Data Processing Research Articles

Pengaruh Perceived Credibility, Trust, Subjective Norms, Perceived Behavioral Control, dan Perceived Expertise terhadap Attitude toward the Influencer dan kaitannya dengan Purchase Intention pada Doctor Influencer Avoskin

Influencer marketing is an effective strategy in the digital era, the use of doctor influencers in skincare promotions increases sales and purchasing intention. Doctors are the most trusted figures in providing health solutions in Indonesia. This study aims to determine and see the effect of perceived credibility, trust, subjective norms, perceived behavioral control, and perceived expertise on attitudes toward the influencer and the effect of attitudes toward the influencer on purchase intention. The method used in this study is descriptive and quantitative, with the contribution of 400 audience respondents who have watched Avoskin promotions using doctor influencers as talents or brand ambassadors for their promotions. The sampling technique used is non probability sampling with a purposive sampling type. Data processing and analysis techniques use PLS-SEM on the SmartPLS 4.0. The results of this study indicate that there is a favorable correlation between perceived credibility, trust, subjective norms, perceived behavioral control, and perceived expertise, on attitudes toward the influencer, which have an impact on the positive attitude of the audience towards product promotions using doctor influencers. In addition, the correlation of attitudes toward the influencer to purchase intention triggers the emergence of audience purchase intentions for products promoted by doctor influencers.

THE LOGISTICS PROCESS AND ITS ROLE IN THE ACTIVITIES OF MODERN ENTERPRISES

THE PURPOSE OF THIS ARTICLE is to study the peculiarities of the logistics process in the activities of domestic enterprises. RESEARCH METHODS. In the process of writing the article, general scientific and special methods of researching the logistics process at the enterprise were used, including: epistemological analysis, theoretical generalization, analysis, synthesis, tabular and graphic. PRESENTATION OF THE MAIN RESEARCH MATERIAL. The article defines that at a modern enterprise, the organization of production, supply and sales processes is ensured with the help of logistics. The essence of the concept of "logistics" from the point of view of foreign and domestic scientists is summarized. It was found that the logistics process is the basis of logistics. The main and auxiliary logistics processes at the enterprise, which consist of separate logistics operations and functions, are summarized. A diagram of the logistics process at the enterprise, which occurs simultaneously with material and information flows, has been formed. Logistics operations with material flow in the logistics process of the enterprise are systematized, which include: supply, transportation, storage, production and sales. Logistics operations with information flow in the logistics process of the enterprise include: information collection; accumulation and storage of information; data processing; transmission of information. A descriptive description of the specified logistics operations is given. It has been proven that the complexity of the logistics process at the enterprise depends on the specifics of its activity and the technological process of manufacturing products. CONCLUSIONS FROM THE CONDUCTED RESEARCH. Under the influence of modern digital technologies, the enterprise's logistics process will continue to undergo transformations that will affect its duration, the change in the number of logistics operations and functions, the transition to automation, and the level of cost effectiveness. KEYWORDS: logistics; logistics process; logistics operations; logistics functions; enterprise; digitalization.

Pengaruh Jumlah Penduduk, Pengangguran, dan Pendidikan terhadap Kemiskinan di Wilayah Eks Karesidenan Banyumas

Poverty is a condition characterized by insufficiency to satisfy everyday needs in situations not involving the will. Poverty can degrade the quality of human resources, resulting in lower levels of productivity and lower earnings. The aim of this study is to find out the influence of factors such as population, unemployment, and education on poverty in the Banyumas Ex-Resident Territory from 2013 to 2022. This is quantitative research, processing data using E-Views 10 software in the regression of panel data. The data used is secondary data from the Central Statistical Agency source. Fixed+Effect+Model became a suitable model in this study. According to the research, the population, unemployment, and education variables have a combined or simultaneous impact on the poverty variable.

GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.

A Hybrid Parameters Estimation Approach for Power Consumption Modeling of Ground Mobile Robots With Unknown Payload

ABSTRACTTypically, batteries are the main power source for mobile robots, and the practical constraints in carrying them pose challenges in achieving efficiency, autonomy, and safety objectives in robotic missions. Moreover, dealing with battery degradation requires a precise understanding of energy consumers' behavior in the system. Therefore, establishing an energy consumption model has an important role in addressing these challenges. This study focuses on the modeling and estimation of power consumption in indoor ground mobile robots with unknown payloads. We investigate key components and parameters that influence the actuation energy consumption of ground mobile robots equipped with DC motors, particularly in the context of pure rolling without slipping. To estimate the actuation power consumption of ground mobile robots, we present a hybrid parameters estimation approach. This method includes an offline step for estimating the mass‐decoupled coefficients of a power model, followed by two online steps for estimating the robot's unknown payload and its actuation power consumption. Simulation and experimental results with a differential drive robot (Turtlebot3) validate the effectiveness of the proposed approach. Additionally, the performance of the proposed power consumption estimation method is compared with two other data‐driven models based on multivariate linear regression and multilayer perceptron neural networks. The results demonstrate that the proposed method provides improved power consumption estimation accuracy on the selected indicators compared to the others. We also emphasize the importance of balancing the accuracy of power consumption estimation with its associated costs in the context of the proposed approach. This extra cost includes the energy needed for additional data measurement and processing. Considering this trade‐off is crucial for the practical implementation and resource optimization in robotic systems.

Oceanic and ionospheric tidal magnetic fields extracted from global geomagnetic observatory data.

Ocean tide generated magnetic fields contain information about changes in ocean heat content and transport that can potentially be retrieved from remotely sensed magnetic data. To provide an important baseline towards developing this potential, tidal signals are extracted from 288 land geomagnetic observatory records having observations within the 50-year time span 1965-2015. The extraction method uses robust iteratively reweighted least squares for a range of models using different predictant and predictor assumptions. The predictants are the time series of the three vector components at each observatory, with versional variations in data selection and processing. The predictors fall into two categories: one using time-harmonic bases and the other that directly use lunar and solar ephemerides with gravitational theory to describe the tidal forces. The ephemerides predictors are shown to perform better (fitting more variance with fewer predictors) than do the time-harmonic predictors, which include the traditional 'Chapman-Miller method'. In fitting the oceanic lunar tidal signals, the predictants with the highest signal/noise involve the 'vertical' magnetic vector component following principle-component rotation. The best simple semidiurnal predictor is the ephemeris series of lunar azimuth weighted by the inverse-cubed lunar distance. More variance is fitted with predictors representing the lunar tidal potential and gradients calculated for each location/time.This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'.

Pengaruh Kinerja Kualitas Layanan, Kualitas Produk, Kepuasan Pelanggan dan Niat Beli Ulang pada Restoran Hoka-Hoka Bento di Kabupaten Tangerang

The fast food restaurant industry has experienced rapid growth and has become one of the important sectors in the culinary business, fast food restaurant business actors continue to innovate to meet the needs and desires of consumers. The intention to repurchase is the most important part that must be considered by restaurant business actors which directly impacts the sustainability and growth of the business. This study aims to fill the knowledge gap about the role of service quality performance on product quality, customer satisfaction, and repurchase intention in Hoka-Hoka Bento restaurant customers in the Tangerang Regency area. Data collection was obtained through a questionnaire distributed online in June 2024. The sampling method used is non-probability sampling with purposive sampling technique The data analysis technique used is Partial Least Square-Structural Equation Modeling (PLS-SEM). The results of data processing show that the performance of service quality,product quality, and customer satisfaction have a positive and significant effect on repurchase intentions. This research contributes to companies to improve service quality performance in forming repurchase intentions in consumers. Further research is expected to use a larger scale of respondents and use different platforms.

Analisis Faktor-Faktor yang Mempengaruhi Loyalitas Pelanggan pada Restoran Cepat Saji di Kabupaten Tangerang

The fast food industry has grown rapidly driven by urbanization, modern lifestyles, and innovation in menus and service. This industry also faces significant challenges, namely intense competition. Therefore, companies need to improve factors that can strengthen relationships with customers and encourage them to remain loyal. This research aims to analyze the influence of service quality, price and food quality on customer satisfaction and customer loyalty at the Tigaraksa fast food restaurant. This research uses quantitative methods and uses non-probability sampling methods with purposive sampling techniques. The data analysis technique used was Partial Least Square-Structural Equation Modeling (PLS-SEM), with a sample size of 240 respondents. The results of data processing prove that service quality has a positive effect on customer satisfaction. Price has a positive effect on customer satisfaction. Food quality has a positive effect on customer satisfaction. Service quality has a positive effect on customer loyalty. Price has a positive effect on customer loyalty. Food quality has a positive effect on customer loyalty, and customer satisfaction can increase customer loyalty. This research contributes to restaurant managers continuing to improve service quality, setting competitive prices, and ensuring consistent food quality in order to achieve higher customer satisfaction and loyalty.

Analytical review of confidential artificial intelligence: methods and algorithms for deployment in cloud computing

The technologies of artificial intelligence and cloud systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.

Pengaruh Kapabilitas, Visibilitas, Terhadap Performa Rantai Pasokan dan Perusahaan yang Dimoderasi Jumlah Karyawan, Usia Perusahaan

This study aims to analyze the impact of capabilities, visibility, and supply chain resilience on supply chain performance and company performance, with the moderating variables of the number of employees and company age. The subjects of this study are directors, managers, supervisors, or staff/operators from various industrial companies in Indonesia. The research sample consists of 140 respondents, selected using a non-probability sampling technique with purposive sampling. Data collection was conducted using questionnaires distributed via Google Forms and measured with a five-point Likert scale. For data processing, this study employs Structural Equation Model (SEM) Partial Least Square (PLS) analysis using the SMART-PLS version 3 software. The results of this study are as follows: supply chain capabilities and visibility have a significant impact on supply chain resilience, supply chain performance, and company performance, with supply chain resilience serving as a mediating variable. Additionally, supply chain performance significantly affects company performance. Company age does not moderate the relationship between capabilities and supply chain resilience. Similarly, the number of employees does not moderate the impact of supply chain capabilities on supply chain resilience

Endo-bind-n-seq: identifying RNA motifs of RNA binding proteins isolated from endogenous sources.

RNA binding proteins (RBPs) are crucial regulators of gene expression and critically depend on the specific recognition of their target RNAs. Accordingly, a selection of methods to analyze RBP specificities has been developed, including protein-RNA crosslinking and sequencing (CLIP) and in vitro selection methods such as SELEX, RNA compete or RNA bind-n-seq. However, limitations like the availability for purified recombinant proteins and custom microarray platforms (RNAcompete) or extensive sequencing depth and sophisticated bioinformatic data processing (CLIP) may limit a broader implementation of these methods. Here, we present an RNA bind-n-seq method that uses short random RNA pools and enables multiple rounds of selection. This results in strong motif enrichment with low positional variance thus reducing sequencing depth requirements. Furthermore, we have coupled our protocol to immunoprecipitation of tagged or endogenous RBPs from cultured cells or tissue samples, eliminating the need for recombinant proteins. Our method also allows for the identification of indirect RNA motifs of proteins that are integral parts of multiprotein RNPs and result in physically more relevant RNA motifs.

Заболеваемость болезнями нервной системы детей и подростков мегаполиса.

The significant proportion in the structure of childhood disability determines the relevance of assessing the incidence rates of newly diagnosed diseases of the nervous system in the child population of the metropolis. Purpose: t o analyze the primary incidence of diseases of the nervous system in children and adolescents in a metropolis. Materials and methods. The statistical data collections of the Federal State Budgetary Institution «Central Research Institute for Health Organization and Informatization» of the Ministry of Health of the Russian Federation for 2015–2024 were used as the base material. An assessment of intensive and extensive indicators of primary morbidity of children aged 0–14 and 15–17 in St. Petersburg was carried out in dynamics over ten years. A comparative analysis of the indicators was carried out using the visibility indicator with indicators for the Russian Federation and the Northwestern Federal District. The significance of differences between the indicators was assessed using Student’s t-test. Statistical data processing was carried out using MS Office‑2019 software packages. Results and discussions. The primary incidence of nervous system diseases among the child population aged 0–14 in St. Petersburg was significantly higher than the national and Northwestern Federal District averages in the entire period 2014–2023, and has grown by 19.4% over the past ten years. The incidence of 15–17-year-old adolescents was higher than the figures for 0–14-year-old children. The incidence of newly diagnosed nervous system diseases among the adolescent population of St. Petersburg also significantly exceeded the national and federal district averages, and had an upward trend (+4.0%). In the metropolis, the proportion of newly diagnosed nervous system diseases among all newly diagnosed diseases among children aged 0–14 and adolescents aged 15–17 was significantly higher than the national and Northwestern Federal District averages over the entire period studied, but has decreased over the past ten years. The rate of decline in the share of newly diagnosed nervous system diseases in adolescents was 2.7 times higher than in children (20.8% versus 7.6%), and the difference between the metropolis’ indicators and the indicators for Russia and the federal district was less significant. The COVID‑19 pandemic had a significant impact on the primary morbidity rate of children aged 0–14 and 15–17 and the share of newly diagnosed nervous system diseases in them. In 2020, compared to 2019, the incidence rate of nervous system diseases in children and adolescents in the metropolis significantly decreased, while the share of newly diagnosed nervous system diseases, on the contrary, increased. Conclusion. The high level of primary morbidity with a high proportion of newly diagnosed diseases of the nervous system among all newly diagnosed diseases in children and adolescents in the metropolis indicates an increasing role of neurological pathology in the morbidity of the child population.

Gas Leakage Detection Using Tiny Machine Learning

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards and preventing dangerous incidents. Traditional detection systems often rely on centralized data processing, which can lead to delays and scalability issues. To overcome these limitations, in this study, we present a solution based on tiny machine learning (TinyML) to process data directly on devices. TinyML has the potential to execute machine learning algorithms locally, in real time, and using tiny devices, such as microcontrollers, ensuring faster and more efficient responses to potential dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 and EfficientNet-B0, deployed on the Arduino Nano 33 BLE Sense. The results show that our system not only provides real-time analytics but does so with high accuracy—88.92% for MobileNetV1 and 91.73% for EfficientNet-B0—while achieving inference times of 1414 milliseconds and using just 124.8 KB of memory. Compared to existing solutions, our edge-based system overcomes common challenges related to latency and scalability, making it a reliable, fast, and efficient option. This work demonstrates the potential for low-cost, scalable gas detection systems that can be deployed widely to enhance safety in various environments. By integrating cutting-edge machine learning models with affordable IoT devices, we aim to make safety more accessible, regardless of financial limitations, and pave the way for further innovation in environmental monitoring solutions.

AI-Driven Energy Efficiency Optimizations in mHealth Applications: A Comprehensive Review on User Behavior Prediction and System Performance

Recently, mHealth applications have gained immense popularity, revolutionizing healthcare management for chronic diseases and fitness tracking. However, continuous data processing and transmission increase the strain on battery life. This study examines AI and machine learning-based techniques to reduce energy consumption in mHealth applications without compromising functionality. Adaptive sampling, task scheduling, and predictive user behavior modeling were implemented, significantly reducing power consumption and extending battery life. Challenges such as data privacy and model generalization in deploying these AI technologies are also addressed, along with future research and broader adoption.

Fiducial Reference Measurements for Air Quality Monitoring Using Ground-Based MAX-DOAS Instruments (FRM4DOAS)

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO2 vertical profiles and formaldehyde profiles. The ozone and NO2 products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.

Faktor-Faktor yang Mempengaruhi Belanja Daerah pada Pemerintah Provinsi Indonesia

This research was carried out in order to achieve the aim of understanding the factors that influence Regional Expenditures in Provincial Governments in Indonesia. The data used is secondary data sourced via the BPK RI website. The focus of this research covers a population of 34 provincial governments with the sampling technique used is a saturated sample with 170 observation units. Data processing involves the help of the Eviews version 12 application. Data analysis uses multiple linear regression analysis with a signitificance level of 5%.The results of multiple regression analysis show that simultaneously Original Regional Income, Balancing Funds and SiLPA have a signitificant effect on Regional Expenditures. But partially, Original Regional Income and Balancing Funds have a signitificant positive influence on Regional Expenditures but SiLPA has no effect on regional Expenditures.

Semantic segmentation network for mangrove tree species based on UAV remote sensing images

Mangroves are special vegetation that grows in the intertidal zone of the coast and has extremely high ecological and environmental value. Different mangrove species exhibit significant differences in ecological functions and environmental responses, so accurately identifying and distinguishing these species is crucial for ecological protection and monitoring. However, mangrove species recognition faces challenges, such as morphological similarity, environmental complexity, target size variability, and data scarcity. Traditional mangrove monitoring methods mainly rely on expensive and operationally complex multispectral or hyperspectral remote sensing sensors, which have high data processing and storage costs, hindering large-scale application and popularization. Although hyperspectral monitoring is still necessary in certain situations, the low identification accuracy in routine monitoring severely hinders ecological analysis. To address these issues, this paper proposes the UrmsNet segmentation network, aimed at improving identification accuracy in routine monitoring while reducing costs and complexity. It includes an improved lightweight convolution SCConv, an Adaptive Selective Attention Module (ASAM), and a Cross-Layer Feature Fusion Module (CLFFM). ASAM adaptively extracts and fuses features of different mangrove species, enhancing the network’s ability to characterize mangrove species with similar morphology and in complex environments. CLFFM combines shallow details and deep semantic information to ensure accurate segmentation of mangrove boundaries and small targets.Additionally, this paper constructs a high-quality RGB image dataset for mangrove species segmentation to address the data scarcity problem. Compared to traditional methods, our approach is more precise and efficient. While maintaining relatively low parameters and computational complexity (FLOPs), it achieves excellent performance with mIoU and mPA metrics of 92.21% and 95.98%, respectively. This performance is comparable to the latest methods using multispectral or hyperspectral data but significantly reduces cost and complexity. By combining periodic hyperspectral monitoring with UrmsNet-supported routine monitoring, a more comprehensive and efficient mangrove ecological monitoring can be achieved.These research findings provide a new technical approach for large-scale, low-cost monitoring of important ecosystems such as mangroves, with significant theoretical and practical value. Furthermore, UrmsNet also demonstrates excellent performance on LoveDA, Potsdam, and Vaihingen datasets, showing potential for wider application.

A framework for flood inundation extraction based on microwave and optical remote sensing images

IntroductionEffective monitoring and evaluation of floodwaters are essential for disaster prevention and mitigation. The flood inundation range can be obtained by using traditional simulation methods, but these methods still have shortcomings. This work proposes an optimization method for traditional methods.MethodsThis study aims to introduce an effective solution for the rapid and accurate extraction of flood inundation areas, emphasizing the enhancement of extraction speed and dynamic monitoring throughout the flood event. The solution uses a normalized difference water index (NDWI), a refined threshold method, and a filtering process for microwave (radar) images. Sentinel-1 SAR (Synthetic Aperture Radar) and Sentinel-2 MSI (Multi-spectral Image) images served as the primary data sources. The Sentinel-2 images were preprocessed to extract pre-flood water bodies, while the Sentinel-1 SAR images were processed using the proposed filtering method to identify post-flood inundation areas.ResultsThe application and validation of this framework are demonstrated through the case of the 2020 flood event in Tongling, Anhui Province. The framework’s performance was validated through comparison with ground truth data, yielding high kappa accuracies of 98% for optical images and 89% for Synthetic Aperture Radar. The findings highlight the framework’s ability to capture high-accuracy changes in flood inundation areas and to characterize the dynamic process of flood inundation area changes.DiscussionThis study contributes to the field by enhancing the extraction speed and scope of water bodies from SAR images and improving the quality of microwave remote sensing data processing. It offers valuable insights for emergency rapid response and situational awareness in the context of extreme weather events and associated flood disasters.

A consortium blockchain-edge enabled authentication scheme for underwater acoustic network (UAN)

The Internet of Things (IoT) allows for automated operations in diverse fields, such as agriculture monitoring, pollution monitoring, health care, and underwater monitoring. The Internet of Underwater Things (IoUT) observes the underwater environment, assists in exploration, mitigates disasters, and monitors some factors including temperature, pressure, and pollution. The IoUT relies on a network of intelligent underwater sensors that send data to surface base stations and IoT devices for storage and analysis. Nevertheless, these systems face security risks as they operate in unattended environments. Many authentication methods depend on a centralized third party, leading to higher computation costs and energy usage. To mitigate security risks, autonomous underwater devices need secure connections and authentication. This paper suggests a decentralized authentication mechanism for UAN to safeguard against unauthorized access and ensure secure data storage in the cloud. The proposed mechanism prioritizes robustness, transparency, and energy efficiency. The suggested solution incorporates an architecture based on edge and cloud layers, utilizing customized blockchain technology for secure storage and processing of data. The security of the proposed solution has been thoroughly examined through formal analysis utilizing the Real or Random (ROR) Oracle model and Scyther tool. Informal analysis further confirms the solution’s resilience against various malicious attacks. Additionally, performance and comparative analysis demonstrate that the proposed solution surpasses existing schemes.

Implementation of effective communication in interprofessional collaboration to improve inpatient services

Introduction: Interprofessional Collaboration (IPC) is an interprofessional collaboration between people with different professional backgrounds who work together to solve health problems and provide health services. This research aims to analyze the implementation of effective communication in interprofessional collaboration to improve inpatient services. Research Methodology: The research is qualitative with a phenomenological design. Six people were the research informants: hospital leadership, PMKP, medical committee, medical service element, nursing element, and medical support element. Data will be collected using observation, FGD, in-depth interviews, and document study. Data processing and analysis use the Miles and Huberman method. Result: The study's results indicate that the implementation of IPC still needs improvement. Lack of practical communication skills is the leading cause of IPC being less than good. Management efforts to improve helpful communication skills include providing training related to effective communication, forming an evaluation team, and giving rewards and punishments. Conclusion: Management is making efforts to improve effective communication skills by providing training related to effective communication, forming an evaluation team, and giving rewards and punishments. Health workers are making efforts to comply with all rules that have been given by management to support an effective communication program for patients and between health workers