deep Learning-based approach offers a more adaptive, data-driven investment strategy, supporting more informed decision-making in the dynamic capital market

This study aims to explore the strategies employed by teachers in addressing the needs of students with dyslexia in elementary schools. Specifically, the research seeks to gain an in-depth understanding of the approaches utilized by teachers at SD Negeri 03 Karangbangun when supporting dyslexic students. Data collection methods included observation, interviews, documentation, and data processing aligned with the research focus. To ensure data validity, triangulation techniques were employed by comparing findings from observations and interviews. Data analysis was conducted interactively. The participants involved were fourth- and fifth-grade teachers, as well as a fifth-grade student with dyslexia. The findings of this study indicate that the strategies used by teachers to support dyslexic students in elementary schools include: (1) providing individualized assistance through extended learning time focused on activities such as spelling, reading, and writing; and (2) utilizing information technology-based tools, such as Word Wall or engaging instructional videos, to create a supportive and stimulating learning environment for dyslexic students.

The Tambakrejo Village office has problems in managing village finances in recording and presenting financial reports. Currently, the Tambakrejo village office uses a manual reporting system by handwriting it in a book. Data is stored in archives which may be lost or damaged, requires a long time to search if the data is to be used or viewed again, delays in preparing village financial reports due to ineffective recording and management of financial reports.
The aim of this research is to produce a Financial Accounting Information System Using the Cash Basis Method in Tambakrejo Kendal Village to help handle the recording and processing of village financial reports to make them more effective and computerized. Design and development of an accounting information system using the PHP and CSS bootstrap programming languages, MySQL as a database, with Research and Development research methods.
The implementation of this system has been able to simplify the financial management process of the Tambakrejo Village Office so that financial reports are obtained more quickly, easily and effectively. The financial accounting information system of the Tambakrejo Kendal Village Office, the cash basis method, is useful for handling the process of recording, processing and reporting village cash finances based on proof of cash transactions when received or disbursed and is able to provide accurate information on village financial balances in the form of numbers and is equipped with digital archiving of proof of transactions.

This study explores the application of machine learning (ML) in human resource (HR) management to enhance the recruitment process by predicting high-performing candidates. The research addresses gaps in traditional recruitment methods, which are often time-consuming and susceptible to subjective bias. By employing a Random Forest algorithm, this study utilizes a dataset of 10,000 records, encompassing attributes such as education, work experience, psychometric assessments, and interview evaluations. Data were divided into 70% training and 30% testing sets to ensure robust model evaluation. The findings demonstrate that the Random Forest model achieved a prediction accuracy of 87%, outperforming traditional methods and other ML models like Logistic Regression. The model's ability to identify key attributes contributing to candidate performance underscores its potential for data-driven decision-making in HR management. However, challenges such as data bias, algorithmic transparency, and resistance to technological change were identified as barriers to implementation. This research contributes to the theoretical and practical understanding of ML in HR by offering a predictive model that balances accuracy with interpretability. Practical implications include strategies for integrating ML into existing HR systems, emphasizing the importance of explainable AI to foster trust among practitioners. The study concludes that ML-based recruitment can significantly improve efficiency, objectivity, and the quality of hiring decisions, paving the way for more innovative and strategic HR practices.