International Journal Scientific and Professional

Enhancing Pediatric TB Treatment Compliance: Marketing Strategies for Medication Reminder Devices

2025-04-16T01:15:49+00:00

This research focuses on developing a marketing strategy for a medication reminder device (Alarm Obat) to improve medication adherence among children with tuberculosis (TB) in Indonesia. Tuberculosis remains a significant public health challenge, especially in pediatric populations, where adherence to long-term treatment regimens is critical for successful outcomes. The study explores how Alarm Obat, a device designed to remind children to take their medication on time, can help increase adherence and reduce treatment failures. The research also highlights the potential for this product to be integrated into healthcare programs, focusing on partnerships with healthcare institutions and digital marketing strategies. By targeting families with children suffering from TB, the research aims to demonstrate the impact of Alarm Obat on improving health outcomes and reducing the risk of drug resistance. The study concludes by emphasizing the importance of awareness campaigns and collaborative efforts to ensure accessibility and adoption of the product across diverse communities in Indonesia.

Implementation of the Greedy Algorithm in Phrase Pattern Matching for a Text Recognition System

2025-04-22T14:58:50+00:00

The Greedy pattern-matching algorithm is a phrase pattern-matching method that works by selecting the optimal solution at each step without backtracking. This approach is applied in text recognition systems for keyword search, natural language processing, and automatic text filters. This research analyzes the performance of the algorithm through computational experiments and literature review by evaluating the efficiency of execution time, number of character comparisons, and matching success rate. The results show that the algorithm offers high speed in pattern matching, especially on large datasets, as it is able to optimally shift the search index. However, its accuracy decreases when handling complex patterns or phrases that have many similarities. By combining this algorithm with heuristics or data preprocessing techniques, its drawbacks can be minimized, thus remaining an effective solution in text recognition systems that require fast and real-time processing.

Implementation of the Greedy Algorithm for Optimal Police Patrol Route Search in the Jurisdiction of Semendawai Suku III Police Sector

2025-04-21T14:16:12+00:00

Police patrols represent a strategic effort to maintain public security and order. However, determining an optimal patrol route remains a challenge, particularly in ensuring time and distance efficiency. This study aims to identify the optimal police patrol route in the jurisdiction of the Semendawai Suku III Police Sector using the Greedy algorithm. This method was selected for its ability to rapidly generate solutions by choosing the most favorable option at each step. The data utilized in this research include ten villages identified as high-risk areas based on the number of criminal reports recorded in 2024, as well as inter-village distances collected through regional mapping. The application of the algorithm resulted in a total patrol distance of 121.2 kilometers, following the sequence: Police Sector (A) → Sriwangi (B) → Kerujon (C) → Karang Endah (D) → Margorejo (E) → Taman Agung (F) → Taraman (H) → Kota Tanah (I) → Melati Jaya (J) → Nirwana (K) → Karang Marga (G) → returning to the Police Sector (A). This study contributes to data driven patrol strategy management, enhancing both the efficiency and effectiveness of police operations in maintaining regional security stability.

Utilization of Fibonacci Algorithm to Determine Product Bundling Discounts in Culinary Business

2025-04-30T04:59:06+00:00

Micro, small, and medium-scale culinary businesses (MSMEs) often face challenges in determining product bundling discount strategies due to limitations in data analysis. This study examines the effectiveness of implementing the Fibonacci algorithm in determining food product bundling discounts to increase sales and profitability. The method used is a quantitative experiment with an algorithm simulation approach, where discounts are determined based on the Fibonacci number sequence (1%, 2%, 3%, 5%, 8%, etc.). Data were obtained from the Lumpia Beef Lumer culinary MSME in Bekasi Regency and analyzed using Python, while the results were visualized in a soft Excel document. A comparison graph of the number of sales and income before and after the application of discounts was compiled in Excel based on daily transaction data. The results show an increase in sales volume of 60% and gross income of 60% after this strategy was implemented. The tiered discount strategy based on Fibonacci has proven attractive to customers, encouraging bundling purchases without significantly reducing profit margins. This approach offers a systematic and adaptive data-driven solution and can be used by other MSMEs to develop more effective and sustainable marketing strategies.

Implementing Conditional Random Fields on English Text Grammar Analysis

2025-04-21T10:37:38+00:00

This study explores the implementaion of the Conditional Random Fields (CRF) algorithm in the grammatical analysis of English texts, specifically in the task of Part of Speech (POS) tagging. CRF is a statistical model effective in classifying words into grammatical categories such as nouns, verbs, adjectives, and others. The research methodology includes a literature review and experimental implementation using labeled datasets, integrated into a web-based application. The implementation results demonstrate that the CRF model provides accurate tagging results and can be utilized for sentence structure analysis in English texts. The application is developed using the Python programming language, supported by the NLTK and sklearn-crfsuite libraries, and uses the Flask framework for the user interface. This research is expected to contribute to the development of technology-based tools for English language learning.

Flood Fill and Scanline Fill Algorithm Optimization to Improve Design and Animation Application Performance

2025-04-28T03:40:58+00:00

Flood and Scanline Fill algorithms are two primary methods in the color-filling process in design and animation applications. However, limitations in computational efficiency often cause long rendering times, especially for high-resolution images and complex areas. This study aims to optimize both algorithms by implementing parallel processing using multi-threading technology and GPU-based processing. This implementation is expected to improve color filling performance compared to conventional methods significantly. Testing was carried out by comparing the execution time of the algorithm before and after optimization in various usage scenarios. The results showed that the parallel processing technique accelerated the color-filling process by up to 60% under certain conditions. Thus, this approach improves the efficiency of design and animation applications, especially in real-time rendering.

Implementation of Greedy Algorithm for National Selection of New Students at MAN Insan Cendekia OKI

2025-04-20T09:10:49+00:00

The National Selection of New Learners at MAN Insan Cendekia OKI is a process of selecting the best students based on certain criteria. This selection process requires an efficient method to ensure that the selected participants have qualities that match the school's standards. The Greedy Algorithm is one approach that can be used to solve optimization problems such as learner selection. This algorithm works by taking locally optimal decisions at each stage in the hope of getting an overall optimal solution. This research aims to implement the Greedy algorithm in the Selection of New Learners process at MAN Insan Cendekia OKI. In its application, the Greedy algorithm will be used to select participants based on criteria such as academic scores, non-academic achievements, and other factors deemed relevant by the school. The results of this study show that the Greedy algorithm can be applied well in the selection of students and is able to improve the efficiency of the selection process. However, there are some limitations that need to be considered, especially in terms of dynamic selection criteria and the possibility of non-optimal solutions in certain cases. Thus, the Greedy algorithm provides an interesting alternative in solving selection problems while still considering further development so that the results obtained are more optimal.

Optimizing Sprint Planning in Agile Methodology Using Greedy Algorithm

2025-04-25T07:20:32+00:00

Sprint planning is a pivotal process in Agile-based software development, where project success heavily depends on the team's ability to select and deliver the most valuable tasks within limited time and resources. A core challenge in this process is determining the optimal set of tasks that can be completed in a sprint, considering the constraints imposed by story point capacity. This decision-making problem closely resembles the classic Knapsack Problem in combinatorial optimization. This paper investigates the implementation of the Greedy algorithm as a heuristic approach to solve this problem by selecting tasks based on their value-to-story-point ratio. The Greedy strategy simplifies task selection by making locally optimal decisions at each step, thereby enabling efficient prioritization of high-value tasks without exceeding the sprint limit. A comparative experiment using real-world data was conducted to evaluate the effectiveness of the Greedy method against manual selection. The results demonstrate that the Greedy algorithm not only utilizes story point capacity more efficiently but also maximizes the total value of tasks included within the sprint. In some scenarios, it even achieved higher priority scores while consuming fewer story points. These findings affirm the practicality of Greedy-based optimization in Agile environments, particularly for rapid and scalable sprint planning. Future work may explore hybrid models or more advanced algorithms such as Dynamic Programming for enhanced optimization outcomes.

Standards for Storytelling-Based Nursing Communication to Reduce Hospitalization Anxiety in Preschool Children: A Systematic Review

2025-04-21T18:23:05+00:00

Hospitalization often triggers significant anxiety in preschool children due to their developmental vulnerability, separation from caregivers, and exposure to unfamiliar medical environments. If left unaddressed, this anxiety can result in both short- and long-term psychological and behavioral issues. Storytelling-based nursing communication has emerged as a promising intervention to help children understand and cope with hospitalization; however, standardized approaches remain limited. This study aims to identify and synthesize existing evidence on the standards of storytelling-based nursing communication for reducing hospitalization-related anxiety in preschool children. Using a descriptive analytical approach, a systematic search was conducted across PubMed, Scopus, ScienceDirect, and CINAHL for peer-reviewed studies published between 2020 and 2025. Eligible studies included nurse-led storytelling interventions targeting hospitalized preschoolers. Both qualitative and quantitative research was reviewed and critically appraised. The review identified fourteen relevant studies. Storytelling interventions were consistently effective in reducing anxiety, particularly when implemented using standardized methods such as structured narratives, visual aids (e.g., puppets, books, digital media), therapeutic play, and nurse communication training. These approaches enhanced emotional expression, improved nurse-child interaction, and fostered more positive hospital experiences. In conclusion, standardized storytelling-based nursing communication is an effective strategy for alleviating anxiety in hospitalized preschool children. Its broader implementation in pediatric nursing practice requires further research, training, and policy development.

Implementation Strategies for the Indonesian 3S Nursing Framework (SDKI, SIKI, and SLKI) in Clinical Practice: A Scoping Review

2025-05-01T05:14:16+00:00

The Indonesian 3S Nursing Framework—comprising the Nursing Diagnosis (SDKI), Nursing Interventions (SIKI), and Nursing Outcomes (SLKI)—was developed to standardize nursing care and enhance patient safety. Despite its formal adoption, implementation across Indonesian clinical settings remains inconsistent. This scoping review aims to identify and map strategies for implementing the 3S Framework in clinical nursing practice. Following PRISMA-ScR guidelines, a systematic search was conducted across Garuda, CINAHL, ScienceDirect, and Google Scholar for articles published between 2020 to 2025. From 1,356 articles, 12 met the inclusion criteria. Seven key strategies were identified: structured training programs, case study discussions, mentoring, use of standardized instruments, observational learning, innovative methods (e.g., simulation, role-play), and institutional support through policy and system integration. These approaches improved nurses' knowledge, skills, documentation accuracy, and patient safety. Challenges included institutional variability, limited monitoring, and unfamiliarity with the 3S components. Comprehensive strategies integrating education, mentorship, innovation, and institutional support are essential for effective implementation. Future efforts should focus on developing national guidelines, evaluation tools, and sustainable training models to support widespread adoption.

Implementation of the Backtracking Algorithm for Optimizing Work Shift Scheduling

2025-04-22T03:00:30+00:00

This research aims to implement the backtracking algorithm for optimizing shift scheduling at PLTU SSP. The study is motivated by the complexity of manual shift scheduling, which is prone to human error and struggles to accommodate various constraints such as employee availability, preferences, and operational needs. The backtracking algorithm was selected due to its ability to search systematically for optimal solutions that satisfy all constraints, based on Depth First Search (DFS). The research methodology includes requirements analysis, system design, algorithm implementation, testing, and results evaluation. The application of the backtracking algorithm produced schedules that accurately meet constraints and consider employee preferences. The results indicate that the backtracking algorithm can generate effective and efficient schedules. The implementation of the backtracking algorithm is expected to improve the quality of shift work management, positively impacting productivity, employee welfare, and the smooth operation of PLTU SSP.

Dynamics of Self-Compassion in Individuals with Childhood Sexual Abuse (CSA)

2025-04-28T13:14:20+00:00

This study aims to understand how the process of self-compassion is formed in individuals who experienced childhood sexual abuse trauma by emphasizing the aspects of subjective experience and the psychological meaning attached. This research method uses a descriptive phenomenological approach involving three adult respondents with a history of such experiences. Data were collected through in-depth interviews and analyzed through the stages of phenomenological reduction, identification of meaning units, transformation of meaning into psychological expressions, and synthesis of essential structures. The results of the study indicate that self-compassion develops gradually through the process of emotional reflection, spiritual awareness, and social support. Three main dimensions of self-compassion, self-kindness, common humanity, and mindfulness were identified in the respondents' recovery process. Self-compassion functions as a trauma recovery mechanism that helps respondents reconstruct their self-narrative from victims to whole and empowered individuals. These findings indicate the urgency of developing self-compassion-based psychological interventions for survivors of childhood trauma.

Implementation of the Bayesian Network Algorithm to Predict Chronic Diseases Using Electronic Medical Record Data at UPTD RSD Besemah, Pagar Alam City

2025-04-21T03:55:40+00:00

Chronic diseases are one of the leading causes of death in Indonesia and around the world. Early detection of chronic diseases poses a significant challenge for healthcare facilities, particularly in resource-limited areas such as UPTD RSD Besemah, Pagar Alam City. This study aims to implement the Bayesian Network algorithm as a method for predicting chronic diseases based on patients' electronic medical record (EMR) data. The Bayesian Network method was chosen due to its ability to model causal relationships between variables and its robustness in handling incomplete data. The data used in this research consists of secondary data in the form of patient medical records, with attributes including age, gender, medical history, laboratory results, and lifestyle factors. The research process involves data collection, preprocessing, Bayesian network structure formation, and model performance evaluation using accuracy, precision, and recall metrics. The results indicate that the Bayesian Network model is capable of delivering high prediction accuracy for chronic diseases such as diabetes mellitus, hypertension, and heart disease. The implementation of this predictive system is expected to assist medical personnel in clinical decision-making and enhance the effectiveness of preventive healthcare services

Implementation of the Backtracking Algorithm for Bandwidth Management in the Network of SMA Negeri 1 Belitang

2025-04-27T08:01:52+00:00

Internet has become an essential necessity in the field of education, particularly in supporting the teaching and learning processes in schools. Effective bandwidth management ensures efficient and equitable network usage for all users. This study implements the Backtracking algorithm as a solution for bandwidth management at SMA Negeri 1 Belitang. The algorithm is utilized to optimize bandwidth allocation based on usage priority and the dynamic number of users. The results indicate that this method can enhance bandwidth utilization efficiency, reduce delays, and improve the quality of service (QoS). Therefore, the Backtracking algorithm can serve as an alternative solution for achieving optimal network management in schools.