Regardless of the importance of accreditation and the role faculty play in a such process, not much attention was given to those in dental colleges This study aimed to explore faculty perceptions of accreditation in the College of Dental Medicine and its impact, the challenges that hinder their involvement in accreditation, and countermeasures to mitigate these barriers using a convergent mixed methods approach. The interviewees were faculty who hold administrative positions (purposeful sample). The remaining faculty were invited for the survey using convenience sampling. Quantitative data were analyzed by Mann-Whitney and Kruskal-Wallis tests at 0.05 significance. A consensus was achieved on the positive impact of accreditation with an emphasis on the collective responsibility of faculty for the entire process. Yet their involvement was not duly recognized in teaching load, promotion, and incentives. Quality Improvement and Sustainability Tools and Benchmarking were identified as common themes for the value of accreditation to institutions and faculty. Global ranking and credibility as well as seamless service were key themes for institutional accreditation, while education tools and guidance or unifying tools were central themes for faculty. Regarding the challenges, five themes were recognized: Lack of Resources, Rigorous Process, Communication Lapse, Overwhelming Workload, and Leadership Style and Working Environment. To mitigate these challenges, Providing Enough Resources and Leadership Style and Working Environment were the identified themes. This research endeavors to achieve a better understanding of faculty perceptions to ease a process that requires commitment, resources, and readiness to change.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

The research aims to examine East Nusa Tenggara (NTT) bank service digitalization innovations and examine several implications of bank service digitalization innovations. This research uses a qualitative approach with data collection techniques: in-depth interviews, documentation, and focused discussions. The key informants in this research were the board of commissioners, directors, division heads, and NTT bank employees. The findings of this research are, first, the existence of an existing/generic model in the operational, supporting, and monitoring fields of NTT banks. Second, there is an innovation model for digitizing services and efforts to popularize the digitization of NTT bank services to the government-private sector, including micro, small, and medium enterprises (MSMEs), religious institutions, educational institutions, students and students as well as the broader community to provide easy access to sources of financing for the community, Eliminate regional tax leakage, encourage the development of micro, small, and medium enterprises (MSMEs) and assisted village farmers/breeders, provide entrepreneurial opportunities for the community, namely as a digital agent for NTT bank, minimize fraudulent behavior (shirking) in credit distribution. Third, service digitalization innovation uses a contextual sociolinguistic approach because it incorporates local and global vocabulary such as Bpung Mobile, Bpung Farmer, Lopo Dia Bisa, and Bpinjam. Fourth, service digitalization innovation refers to OJK regulations regarding banking digital transformation contained in RP 21 and PBI number 23/26/2021. Fifth, conventional services (hybrid approach) still accompany the digitalization innovation model. Sixth, Bank NTT is in quadrant III, namely growth. Bank NTT continuously optimizes existing resources by taking advantage of opportunities to increase business growth and continues to mitigate threats into opportunities and strengths. The implications of the innovation in digitizing NTT bank services include updating standard operating procedures (SOP), changing corporate culture from Flobamora to Bintang, and accelerating the increase in human capital capacity. The implications of research on bank management refer to the innovation of procurement of new IT systems. Banks can increase their attention to service quality and maintain customer trust to maintain the quality of digital banks among customers. Moreover, with post-COVID-19 conditions that require people to make digital transactions. With the changes in the financial industry towards digitalization, it is necessary to strengthen risk management in financial service institutions. The implications of the research results for policymakers need to be considered in the transformation towards digital banking related to equitable internet access in Indonesia, cybersecurity, and employment. Recommendations for future research are the importance of studying the determinants of digital service innovation in bank services, such as transformational leadership style, good corporate governance, and organizational commitment.