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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.      

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.

The development of image processing technology today can create systems that are able to effectively recognize digital images, one of which is in the field of agriculture for plant disease identification. Citrus plants experience a decrease in productivity due to pathogen attacks on leaves such as Black Spot, Cancer, and CVDP so that disease identification is needed. The classification method that can be used to classify images is the K-Nearest Neighbor (K-NN) algorithm because it is simple and has high accuracy in image management. This study aims to implement and determine the performance of the K-NN algorithm in identifying citrus plant diseases based on leaf images. This research uses a dataset from the Kaggle website of 1,096 images. There are 12 research scenarios using the comparison between test data and training data as much as 4, namely (90% training data + 10% test data, 80% training data + 20% test data, 70% training data + 30% test data, 60% training data + 40% test data) and testing with 3 random state values (42, 32, 22). The results showed that the K-NN algorithm is very effective in identifying citrus plant diseases with the highest accuracy value in the 90% training data scenario and 10% test data with a value of K = 2 which is 98.5%.

Digital Pathology Image Analysis (DPIA) is one of the areas where deep learning (DL) techniques offer modern, cutting-edge functionality. Convolutional Neural Network (CNN) technology outperforms the competition in classification, segmentation, and detection tasks while being just one of numerous DL techniques. Classification, segmentation, and detection methods can often be used to address DPIA concerns. Some difficulties can also be resolved using pre- and post-processing techniques. However, other CNN models have been investigated for use in addressing DPIA-related issues. Furthermore, the research seeks to explore how susceptible the model is to adversarial attacks and suggest strategies to counteract them. To predict ischemic strokes caused by blood clots, the authors of this study developed CNN with a pixel brightness transformation (PBT) technique for image enhancement and developed several approaches of image augmentation techniques to increase and provide the learning model with more diverse features. Also, adversarial training was integrated into CNN models to train the model with perturbed data in order to assess the impact of adversarial noise at different stages of training. Several metrics, including precision, F1-score, accuracy, and recall, are utilized to assess the experiments' effectiveness. The research findings indicate that employing transfer learning with a deep learning model achieved an accuracy of up to 97% using the ReLU activation function. Also, data augmentation helps improve the accuracy of the model.

Sabu woven fabric is one of the cultural heritages of Sabu Island. In addition to being a cultural heritage, Sabu woven fabric is one of the handicrafts that still exist today which is preserved by Sabu women. Based on its manufacture, the classification process of Sabu woven fabric is based on color or motif identification. However, the classification process is not an easy process, because the classification process requires time and experts in the field of Sabu woven fabric. In addition to the classification process, the wider community also does not get much information about Sabu woven fabric clearly, because it is necessary to introduce the type of Sabu woven fabric, so that people can know or recognize the type of Sabu ikat woven fabric based on its type. Digital image processing techniques are utilized to build a system that can overcome the problems faced. Furthermore, image feature extraction will be carried out using gray level co-occurrence matrix (GLCM) with 4 features namely contrast, correlation, energy, and homogeneity with angles of 0°, 45°, 90°, and 135°. Each GLCM feature shows the same value even though the original image is rotated. After image feature extraction, the extracted data will be classified using the TensorFlow library. From these results it can be concluded that the program succeeded in selecting the type of Sabu ikat woven fabric class.

Computed Radiography (CR) is a radiographic system that can convert analog signals into digital signals so that they are easily processed by image processing , to deal with the inconsistency of image quality from errors in lighting . One of the problems in CR is that the radiograph results are less than the maximum and can inhibit or reduce the ability to diagnose. This study aims to determine how the quality control of CR. This type of research is carried out in literature ( library study) that is by reading literature that has to do with the research conducted, as well as from other sources relating to Quality Control Computed Radiography (CR)  and draw conclusions based on existing data. Based on the available literature, it can be concluded that the quality control on Computed Radiography (CR) is different but it does not reduce in terms of improving the quality of Computed Radiography (CR) in producing quality radiographs and can establish diagnoses.

Convolutional neural network (CNN) is a deep learning (DL) model that has significantly contributed to medical systems because it is very useful in digital image processing. However, CNN has several limitations, such as being prone to overfitting, not being properly trained if there is data duplication, and can cause unwanted results if there is an imbalance in the amount of data in each class. Data augmentation techniques are used to overcome overfitting, eliminate data duplication, and random under sampling methods to balance the amount of data in each class, to overcome these problems. In addition, if the CNN model is not designed properly, the computation is less efficient. Research has proved that data augmentation can prevent or overcome overfitting, eliminating duplicate data can make the model more stable, and balancing the amount of data makes the model unbiased and easy to learn new data as evidenced through model evaluation and testing. The results also show that the custom convolutional neural network model is the best model compared to ResNet50 and VGG19 in terms of accuracy, precision, recall, F1-score, loss performance, and computation time efficiency

Main Objective: This research targets AP sizing entrenched on image structure to raise denoising performance using an improved method for classifying image pixels. Background problem: Digital images may be blended by noise while the addition or communication process, affecting the authentic image signal. Image noise can cause problems at several stages of image processing equally image distribution. Accordingly, image denoising is a significant activity to recover the initial clean image signal from the detected noise signal. Novelty: The proposed WAV method has been refined and improved regarding the classification scheme, and the APS, and the classification results can be used as a mask on the noise image to fix identical patches. Research Method: This study proposes a WAV reprojection algorithm, with the PS being set dynamically entrenched on the image structure. Image structures are consistently taken with an upgraded and enhanced analysis method entrenched in the structure tensor matrix. Analysis results are also used to develop the analysis of comparable patches in images. Finding/Result: Empirical outcomes present that the noise cancellation work of the suggested method is better than the authentic WAVRA, along with several other modifications of the NLMA. Conclusion: In intensity profiles, the proposed method mostly has fewer changes to the original image values than other methods, thus, this method can be continued again to color images, and can also be applied to various types of data such as medical images.   Keywords: Weighted Average (WAV), Noise Cancellation Image, Non-Local Means (NLM), Adaptive Patch

Adapun penelitian pendahuluan yang pernah dilakukan diantaranya adalah penelitian yang berkaitan dengan pendeteksian warna, klasifikasi berdasarkan warna tersebut dan penentuan pemilahan berdasarkan kriteria tertentu, mulai dari proses morfologi hingga metode center plotting. Target pada penelitian berikutnya adalah penggabungan analisis dari kedua hasil penelitian sebelumnya dan saat ini, yaitu klasifikasi warna objek dengan metode center plotting dan metode pencacahan objek pada suatu file citra, yang dihasilkan dari perangkat akuisisi data berupa kamera digital dan web camera. Konsep pencacahan objek ini merupakan sekelompok urutan sub proses yang cocok untuk objek yang di-capture dari webcamera dan terbukti dalam implementasinya mampu dilakukan dengan baik. Hasil pengamatan dalam penelitian ini mampu mencacahkan citra berwarna terhadap berbagai bentuk objek yang telah di-capture dengan baik melalui web camera, yang dibuktikan dengan perolehan hasil hingga 100 % dari 31 citra uji, namun hasil sebaliknya saat objek hasil capture dari kamera digital hanya 10 % dari 10 citra uji.   Kata kunci: pencacahan, counter, image processing