The Soerakarta Walking Tour (SWT) is an innovative urban tourism initiative that integrates cultural preservation, environmental sustainability, and inclusivity. This study examines how SWT's promotes green tourism through a mixed-methods approach, combining quantitative surveys and qualitative interviews with participants. The quantitative findings reveal high levels of participant satisfaction, including understanding green tourism principles and satisfaction with cultural storytelling. Qualitative data complement these results, highlighting participants’ appreciation for Muslim-friendly facilities, opportunities for cultural learning, and the promotion of local economic and environmental sustainability. The study applies the frameworks of Integrated Marketing Communication (IMC) and Social Marketing to analyze how SWT delivers consistent and engaging messages while fostering behavioral change. The findings show that SWT successfully inspires participants to adopt sustainable practices, though some variability in the visibility of green tourism principles suggests room for improvement.

This study explores the transformative role of Artificial Intelligence (AI) in enhancing customer experience personalization (CEP) and optimizing marketing strategies (MSO) within digital marketing environments. Grounded in the Technology Acceptance Model (TAM), the research incorporates ethical and privacy concerns (EC) as moderating factors influencing AI adoption and its effectiveness. Using survey data from marketing professionals across diverse industries, the study employs Structural Equation Modeling (SEM) to analyze complex relationships among AI use, CEP, MSO, and marketing outcomes. The findings reveal that AI significantly improves customer engagement and marketing performance by enabling tailored interactions, data-driven segmentation, and campaign optimization. Moreover, ethical and privacy concerns positively moderate these effects, underscoring the necessity of responsible AI practices in sustaining consumer trust and regulatory compliance. By offering empirical insights into the interplay between AI, personalization, strategy, and ethics, this study contributes to both theoretical development and practical guidance for businesses navigating digital transformation. The research highlights AI’s strategic potential in creating sustainable competitive advantage while advocating for ethical safeguards in technology-driven marketing.

Piping systems are critical components across various industrial sectors, including agriculture. The 90° elbow plays a key role in directing fluid flow; however, abrupt changes in flow direction within the elbow often lead to pressure losses and vortex formation, particularly in configurations with small curvature radii. This study aims to analyze the fluid flow characteristics within a 90° elbow under different curvature radii (extreme, R30 mm, and R50 mm) and various valve opening conditions, using numerical simulation based on Computational Fluid Dynamics (CFD) in ANSYS Fluent 2019 R3. The simulation results show that increasing the curvature radius leads to a more uniform pressure distribution along the bend and a reduction in vortex formation. Although larger valve openings increase flow velocity and Reynolds number, the pressure and velocity contours do not exhibit significant changes in flow patterns. This study confirms that elbow geometry design greatly influences the hydrodynamic efficiency of piping systems and can help minimize the risk of damage due to uneven pressure distribution

This study analyzes the performance of the Logistic Regression algorithm in facial image classification using a combination of color and shape features. These features are used to extract key information to distinguish individuals. Logistic Regression was chosen for its simplicity and ability to produce probabilities, despite the availability of more complex algorithms. The dataset includes variations in facial expressions and lighting conditions. The results show that combining color and shape features improves classification accuracy to 80%, with a recall of 88% and a precision of 75.86%. However, the algorithm shows limitations when dealing with high-complexity data. This study contributes to the development of more efficient facial recognition systems and offers insights into the strengths and limitations of Logistic Regression in image classification tasks.

The COVID-19 pandemic requires banks to be able to provide electronic-based transactions, carry out digital transformation, and also open up opportunities for financial technology-based companies. An example of this transformation is the first Sharia-based digital bank such as Aladin Digital Bank. This study aims to analyze the effect of perceived usefulness, perceived credibility, and features on the decision to use Aladin Digital Bank in the Pekalongan Regency and City in the New Habit Era. This study uses primary data with 20 statement items distributed through Google Forms and questionnaires with quantitative methods to the people of the Pekalongan Regency and City who have used the Aladin Digital Bank application. The number of samples was 97 respondents using the purposive sampling method. The data analysis technique uses multiple linear regression analysis with the help of SPSS version 26 software with a significance level of 5%. The results showed that the perceived usefulness variable had a positive and significant effect partially, while the perceived credibility and feature variables had no positive and significant effect partially. However, the variables of perceived benefits, perceived trust, and features have a positive and significant impact simultaneously.

The application of machine learning, particularly the *Convolutional Neural Network* (CNN) method, has experienced rapid development in the medical field. Through the process of image data grouping, CNN is capable of classifying based on the similarity of characteristics and attributes of the images. This capability is highly beneficial in the healthcare sector, especially in differentiating chest X-ray images. This study aims to evaluate the DenseNet architecture while applying it to classify Covid-19 disease by utilizing 98 chest X-ray images as training data. The test results showed that the average *loss* value on the training data was 0.8621, with an average accuracy of 0.6950. Meanwhile, the average *loss* value on the validation data was recorded at 1.0682, with an average accuracy of 0.6399. From these results, it can be concluded that the model performed better on the training data than on the validation data, indicating the occurrence of *overfitting*. Therefore, further adjustments to the model are necessary to improve its performance on the validation data

Micro, Small, and Medium Enterprises (MSMEs) are an important sector in Indonesia's economy. However, many MSME actors still face challenges in managing financial reports in a simple and effective manner. One solution is to utilize an accounting application based on Microsoft Excel, which is easy to operate and requires no additional costs. This community service activity aims to provide training and mentoring for MSME actors in using Excel-based accounting applications to record transactions, prepare simple financial statements, and monitor the financial condition of their businesses. The implementation of this activity was carried out online through Zoom Meetings and WhatsApp Groups. The methods used included socialization, interactive online training, and online mentoring. The results of the activity showed that participants were able to understand the basic concepts of accounting records and were capable of using a simple Excel application to record daily transactions, prepare income statements, and cash flow reports. This activity is expected to improve financial literacy and the independence of MSME actors in managing their business finances

Landslides are a frequent natural disaster in the Karanganyar area, especially in hilly areas with high rainfall and fragile geological conditions. This community service activity aims to provide technical assistance on gabion installation as a landslide mitigation effort in Dusun Sukuh, Ngargoyoso District, Karanganyar Regency. The method includes lectures, demonstrations, and direct practice, covering preparation stages, material selection, and correct installation techniques. The results showed a significant increase in participants' understanding, from an average pre-test score of 32% to 70% in the post-test. This indicates that the program effectively improved the knowledge and skills of the community regarding gabion installation techniques. The community showed active participation and high enthusiasm, reflecting their awareness of the importance of disaster mitigation efforts. This activity is expected to support the sustainability of community-based disaster risk reduction efforts in the region.

Toko Ahmad Adam adalah toko sembako yang menyediakan kebutuhan pokok seperti beras, minyak goreng, mie instan, dan gula. Pengelolaan stok yang masih dilakukan secara manual menyulitkan pemilik toko dalam menetapkan prioritas pengadaan barang. Ketiadaan sistem klasifikasi stok menyebabkan ketidakseimbangan antara kebutuhan dan ketersediaan barang, sehingga berisiko terjadi kekurangan atau kelebihan stok. Penelitian ini bertujuan membangun model klasifikasi stok guna mengoptimalkan pengelolaan persediaan. Metode yang digunakan adalah algoritma Naive Bayes dengan input berupa jumlah stok, frekuensi barang masuk, dan data kategori barang. Model klasifikasi ini menghasilkan pengelompokan barang ke dalam dua kategori, yaitu laku cepat dan kurang laku, berdasarkan kecepatan habisnya stok dalam periode waktu tertentu. Hasil klasifikasi digunakan sebagai dasar pengambilan keputusan pengadaan barang. Pendekatan ini mampu menyajikan informasi relevan untuk membantu pemilik toko dalam menentukan barang yang perlu segera diisi ulang maupun yang pengadaannya dapat ditunda, sehingga pengelolaan persediaan menjadi lebih efisien dan efektif.

Abstract: This research systematically concentrates on the effect of different dyeing temperatures on the mechanical properties of AISI 1045 steel which has an important role in the field of mechanical engineering. These findings are important for the development of engineering components such as shafts and gears. This study aims to analyze the effect of quenching temperature variations on the hardness, tensile strength, and ductility of AISI 1045 steel experimentally. Heat treatment was carried out through samples up to an austenization temperature of 850°C with a holding duration of 30 minutes, followed by flash dipping (rapid cooling) using water media at temperature variations of 5°C, 15°C, 25°C, and 35°C. Each treatment was carried out five times and statistical analysis was given to obtain representative data. Observation of microstructures using serial light microscopy was carried out including evaluation. The results showed a close relationship between the decrease in coolant temperature and the increase in hardness and tensile strength. However, there is an opposite correlation with material durability. The highest hardness, 58 HRC, and the strongest tensile strength, 1250 MPa, were obtained for samples cooled at 5°C.  Microstructure observations show a more dominant martensite formation at cooler immersion temperatures. The results of this study provide insight into the correlation with the transformation of the mechanical and microarchitectural characteristics of AISI 1045 steel.