Publication Search

Primary Healthcare Facilities (Fasilitas Kesehatan Tingkat Pertama, FKTP) represent the first level of contact in the healthcare system and play a central role in infection prevention and control. Despite mandatory Infection Prevention and Control (IPC) training in Indonesia, evidence regarding its effectiveness in improving cognitive abilities among primary healthcare workers remains limited. This study aimed to evaluate the effectiveness of IPC training in enhancing the cognitive abilities of healthcare workers in FKTP. A quasi-experimental study with a one-group pretest–posttest design was conducted involving 91 healthcare workers who participated in IPC training across three cohorts in 2024. The training was delivered online through a Learning Management System and consisted of structured learning modules accompanied by a pre-test and a final quiz. Cognitive improvement was assessed using paired samples t-tests, while the magnitude of training impact was evaluated using Cohen’s dz effect size. The results showed statistically significant improvements in cognitive scores across all cohorts (p < 0.001), with mean score increases ranging from 16.10 to 23.35 points. Effect size analysis revealed large to very large effects, with an overall Cohen’s dz of 1.19, indicating substantial and practically meaningful cognitive gains. In conclusion, IPC training was effective in improving cognitive competence among FKTP healthcare workers. These results reinforce the value of well-structured training programs as an essential component of efforts to strengthen infection prevention capacity in primary healthcare settings.

Advancements in modern technology have reshaped people’s lifestyles, including how individuals carry out their daily activities. These changes have influenced individuals to make less realistic decisions in an effort to align themselves with their desired lifestyle. This study aims to examine whether college students with certain lifestyle tendencies are more likely to engage in impulsive buying when shopping online. The study involved 392 college students residing in Surabaya, East Java, selected through a random sampling technique. Data were collected using a Google Form–based questionnaire containing measurement instruments developed by the researcher: (1) the Impulsive Buying Scale (15 items; α = 0.858) and (2) the Lifestyle Scale (22 items; α = 0.898). Multiple linear regression was used to analyze the data.The findings reveal a significant positive relationship between lifestyle and impulsive buying. The strongeran individual’s consumerist lifestyle tendency, the more likely they are to make impulsive purchases.  

This study aims to analyze hematocrit values ​​using a micro method with capillary blood samples in pregnant women with anemia at the Sidodadi Kisaran Barat Community Health Center. This study is a descriptive study with a cross-sectional approach involving 15 pregnant women as respondents. Data collection was carried out through hematocrit laboratory examinations and data collection of respondent characteristics. The results showed that 60% of respondents had hematocrit levels below normal values, while 40% of respondents had hematocrit levels within normal limits. Low hematocrit levels in pregnant women are influenced by several factors, including age, education level, employment status, and level of knowledge about anemia during pregnancy. This condition has the potential to increase the risk of pregnancy complications if not optimally managed. Therefore, it is recommended that health workers continue to increase educational efforts regarding anemia prevention, the importance of regular pregnancy check-ups, and encourage pregnant women to maintain a balanced nutritional consumption pattern and adequate iron intake to support maternal and fetal health.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.  

The implementation of industrial engineering technologies, such as automation, the Internet of Things (IoT), artificial intelligence (AI), and lean manufacturing, has significantly transformed human resource (HR) capabilities in the production sector, particularly in response to the Industry 4.0 paradigm. This study aims to examine the relatively low level of technology adoption in Indonesia, estimated at only 6–20% of manufacturing companies, and its impact on the development of HR competencies. The analysis focuses on changes in technical skill requirements, including digital literacy, data analytics, and technology-based decision-making, as well as the shift in job roles from manual tasks to more strategic functions. This research employs a qualitative descriptive approach grounded in sociotechnical systems theory and the strategic alignment model. The findings indicate that existing skill gaps can be addressed through continuous upskilling and reskilling programs, supported by strengthened triple helix collaboration among government, industry, and educational institutions. The implementation of these strategies has been shown to increase productivity by approximately 30–72% and enhance the competitiveness of the national production sector in the global industrial landscape.  

Waste from used Polypropylene (PP) and High Impact Polystyrene (HIPS) plastic is problematic in its management. The purpose of this research is to obtain the fatigue life of a mixture of used PP and HIPS plastics in its pure plastic. The research method is through the stages of mixing pure PP and 50% volume of used PP, pure HIPS and 50% volume of used HIPS, injection molding of R.R. Moore standard fatigue test specimens for pure PP, pure HIPS, pure PP + used PP, and pure HIPS + used HIPS, checking the straightness and smoothness of the molded specimen surface, fatigue testing with increasing serial loads, analysis of the bending stress curve (S) against fatigue life (N). The results showed that mixing used PP and used HIPS in pure plastic affected the fatigue life at the test speed (n)=2100 rpm for recycled PP was 9.84% shorter than pure PP, and at n=1600 rpm for recycled PP it was 9.32% shorter than pure PP, while at n=2100 rpm for recycled HIPS it was 4.45% shorter than pure HIPS, and at n=1600 rpm for recycled HIPS is 4.77% shorter than pure HIPS, while the fatigue life of pure PP is 1627704 cycles and the fatigue life of pure HIPS is 1291636 cycles or the fatigue life of pure HIPS is 20.65% shorter than pure PP, the implication of which is that the addition of used PP and used HIPS reduces the fatigue life to 9.84% for PP and 4.45% for HIPS.

Currently, patients experiencing early symptoms of skin disease caused by the exanthema virus are unable to immediately consult a dermatologist due to the high cost and limited time available for specialists in hospitals. Therefore, the author needs to develop an expert system application that can address this issue. Through this application, users can consult with the system, much like an expert, to diagnose their symptoms and find solutions to their problems. This expert system is designed to provide answers based on whether the symptoms are correct or not, or to provide several recommended answer choices based on the symptoms. To diagnose skin disease caused by the exanthema virus, the author used the Case-Based Reasoning method. The CBR method is a weighting technique that compares new cases with previous cases. The diagnosis is based on data provided by the patient and expert, which is then analyzed using case-based reasoning and stored as a knowledge database in the expert system. Therefore, this expert system can help identify solutions for problems experienced by patients suffering from skin disease caused by the Exanthema Virus.

Tulungagung Regency has the potential for culinary tourism that can be developed, one of which is traditional culinary. Although the Tulungagung Regency Government has presented a "Halal Culinary Area" and a "Pinka Culinary Tourism Area", the area has not fully provided an interesting visual and functional experience. Therefore, it is necessary to design a space that facilitates traditional culinary MSMEs as well as an educational and recreational place. This design carries the theme of contemporary tropical architecture by considering local climatic conditions, through the application of wide openings, cross ventilation, wide trellises, and the use of natural materials, as well as expressive styles with the shape of a rooster roof and the basic shape of an elliptical building as a symbol of adaptation to climate change and the economy in the business environment. The facilities in this design include traditional restaurants, educational bales, wiyata offices, prayer rooms, playgrounds and other supporting facilities that can attract the interest of the community and introduce traditional Tulungagung culinary.

The development of sustainable tourism facilities is one of the important efforts in increasing the attractiveness of destinations while preserving the environment. This study aims to analyze the implementation of ecological concepts in the development of glamping facilities in the Pearl Beach tourist area. The method used is a descriptive qualitative approach, with data collection through field observations, interviews with managers and tourists, and literature studies related to ecotourism principles and sustainable design. The results of the study show that the application of ecological concepts in glamping facilities in Mutiara Beach includes the use of environmentally friendly materials, integrated waste management, the application of energy efficiency, and designs that integrate the natural landscape without damaging the coastal ecosystem. The application of this concept not only improves the comfort and experience of tourists, but also contributes to increasing environmental awareness and strengthening the positive image of tourist destinations. In addition, this ecologically-conceptual glamping development model is expected to be a reference for the development of sustainable tourism facilities in other coastal areas, which prioritizes nature preservation and the welfare of local communities.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific details. The methodological stages include needs analysis, user interface (UI) design using Figma, front-end implementation with HTML, CSS, and JavaScript, database integration, and system testing through Black Box Testing. The results showed that the SIE implementation successfully integrated operational data, including schedules, ships, and manifests, into an interactive dashboard. The two-level drill-down feature provides the ability for executives to identify operational anomalies and market fluctuations in real-time. In conclusion, the system significantly enhances executive data analysis capabilities, transforming complex transaction data into accurate strategic information, thereby supporting more precise business decision-making and adaptive to the dynamics of the marine transportation market.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

Queueing systems play an important role in evaluating service performance, especially in small-scale businesses such as barbershops, where fluctuating customer arrival patterns and limited service capacity often lead to long waiting times. This study aims to analyze the performance of barbershop services using the M/M/1 queueing model and an analytical approach based on experimentally tested arrival (λ) and service (μ) rates. The model was selected because it represents a single-server system with Poisson arrivals and exponentially distributed service times, closely matching real barbershop operational characteristics. Using assumed realistic parameters, the analysis shows that when λ = 12 customers per hour and μ = 6 customers per hour, the system becomes unstable with a utilization rate (ρ) exceeding 1, indicating continuous queue growth. Further simulations with increased service rates demonstrate significant improvements: at μ = 15, the system achieves ρ = 0.8 with an average waiting time of 16 minutes, while at μ = 13, the system remains stable but experiences a long waiting time of approximately 55 minutes. These findings emphasize that barbershop performance is highly sensitive to service speed and that even small increases in μ can produce substantial improvements in queue stability and customer waiting times. The study concludes that barbershops must ensure adequate service capacity—either through optimizing service duration, improving worker efficiency, or adding servers—to maintain service quality and enhance customer satisfaction.

This research aims to design and implement a village MSME website as a centralized digital promotional medium to overcome the limitations of conventional marketing and expand the market reach of local products more effectively and sustainably. The system was developed using the waterfall method, encompassing requirements analysis, design, implementation, testing, and maintenance. The system was developed using the PHP programming language and the CodeIgniter framework based on the Model-View-Controller (MVC) architecture to ensure a structured, efficient, and maintainable development process. The implementation resulted in a responsive and user-friendly website equipped with key features such as an informative product catalog, village MSME profiles, and a content management system via an admin dashboard that allows MSMEs to update data independently and flexibly. Functional testing demonstrated that all features functioned well and reliably according to user needs. Therefore, this village MSME website can be concluded as an effective digital solution for increasing the visibility of local products, strengthening MSME competitiveness, and supporting village economic growth through sustainable and integrated online promotion.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

The scarcity of real-world data in Air-Conditioning (AC) fault diagnosis necessitates the use of synthetic data; however, rule-based synthetic datasets often suffer from a significant sim-to-real domain gap. To address this, we propose a Model-Data Coevolution (MDC) framework that employs a Simulated Annealing (SA) controller to optimize augmentation parameters. We introduce a novel technique, Stochastic Feature Decoupling (SFD), which applies independent noise to raw and derived features, contrasting it with traditional Logically-Consistent Augmentation (LCA). Empirical results show that SFD significantly outperforms LCA, achieving a weighted F1-score of 0.93 and increasing NORMAL class recall to 82%. We demonstrate that by breaking deterministic feature links, SFD acts as a robust regularizer, utilizing "physically impossible" data to enhance generalization in complex real-world environments.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.