The Great Mosque of Tegal City is a historic building that has undergone numerous renovations since its establishment in 1825-1830. These renovations aimed not only to meet the functional and capacity needs of worshippers but also reflected modern architectural trends, occasionally compromising historical elements. This study employs a qualitative approach using documentation, observation, and literature synthesis methods to analyze the mosque's architectural changes. Using John Obert Voll's "continuity and change" theory, the research identifies the preserved original architectural elements and the modifications made. The findings reveal that while several original features, such as the mosque's core structure, remain intact, significant changes in renovations—such as the addition of floors, towers, and alterations to the gate design—have impacted the mosque's historical and aesthetic values. The study recommends the preservation of historical elements to maintain the mosque's cultural identity.

Indonesian Migrant Workers (PMI) abroad often face various social, legal, and cultural challenges that can increase their vulnerability in everyday life. One effort to minimize these vulnerabilities is through the establishment of regional community organizations as a platform for solidarity, advocacy, and cultural identity preservation. This community service activity aims to assist in the formation of the Ikatan Warga Ponorogo di Hong Kong (IWPH) — an association of PMI from Ponorogo working in Hong Kong. The methods employed include community needs assessment, facilitation of organizational meetings, formulation of organizational statutes and bylaws, and basic community-based advocacy training. The results of this activity showed high enthusiasm among PMI from Ponorogo to participate in this community as a forum for sharing information, social protection, and preserving regional cultural traditions abroad. Moreover, IWPH has begun to actively voice issues faced by PMI, such as overcharging, gender-based violence, and unclear employment contracts. The establishment of IWPH is expected to serve as a model for strengthening social networks and advocacy based on regional community groups, enhancing the bargaining power of Indonesian migrant workers abroad. This activity also reflects the contribution of higher education institutions in supporting the protection and empowerment of Indonesian migrant workers in various destination countries.

This article discusses the importance of a multicultural approach in guidance and counseling, in a country rich in cultural diversity. Within this context, counselors are expected to understand and appreciate the cultural differences faced by clients, including different values, norms, and perspectives. Multicultural counseling not only increases the effectiveness of the counseling process, but also creates a safe counseling environment for clients to express themselves. By understanding the client's cultural background, the counselor can minimize potential conflicts and increase confidence in the counseling relationship. This research uses a qualitative method with a literature study to explore the challenges and solutions in multicultural counseling practice. The findings of this article suggest that sensitivity to cultural diversity is essential for successful inclusive and responsive counseling.

This literature review investigates the role of HRM as a strategic factor in boosting institutional performance, competitiveness, and organizational resilience in educational institutions. By analyzing five selected academic articles, the study identifies three core HRM approaches: best practices, contingency, and configurational models. Key focuses include competency development, technological integration, and transformational leadership in fostering a productive and adaptive work environment. Findings reveal that strategic HRM significantly contributes to organizational sustainability amid global challenges. The study recommends a human-centered, data-driven approach to establish a sustainable HRM framework for educational institutions.

The modulation of the structures and electronic properties of transition metal complexes by multi-ligands coordination has been widely exploited to enhance the catalytic?efficiency in organic transformations. Here, we present synthesis, characterization, and catalytic performance of Cu(II), Ni(II) and Pd(II) complexes of multidentate ligands- EDTA, 1,10-phenanthroline and?N-heterocyclic carbenes (NHCs). The Pd-NHC complex displayed the high catalytic activity for hydrogenation as well as?small-molecule activation, up to 450 h?¹ of turnover frequency (TOF) and total conversion of styrene to ethylbenzene at room temperature, and is higher efficient than Cu-EDTA (TOF =120 h?¹), Ni-Phen (TOF= 85 h?¹). The DFT calculation with Pd-NHC system showed that the small HOMO-LUMO gap (3.2 eV) and the short Pd–C bond (1.95 Å) of Pd-NHC,?which could facilitate the transfer of the growth species, contributed to the low energy of activation (28 kJ/mol). Mathematical?model also discussed its high catalytic activity (TOF/Ea = 16.07 h?¹·kJ?¹·mol) due to the strong ?-donor ligands and proper geometry. These findings underscore the influence of ligand architecture to secure transition states and modulate electron density in designing efficient sustainable catalysis that is?industrially relevant.

Skin diseases are health problems that require accurate diagnosis to evaluation and ultimately leading to treatment decisions. One of the crucial roles in the diagnostic process is medical imaging. Machine learning technology can assist in classifying skin diseases using image data and achieving high levels of accuracy in diagnosis. The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. The methodology employed is a Systematic Literature Review (SLR), which can be used to provide a comprehensive review of the application of machine learning in the classification of skin diseases. The literature search strategy was based on the Boolean technique, applied to the Scopus database. The selected articles were screened using predefined inclusion and exclusion criteria. The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN).
Keywords - Skin Disease, Machine Learning, Classification, CNN.

Micro, Small, and Medium Enterprises (MSMEs) have a strategic contribution to economic equality and job creation, especially in rural areas. However, limitations in mastering digital technology, especially in marketing, are still a major challenge for MSMEs in Pepelegi Village, Sidoarjo. This community service activity aims to increase the digitalization capacity of MSMEs through training and mentoring in utilizing Google Maps as a promotional media. The method used in this study is Participatory Action Research (PAR), which includes observation, interviews, training, and evaluation. The location of the activity focused on the MSME Warkop Pojok SR in Pepelegi Village, involving the active participation of local business actors. The results of the activity showed an increase in participants' knowledge and skills in creating and managing business profiles on Google Maps. This has a positive impact on business visibility and the potential for increasing income. Digitalization through Google Maps not only makes it easier for consumers to find business locations, but also provides information regarding operating hours, customer reviews, and business contacts. Thus, this activity is expected to be a model for empowering MSMEs based on digital technology that can be replicated in other areas. The sustainability of the program can be strengthened through cross-sector collaboration to support the acceleration of MSME digital transformation.

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.

This study stems from the growing need to support university students' self-growth during early adulthood, a critical phase in preparing for independence and future careers. The Merdeka Belajar–Kampus Merdeka (MBKM) initiative, introduced by Indonesia’s Ministry of Education, provides diverse learning experiences beyond the classroom that are expected to foster personal development. The objective of this research is to evaluate the impact of the MBKM program on various dimensions of student self-development, including cognitive, emotional, and social growth. Employing a quantitative research method, data were gathered through surveys distributed to students who took part in the program. The questionnaire measured specific indicators of personal development. Results indicate that the MBKM program significantly and positively contributes to the self-development of participants. Students demonstrated enhanced critical thinking, emotional intelligence, and interpersonal as well as leadership skills. These findings suggest that experiential learning opportunities such as those offered by MBKM play a vital role in nurturing students' maturity and preparing them for professional challenges. As a result, universities are encouraged to embed similar programs within their curriculum to promote comprehensive student development..

With the rapid advancement of information technology, maintaining a stable and efficient computer network has become essential, particularly in educational settings. SMK Negeri 1 Surabaya encounters difficulties in managing its limited bandwidth because of the large number of users accessing the network simultaneously. This research focuses on applying bandwidth management through the Queue Tree technique combined with Per Connection Queuing (PCQ) on the school's computer network using Mikrotik hardware. The approach aims to enhance network performance by improving throughput, minimizing delays, and stabilizing jitter. Evaluation of Quality of Service (QoS) indicators—including throughput, packet loss, delay, and jitter—demonstrated favorable outcomes, with all metrics categorized as good. Findings confirm that using Queue Tree and PCQ methods contributes to fairer bandwidth distribution, mitigates network congestion, and improves overall network quality in the educational environment. This study offers a practical solution to support teaching and administrative processes by providing a more reliable and efficient network infrastructure.