Publication Search

The purpose of this activity is to develop digital marketing skills through training on the use of digital applications Canva and Capcut to promote Micro, Small, and Medium Enterprises (MSMEs) in fish processing and tourism in the Fisherman Village located in Salira Village, Serang Regency. This activity aims for MSME entrepreneurs to enhance their skills in taking photos and videos, as well as processing these images and videos into more engaging, innovative, informative, and professional content.The method used in this activity involves training on the use of the Canva and Capcut applications with the theme "Application of CANVA and CAPCUT in Digital Marketing." The research results indicate a positive and significant improvement in marketing content compared to previous efforts. However, there are still challenges to address, such as the inadequate facilitation of supporting tools for content creation, which must be improved for the content produced to continue evolving for display on various digital marketing platforms.

This research aims to automate the process of extracting information from financial transaction receipts using Optical Character Recognition (OCR) algorithm. The OCR algorithm is used to recognize characters from digital images of transaction receipts so that information such as date, transaction amount, and other details can be accurately identified. By applying image processing methods, this research successfully demonstrates the effectiveness of the OCR system in overcoming challenges such as varying receipt print quality. This research also offers practical solutions in the form of OCR-based applications that can be used in business environments to improve the efficiency and accuracy of financial transaction data management.

Rice pest control is one of the main challenges in the agricultural sector in Indonesia. Pests such as planthoppers, stem borers, and rats can cause significant losses to crop yields. Currently, many farmers have difficulty identifying pest types and how to control them quickly and effectively. Therefore, a technological solution is needed that can detect pests directly and provide recommendations for action. This study aims to design an Android-based application that is able to detect types of rice plant pests using visual images and provide recommendations for handling. This application is designed using digital image processing methods and is supported by a large pest database. This technology is expected to be an efficient and practical solution for farmers. The research method used in developing this application is a qualitative method, involving interviews with farmers, agronomists, and data collection related to pests and their damage patterns. This application utilizes AI-based pattern recognition technology to detect pests through photos taken directly by farmers. The results of the study showed that this application was able to detect several types of major pests with an accuracy of up to 85%. In addition, this application provides recommendations for handling steps based on guidelines from the Ministry of Agriculture. The trial showed that this application can help farmers identify pests faster than manual methods. The main contribution of this research is to create a technology-based solution to agricultural problems in Indonesia, especially in the rice sector. With this application, farmers can increase their yields through early identification and proper pest management. In future implementations, this application will continue to be developed to detect additional pests and expand its database. It is hoped that this application can be an important tool in supporting smart farming in Indonesia.

OpenCV is a widely used library in the field of image processing and computer vision. Combined with Python's flexibility, OpenCV provides an extensive range of functions for efficient image processing, modification, analysis, and visualization. This paper aims to introduce the fundamental concepts of image processing using Python and OpenCV, including image reading, color conversion, edge detection, and image manipulation such as resizing and cropping. Furthermore, this study discusses basic analysis techniques like color distribution histograms and feature detection. By presenting these concepts, the paper aspires to help readers understand the foundations of digital image processing and explore the potential applications of OpenCV in practical fields such as pattern recognition, object detection, and image segmentation.

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.    

The selection of agricultural and plantation products often relies on human perception of fruit color. Manual identification through visual observation has several drawbacks, such as time consumption, fatigue, and varying perceptions of quality. Digital image processing technology enables automatic sorting of products. This study applies the Perceptron learning method to identify tomato ripeness. Tomato images are captured using a webcam, analyzed through color histograms, and identified using artificial neural networks. The identification success rate reaches 43.33%, with outputs categorized as Unripe (10%), Half-Ripe (6.66%), and Ripe (26.66%).

The development of science and technology continues to grow very rapidly, meanwhile applications in the field of information technology have a major impact in various areas of life, one of which is in the creative industries such as advertising, billboards, graphic design, fashion and digital image processing. At this time graphic design skills are needed in various fields, to support business and organizational needs. On this occasion, we presenters from Universitas Potensi Utama use the CorelDraw application facility to create a Logo. The software used in this training is vector-based. So that the final results obtained are of better quality when enlarged / zoomed. So that the output target of this activity is that participants can implement graphic design both within the school environment in the form of making wall magazines or other announcements and can be implemented in the world of work related to graphic design. This activity aims to improve the knowledge and skills of students in improving the quality of their ability to create attractive graphic designs so that participants can compete to meet the demand for job opportunities and also towards entrepreneurship. The materials given to participants include creating and completing logo designs for business or organizational needs.

Cats (Felis catus) are a type of carnivorous mammal from the Felidae family that was domesticated and has been one of the animals that has mingled with humans since time immemorial. Domestic cats are broadly divided into 2 types, namely village cats and purebred cats. Purebred cats have quite a varied number of types. Therefore, confusion often occurs in determining the type or breed of cat. Meanwhile, in practice, each race does not have the same treatment (especially in the aspect of care). In digital image processing, Machine Learning and Deep Learning are the main aspects in the process of applying technology that can overcome this problem, so research related to this problem was designed. This research was conducted to add insight for further research in a more sophisticated and effective image recognition process. In the experiments carried out in this research, the SVM, KNN, and CNN methods were tested with the Xception and EfficientNet-B1 architectures. Based on the final results obtained from this test, the CNN method with the Xception architecture is the best model. By using fine-tuning and a learning-rate of 1e-5, this method produces a micro average value of 0.974, on a cat breed image dataset of 13 classes and 7800 images. Meanwhile, the method that produces the fastest ETA Training and Testing is obtained by the KNN method, with an ETA Training time of 0.194 seconds, and an ETA Testing time of 1.782 seconds.      

This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning process were also identified. Support and training are needed for lecturers to overcome these challenges. These findings provide valuable insights for the development of problem-based teaching practices in the context of digital image processing.  

This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning process were also identified. Support and training are needed for lecturers to overcome these challenges. These findings provide valuable insights for the development of problem-based teaching practices in the context of digital image processing.

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.    

An important digital image processing is image segmentation, which separates objects from the background for further analysis. One segmentation technique is edge detection, which looks for boundaries between areas of different brightness. This article compares four edge detection methods: Roberts, Prewitt, Sobel, and Canny. The results show that, despite requiring more complex computations, Canny's method produces the sharpest and best connected edges; Sobel and Prewitt's method, on the other hand, is faster and simpler than Roberts' method, but is less effective in dealing with noise and often produces edges that are not connected to the plane. The choice of edge detection method depends on the application. Sobel and Prewitt are good for speed and stability, and Roberts is suitable for fast processing of images with minimal noise.

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.    

Among the vegetables most commonly consumed by people around the world are tomatoes. One of the potential vegetable commodities to be developed is tomato plants. This plant can thrive in rice fields, dry land, and highlands. Use of Technology Digital images are images that can be processed by computers directly. A matrix with M columns and N rows can be used to describe a digital image. The smallest element in an image is called a pixel or image element, and is the intersection between columns and rows. image processing is the process of processing an image numerically; in this case, each pixel or point in the image is treated. One method of image processing is to use computer software to process each pixel in the image. It is easier for object recognition applications in image processing to identify objects based on differences in hue values when the hue values of objects are limited to a certain value. The color space system that mimics the capabilities of the human eye is called the HSI color space model. HSI incorporates the grayscale or color components of an image. The test image of Tomato fruit with a value of H = 32 S = 0.675 I = 83 can be considered ripe, according to the range of fruit reference values that have been established through the use of the HSI method.    

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.      

Currency classification is one of the challenges in the field of digital image processing and computer vision which can be applied in various applications, such as ATM machines, automatic money exchange machines, and mobile banking applications. This research aims to develop a classification model that is able to differentiate between 5000 thousand rupiah and 2000 thousand rupiah currency using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because of its ability to recognize complex visual patterns and specific features from images. The dataset used in this research consists of 10 currency images of 5000 thousand rupiah and 10 images of 2000 thousand rupiah taken in lighting conditions and viewing angles vary and are classified into 2 classes. The data is then processed and normalized to increase model accuracy. The proposed CNN model, namely the Squential Model, consists of several convolution layers, pooling layers, and fully connected layers which are optimized to detect visual differences between the two types of currency.  

Optimal health services require supporting examinations in diagnosing a disease. On lumbosacral examination, radiograph quality is often less than optimal. Digital radiography is a new system on x-ray machines that has features for better image processing, one of which is flexible noise control (FNC). This study aims to determine the effect of digital radiography processing settings on image quality on lateral position lumbosacral examination. In this study using a qualitative descriptive method with an experimental or experimental research approach. The results showed that there was an effect of digital radiography processing settings on image quality with the FNC feature. This was shown by using the Image J application, there was a change in the standard deviation value before and after processing with the FNC feature. It is known that the FNC feature suppresses the noise value in the radiograph images.

Diagnosis of a disease is usually carried out in a laboratory test, where this test requires a lot of money and time. As time goes by in the world of health, there is one method that can be used to detect the level of condition in the body by recognizing the patterns that form on the iris of the eye or better known as Iridology. By using digital image processing, the disease diagnosis process using the iridology method can be carried out. The aim of this program is to identify diabetes milletus by processing digital images in the form of iris images using Matlab . The research data used comes from the open source website, namely Github , which consists of diabetic iris image data and normal iris image data. Data is taken in the form of digital images. In this research there are several stages, including pre-processing, feature extraction, and classification. The feature extraction process is based on statistical characteristics (contrast, correlation, energy, homogeneity) from the iris image, then classified using the K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) as a comparison algorithm. The results obtained from this research show that K-NN has better performance with accuracy values ​​of 84%, sensitivity 72%, and specificity 96%.    

implemented and evaluated the edge detection method using the Sobel Operator, which calculates the gradient of image intensity through two convoluted kernels for horizontal (Gx) and vertical directions. (Gy). The magnitudo gradient is obtained from the combination of both such directional gradients to represent the edge force on each pixel. The main steps include image pre-processing, the application of the Sobel kernel, the calculation of magnitudo gradients, and the filtering of results to extract significant edges. The results show that the Sobel Operator is effective in highlighting intensity differences that indicate the boundary of the object, although it is sensitive to noise and less accurate for fine edges. Despite its limitations, this method is simple to implement and useful as an initial step in image processing applications such as segmentation, pattern identification, and object shape analysis.  

Multispectral image is a type of digital image that captures spectral information in several channels or bands. Edge detection is one of the basic techniques in image processing which is used to identify the boundaries of objects in an image. This research aims to analyze the performance of several edge detection algorithms on multispectral images. The algorithms tested include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian (LoG) algorithms. Tests were carried out on high resolution multispectral images from the Landsat-8 satellite. The evaluation metrics used are accuracy, precision, recall, and F1-score. The research results show that the Canny algorithm has the best performance with the highest F1-score compared to other algorithms. Apart from that, this research also analyzes the effect of the number of channels in multispectral images on the performance of edge detection algorithms.