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Drug-Related Problems (DRPs) are common in patients with type 2 Diabetes Mellitus (T2DM) who receive combination therapy involving oral antidiabetic agents and insulin. This literature review aims to identify the main types of DRPs and the factors contributing to their occurrence based on recent studies from PubMed, ScienceDirect, ResearchGate, and Google Scholar. The most frequently reported DRP is drug interaction, followed by underdosing, overdosing, therapy without indication, untreated indications, adverse drug reactions, and patient-related problems such as non-adherence. Several factors contribute to the occurrence of DRPs, including polypharmacy, inappropriate dosing, the presence of comorbidities, limited patient knowledge regarding therapy, and inadequate clinical monitoring. These conditions may increase the risk of poor glycemic control and treatment failure. Therefore, effective management of DRPs is essential to improve therapeutic outcomes. The findings of this review emphasize the important role of pharmacists in conducting medication reviews, identifying DRPs early, and implementing appropriate interventions to enhance patient safety and optimize treatment in T2DM patients.

Drug-Related Problems (DRPs) are common in patients with type 2 Diabetes Mellitus (T2DM) who receive combination therapy involving oral antidiabetic agents and insulin. This literature review aims to identify the main types of DRPs and the factors contributing to their occurrence based on recent studies from PubMed, ScienceDirect, ResearchGate, and Google Scholar. The most frequently reported DRP is drug interaction, followed by underdosing, overdosing, therapy without indication, untreated indications, adverse drug reactions, and patient-related problems such as non-adherence. Several factors contribute to the occurrence of DRPs, including polypharmacy, inappropriate dosing, the presence of comorbidities, limited patient knowledge regarding therapy, and inadequate clinical monitoring. These conditions may increase the risk of poor glycemic control and treatment failure. Therefore, effective management of DRPs is essential to improve therapeutic outcomes. The findings of this review emphasize the important role of pharmacists in conducting medication reviews, identifying DRPs early, and implementing appropriate interventions to enhance patient safety and optimize treatment in T2DM patients.

This study aims to analyze the effect of wages and work environment on employee performance at PT. Rezeki Surya Gasindo. The background of this research is based on the importance of decent wages and a conducive work environment as factors that can enhance employee productivity and work quality. This study employs a quantitative method with a causal associative approach. Data were obtained through the distribution of questionnaires to all employees of PT. Rezeki Surya Gasindo and measured using a Likert scale. The data analysis techniques used in this study include validity and reliability tests, R-square test, and hypothesis testing through bootstrapping with SmartPLS 3 software. The results indicate that wages have a positive and significant effect on employee performance, and similarly, the work environment also has a positive and significant effect on employee performance. Simultaneously, wages and work environment are proven to have a significant effect on employee performance. These findings suggest that the company should pay attention to providing appropriate wages and creating a comfortable work environment in order to optimize employee performance.

This study aims to know the management of the Mosque Prosperity Board (BKM) of At- Tawwabin Mosque, Jalan Pimpinan, Medan City, in community economic empowerment. The background of this research is based on the need to optimize the function of mosques not only as places of ritual worship but also as centers of social and economic empowerment. In practice, the management of mosque assets such as parking areas and the organization of Ramadan stalls has become a strategic effort implemented by BKM to improve the welfare of the surrounding community. However, the implementation of these policies also creates social dynamics that require professional and equitable management.This research employs a qualitative approach with a descriptive method. Data were collected through field observations, in-depth interviews with the chairman and administrators of BKM, parking attendants, and Ramadan stall traders, as well as documentation of mosque activities. Data analysis was conducted through data reduction, data presentation, and conclusion drawing, supported by triangulation techniques to ensure data validity.The findings indicate that the management of BKM At-Tawwabin has implemented planning, organizing, actuating, and controlling functions in managing mosque-based economic programs. Parking management provides employment opportunities for local residents, while Ramadan stalls significantly increase the income of small traders during the holy month. Overall, participatory and structured management enables the mosque to function not only as a center of worship but also as a center of economic empowerment that contributes meaningfully to community welfare.

This study aims to analyze the level of student satisfaction in paying tuition fees (UKT) through the BRImo application at Universitas Negeri Gorontalo. The research applies the service quality (SERVQUAL) framework, which consists of five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. A qualitative descriptive approach was employed, with data collected through in-depth interviews, direct observations, and documentation. Participants included students who actively use the BRImo application for UKT payments as well as staff responsible for tuition management. Data analysis followed a systematic process involving data reduction, data display, and drawing conclusions to ensure accuracy and clarity of findings. The results indicate that using the BRImo application for tuition payment offers significant convenience and efficiency, allowing students to complete transactions without physically visiting the university payment office. Despite these benefits, several challenges were identified, including failed transactions, delays in payment confirmation, and suboptimal responses from customer service staff. Overall, the study concludes that student satisfaction with tuition payments via BRImo is considered fairly good. Nevertheless, improvements in reliability and responsiveness are recommended to further enhance the user experience and optimize the quality of service provided.  

Tanaman padi merupakan salah satu komoditas pangan utama di dunia yang memiliki peran penting dalam menjaga ketahanan pangan global. Namun, produktivitas tanaman padi seringkali terganggu oleh berbagai penyakit daun yang dapat menurunkan kualitas dan hasil panen secara signifikan. Deteksi penyakit secara dini menjadi langkah penting untuk mengendalikan penyebaran penyakit tersebut. Metode konvensional yang mengandalkan pengamatan manual memiliki keterbatasan dalam hal kecepatan, akurasi, serta ketergantungan terhadap keahlian manusia. Oleh karena itu, diperlukan suatu sistem deteksi otomatis yang mampu mengidentifikasi penyakit secara cepat dan akurat. Penelitian ini bertujuan untuk mengembangkan model deteksi penyakit daun padi menggunakan metode YOLO (You Only Look Once) berbasis Deep Learning. Dataset yang digunakan diperoleh dari Kaggle yang terdiri dari citra daun padi dengan anotasi bounding box. Proses penelitian meliputi tahap pengumpulan data, preprocessing, pelatihan model, serta evaluasi performa. Model dilatih menggunakan optimizer Stochastic Gradient Descent (SGD) dengan parameter tertentu untuk memperoleh hasil yang optimal. Evaluasi kinerja model dilakukan menggunakan metrik mean Average Precision (mAP), precision, dan recall. Hasil penelitian menunjukkan bahwa model YOLO mampu mendeteksi penyakit daun padi dengan tingkat akurasi yang tinggi serta waktu deteksi yang relatif cepat. Hal ini menunjukkan bahwa metode YOLO efektif untuk diterapkan dalam sistem pertanian cerdas. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan teknologi deteksi penyakit tanaman berbasis kecerdasan buatan.

Advances in digital technology have transformed the paradigm of faith proclamation, requiring the Church to optimize social media as a relevant catechetical instrument. This study aims to examine how the use of the TikTok platform can be optimized as an effective means of creative catechesis in connecting complex faith teachings for people in the digital era. The research method used is descriptive qualitative with a literature study technique, where data is collected from various scientific literature, Church documents, and relevant library sources. The research findings indicate that TikTok is an effective digital space for catechesis because it is supported by attractive audio-visual features, interactive features such as stitches and comments that enable two-way dialogue, and an algorithmic system that expands the reach of proclamation. The strategy of catechesis through short videos has proven to be able to change the perception of faith teaching that has become rigid to a more personal spiritual experience that is easily understood by all levels of society, especially the younger generation. The implications of this study emphasize the need for the Church to consistently adapt to digital culture and increase content creativity to ensure the continuity of inclusive evangelization amidst the dynamics of modern developments.

This study aims to analyze the contribution and effectiveness of Local Tax and Local Retribution revenues to the Regional Original Revenue (PAD) of Bantul Regency during the 2021–2024 period. This research employs a quantitative descriptive approach using secondary data obtained from the Budget Realization Report (LRA) of Bantul Regency. The data analysis techniques used include contribution ratio analysis and effectiveness analysis by comparing realization with predetermined targets. The results show that the contribution of Local Taxes to the PAD of Bantul Regency during the 2021–2024 period is classified as good, with an average of 45.12%. Meanwhile, the contribution of Local Retribution to PAD is categorized as very low, with an average of 6.37%. In terms of effectiveness, Local Tax revenue is considered very effective with an average effectiveness rate of 109.18%, while Local Retribution revenue is categorized as quite effective with an average effectiveness of 92.42%. Based on these results, it can be concluded that Local Taxes are the most dominant and reliable source of PAD in Bantul Regency, while Local Retribution, although quite effective in achieving its targets, still requires optimization. Therefore, efforts are needed to optimize the collection of local retributions through the expansion of retribution objects, the review of more realistic targets, and the improvement of regional revenue management quality.

This study aims to analyze the effectiveness of corporate zakat management in reducing economic inequality from the perspective of muamalah. The research employs a library research method with a qualitative-descriptive approach by examining various literature sources, empirical data, and regulations related to corporate zakat in Indonesia. The findings reveal that corporate zakat management achieves optimal effectiveness when zakat funds are distributed through a productive zakat scheme. This scheme is implemented through six main stages: preliminary surveys of beneficiaries, regular assistance, provision of business capital, business partner guidance, motivational training, and periodic evaluations of beneficiaries’ business development. However, practical implementation still faces several challenges. The allocation of funds for business capital among economically disadvantaged communities has only reached approximately 0.4%, while the majority of zakat funds, around 97.1%, are still utilized for consumptive assistance. This condition indicates that the economic empowerment function of zakat has not yet been fully optimized. Therefore, integrative solutions are required, including institutional strengthening through the establishment of Islamic microfinance units such as Baitul Maal wat Tamwil (BMT) internally, as well as stronger government regulations through tax deductible incentive policies externally to support the sustainable optimization of corporate zakat distribution.

This study aims to analyze the influence of Regional Original Revenue (PAD), General Allocation Fund (DAU), and Special Allocation Fund (DAK) on Capital Expenditure, with Budget Surplus (SiLPA) as a moderating variable in district and city governments across Banten Province for the 2020–2024 period. The background of this research is driven by the high regional dependence on central government transfer funds and the suboptimal allocation of capital expenditure as a key indicator of regional development. This study employs a quantitative approach using secondary data from Local Government Budget Realization Reports (LRA), analyzed through Multiple Linear Regression and Moderated Regression Analysis (MRA) using statistical software. The results demonstrate that PAD has a significant positive effect on capital expenditure, whereas DAU and DAK do not show any significant influence. Furthermore, the interaction test reveals that SiLPA is unable to moderate the relationship between PAD, DAU, or DAK and capital expenditure. These findings indicate that regional fiscal independence, through the optimization of PAD, serves as the primary determinant for capital investment. The implications of this study suggest that local governments should further optimize local revenue potential to reduce dependency on transfer funds and improve budget management efficiency to support sustainable regional development.

This research is motivated by the vital role of clean water services and the need to optimize operational revenue at BLUD UPT Water Supply Management in Batam City. The study aims to analyze the influence of willingness to pay, ability to pay, and payment awareness on BLUD revenue achievement. A quantitative associative approach was employed, involving a population of 29,047 customers. A sample of 100 respondents was determined using the Slovin formula, and data were analyzed via multiple linear regression using SPSS software. The results indicate that, simultaneously, all three independent variables significantly affect revenue achievement with an F-value of 56.825 and a significance of 0.000. Partially, willingness to pay (t=8.279), ability to pay (t=11.020), and payment awareness (t=10.435) also exert a significant influence. These findings imply that higher community payment behavior directly increases institutional revenue. It is recommended that BLUD improves service quality and payment socialization to ensure sustainable revenue achievement.

The Village Fund Direct Cash Assistance Program (BLT-DD) is a social protection instrument designed to help low-income families meet their basic needs. However, its implementation often faces challenges such as targeting inaccuracies, limited data, and varying administrative capacity at the village level. This study aims to evaluate the implementation of BLT-DD using William N. Dunn’s six policy evaluation criteria through a Systematic Literature Review (SLR) approach, covering effectiveness, efficiency, equity, responsiveness, conceptual alignment, and implementation. The findings indicate that BLT-DD is fairly effective in helping households maintain consumption, yet its effectiveness and adequacy remain constrained by the amount of assistance and the quality of data collection. Program efficiency and responsiveness are relatively good in villages with sufficient governance capacity, while equity emerges as the weakest aspect due to inaccurate data verification. Conceptually, BLT-DD aligns with the needs of low-income communities, but implementation accuracy still requires reinforcement. Furthermore, the study highlights the need for more systematic monitoring mechanisms, cross-sectoral data integration, and administrative training for village officials to enhance service quality. Overall, BLT-DD provides valuable support but requires improvements in governance and data systems to optimize outcomes, including regular evaluation strategies and refinement of supporting regulations.

Hypertension is a chronic disease whose management is substantially determined by an individual's capacity to access and utilize health-related information. Inadequate health literacy may lead to poor medication adherence and diminished quality of life. This study aimed to examine the association between health literacy, medication adherence, and quality of life among hypertensive patients at Polresta Sleman. An observational analytic quantitative design with a cross-sectional approach was employed, involving 80 respondents selected through purposive sampling. Data were collected using the HLS-ID-SF12, MARS-5, and WHOQOL-BREF instruments. Bivariate analysis was conducted using Pearson correlation, while multivariate analysis applied multiple linear regression. Results revealed that health literacy significantly correlated with medication adherence (r = 0.469; p < 0.05) and quality of life (r = 0.447; p < 0.05). Medication adherence also demonstrated a significant correlation with quality of life (r = 0.615; p < 0.05). Simultaneously, both variables accounted for 41.1% of the variance in quality of life (R² = 0.411; p < 0.05). These findings underscore the importance of jointly improving health literacy and medication adherence as an evidence-based strategy to optimize quality of life in hypertensive patients.

Hypertension is a chronic disease whose management is substantially determined by an individual's capacity to access and utilize health-related information. Inadequate health literacy may lead to poor medication adherence and diminished quality of life. This study aimed to examine the association between health literacy, medication adherence, and quality of life among hypertensive patients at Polresta Sleman. An observational analytic quantitative design with a cross-sectional approach was employed, involving 80 respondents selected through purposive sampling. Data were collected using the HLS-ID-SF12, MARS-5, and WHOQOL-BREF instruments. Bivariate analysis was conducted using Pearson correlation, while multivariate analysis applied multiple linear regression. Results revealed that health literacy significantly correlated with medication adherence (r = 0.469; p < 0.05) and quality of life (r = 0.447; p < 0.05). Medication adherence also demonstrated a significant correlation with quality of life (r = 0.615; p < 0.05). Simultaneously, both variables accounted for 41.1% of the variance in quality of life (R² = 0.411; p < 0.05). These findings underscore the importance of jointly improving health literacy and medication adherence as an evidence-based strategy to optimize quality of life in hypertensive patients.

The advancement of digital technology has significantly transformed organizational decision-making, particularly in modern leadership contexts that demand rapid and data-driven responses. Artificial Intelligence(AI) has emerged as a strategic technology capable of enhancing accuracy, speed, and effectiveness in decision-making through comprehensive data analysis. This study aims to analyze the role of AI in supporting leadership decision-making and its implications for organizational effectiveness using a narrative literature review approach. Secondary data comprising peer-reviewed national and international journal articles were analyzed to identify patterns, themes, and interactions between AI, leadership, and decision-making processes. The findings indicate that AI functions not only as a data analysis tool but also as a strategic element that strengthens leaders’ capabilities in evidence-based decision-making, improves team coordination, and optimizes organizational processes. Thematic synthesis identified three main domains analytics and predictive capabilities, leadership strategies, and implementation challenges that form the basis for integrating AI into managerial practice. This study contributes theoretically by expanding the digital leadership and technology-based decision-making framework and practically by providing guidance for organizations to optimize AI utilization to enhance decision quality and efficiency. The research also offers directions for future empirical studies to explore AI-leadership interactions across various organizational sectors, supporting more adaptive, effective, and data-driven decision-making in the digital era.

Parking tax collection as part of Specific Goods and Services Tax (PBJT) is an important source for increasing Local Own-Source Revenue (PAD). This policy is based on Law Number 1 of 2022 concerning Financial Relations between the Central Government and Regional Governments and is implemented through Regional Regulation of Bandar Lampung City Number 1 of 2024. This study aims to examine the implementation of parking tax collection as PBJT as well as the supporting and inhibiting factors in its execution. The research employs normative juridical and empirical juridical approaches with a descriptive qualitative method. Data were collected through documentation studies and interviews with the Regional Revenue Management Agency (BPPRD) of Bandar Lampung City as the implementing institution. The results show that the implementation of PBJT on parking services in Bandar Lampung City uses a self-assessment system, where parking operators calculate, collect, report, and remit taxes independently through e-SPTPD and SIMANTAP managed by BPPRD. Supporting factors include clear regulations, the utilization of information technology, growth in the parking sector, and inter-agency support. Meanwhile, inhibiting factors include suboptimal taxpayer compliance, limited human resources within BPPRD, technical issues in the reporting system, and fluctuations in revenue due to seasonal factors. Overall, the implementation of PBJT on parking services has been effective but still requires improved supervision to optimize regional tax revenue.

This article discusses innovations in cash waqf management through the integration of digital technology with the SatuWakaf.id platform developed by the Indonesian Waqf Board (BWI). The urgency of this research is driven by the need for a transformation in waqf governance to optimize the potential of the Sharia economy in the era of disruption. This study employs a literature review method with a qualitative approach to examine the role of digitalization in enhancing efficiency, transparency, and public participation in waqf. The findings indicate that digital waqf provides ease of access, transaction flexibility, and transparency through a real-time reporting system that is accountably accessible to donors (wakif). Furthermore, the integration of technologies such as QRIS, e-wallets, and internet-based systems effectively addresses various constraints of conventional waqf management, including limited reach and complex administration. However, significant challenges remain, such as low public literacy, limited technological competence among waqf managers (nazir), and cyber security risks surrounding digital transactions. Consequently, the optimization of digital waqf requires strong synergy between human resource development, adaptive regulatory strengthening, and the development of inclusive technological infrastructure to ensure that national waqf potential is fully utilized for the welfare of the community.

This study examines the role of Artificial Intelligence (AI) in enhancing student collaborative learning, with a particular emphasis on AI-driven feedback mechanisms and patterns of student interaction in developing effective collaborative skills. Unlike prior studies, this research highlights the mediating effect of AI-driven feedback on teamwork efficiency and overall learning outcomes in collaborative environments. An explanatory quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure robust data analysis. Data were collected from 112 university students who were actively engaged in AI-assisted collaborative learning activities, using a structured online survey instrument. The data were subsequently analyzed using SmartPLS software. The results reveal that AI significantly enhances student interaction (β = 0.534, p < 0.000) and improves problem-solving feedback (β = 0.620, p < 0.000), both of which contribute to significantly strengthening collaborative skills (β = 0.716, p < 0.000). However, the findings also indicate that AI alone does not directly improve collaboration without the support of structured pedagogical design and guidance. Therefore, universities should strategically integrate AI-driven feedback into Learning Management Systems (LMS) and strengthen digital literacy initiatives to optimize the effectiveness and sustainability of AI in collaborative learning contexts.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

Mental health issues among university students have become a growing concern, driven by academic pressures, career uncertainties, and complex social transitions. However, a large proportion of students remains reluctant to seek professional psychological support due to social stigma and limited access to institutional counseling services. This study proposes the design and implementation of an Android-based chatbot application that integrates mood tracking and sentiment analysis to continuously monitor the emotional states of university students. The system employs a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) model trained on the EmoContext conversational dataset (SemEval-2019 Task 3), comprising 30,160 labeled three-turn dialogue instances across four emotion classes: angry, happy, sad, and others. The model was fine-tuned for four epochs using the AdamW optimizer with a learning rate of 2e-5 and a maximum sequence length of 128 tokens. Evaluation on a held-out validation set of 6,032 samples yielded an overall accuracy of 88.28%, a macro-average F1-score of 0.87, and a weighted-average F1-score of 0.88. Per-class F1-scores were 0.89 (angry), 0.83 (happy), 0.91 (others), and 0.86 (sad). The classified emotion is transmitted in real time to the chatbot response logic, which generates empathetic replies and personalized relaxation recommendations based on the detected mood. Primary data collection through questionnaires and interviews with 62 and 19 university students respectively confirmed the need for accessible digital mental health support. The results demonstrate that RoBERTa-based fine-tuning on conversational data provides a reliable foundation for real-time emotion-aware mental health chatbot systems.