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This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

In today’s digital era, social media has become a new space for individuals to develop their personal creativity. This study analyzes the content strategy of the TikTok account @buiramira in building audience engagement and understanding the impact it has on the account’s image. The background of this research stems from the phenomenon of increasing social media usage, particularly TikTok, as a means of communication, entertainment, and promotional media. Using McGuire’s information processing theory, the study finds that followers’ perceptions of the TikTok account @buiramira as a source of information for thesis learning are very positive, making it suitable as a learning platform. The content presented not only provides technical knowledge but also shapes students’ attitudes and behaviors in facing the thesis process.This research employs a descriptive qualitative method  with data collection techniques including content observation, interviews, and documentation. The results show that the @buiramira account successfully builds strong interaction with its audience through consistent uploads, the use of a communication style that feels close to viewers, and the utilization of current trends. In addition, the study finds that audience engagement is strongly influenced by a combination of content creativity, relevant topic selection, and the account owner’s ability to maintain emotional closeness with followers. These findings emphasize the importance of well-planned digital communication strategies in managing social media accounts, so that they can provide benefits both personally and professionally.

The increasing demand for low latency and high-throughput multimedia applications has spurred significant advancements in hardware software co design. This study explores the integration of custom digital signal processing (DSP) hardware accelerators with optimized software frameworks to enhance deep learning accelerated DSP tasks. The proposed co design approach significantly reduces latency and improves throughput compared to traditional software-only DSP implementations. Through the development of custom hardware accelerators built with FPGA technology, the system achieves up to a 1.85x reduction in latency and a 1.5x improvement in throughput for real-time multimedia tasks such as image recognition, video decoding, and audio processing. The combination of hardware and software optimizations allows for better resource utilization, enabling the parallel processing of computationally intensive tasks while the software framework handles less demanding operations. Additionally, the co design system demonstrated improved energy efficiency, making it highly suitable for embedded systems. The results show that the hardware software co design approach offers substantial advantages in performance, latency reduction, and energy efficiency, positioning it as a viable solution for real-time multimedia applications. The findings have important implications for applications requiring fast data processing, such as autonomous driving, healthcare, and disaster management. Future research could explore alternative hardware accelerators, advanced software optimizations, and AI-based resource management to further improve the system’s efficiency and scalability for more complex multimedia tasks.

Adulteration of rice bran is commonly done by mixing it with materials of similar appearance but lower nutritional value, such as ground rice husk. A key indicator of such adulteration is increased lignin content. Adding phloroglucinol solution to the mixture produces a red color that varies with lignin levels. This study aims to estimate lignin content in rice bran-husk mixtures using artificial intelligence and digital image processing. YCbCr color model images of eleven rice bran-husk compositions, treated with phloroglucinol, were analyzed. The lignin content of each variation was measured in the lab and used to define eleven classes. A Probabilistic Neural Network (PNN) was employed as the classifier, with image histograms of varying bin sizes as input. PNN performance was evaluated using 4-fold cross-validation. Results showed the highest average accuracy of 85.80% with 32 bins and histograms from all three YCbCr channels.     

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

In today's digital era, the need for fast, accurate, and responsive information systems is increasingly pressing, especially in the higher education sector where prospective students often face obstacles in directly obtaining relevant and reliable academic information. To address this challenge, this research focuses on the design and development of an academic service chatbot by implementing the Retrieval-Augmented Generation (RAG) method at Catur Insan Cendekia University. The RAG approach combines information retrieval capabilities from various documents (retrieval) with the generative intelligence of language models, thus being able to produce contextual, personalized, and data-driven answers. The chatbot system was developed using Python, LangChain, FAISS, and the GPT model as the core of natural language processing. Performance evaluation was conducted using the ROUGE metric, which showed quite good results with a ROUGE-1 value of 0.50 and a ROUGE-L of 0.48. These findings prove that the system is capable of providing relevant and high-quality responses in helping answer prospective students' academic questions. With these advantages, this chatbot is expected to be an innovative solution to improve the quality of academic information services at UCIC, as it can present data quickly, accurately, interactively, and automatically. Furthermore, the implementation of this artificial intelligence-based technology aligns with digital transformation efforts in higher education, supporting the efficiency of academic services and strengthening the institution's image as a modern campus that adapts to developments in information technology.

This study aims to design and implement a damage analysis system for concrete surfaces by utilizing digital image processing based on the Canny edge detection method. The developed system allows users to upload images of concrete surfaces, which are then processed through several stages: conversion to grayscale, transformation to binary images, and crack edge detection using the Canny operator. This process aims to automatically detect crack patterns on the concrete surface. The detection results, represented as edge lines, are used to calculate the percentage of the damaged area. Based on this percentage value, the system automatically classifies the damage level into light, moderate, or severe categories. System testing shows that the Canny method can accurately identify crack patterns, with sufficient detection levels to be used in monitoring the condition of concrete surfaces. The analysis results are then presented in both visual and numerical forms, providing valuable information for assessing the structural condition of concrete. Thus, this system can serve as an efficient and effective tool for early detection of structural damage in concrete infrastructure, ultimately supporting better maintenance and repair efforts.

Indonesia is the world's leading producer of spices, but it still faces challenges in manual visual quality assessment, which is inconsistent. This study aims to develop a spice quality classification system using a Deep Learning approach based on Convolutional Neural Networks (CNN). Data was collected through digital images of five types of spices (cloves, cardamom, cinnamon, pepper, and nutmeg) classified into two categories: good and bad. The dataset was then processed and used to Train the CNN model using Tensorflow. The model architecture consists of several convolution, pooling, and dense layers, and is integrated into a web-based prototype application using Streamlit. Evaluation results show that the model achieves high Accuracy of 98.86% (Training), 98.45% (Validation), and 98.45% (Testing). The prototype application can provide automatic Predictions of spice quality through a simple and responsive interface. The results of this study indicate that CNN is effective in identifying the visual quality of spices and can serve as an objective, efficient technological solution that supports the enhancement of Indonesia's spice export competitiveness.

Digital image processing is a rapidly developing branch of computer science and has many applications in everyday life. One of the fields that most often utilizes this technique is object detection and color identification in images and videos. This study specifically aims to implement the thresholding method in the HSV (Hue, Saturation, Value) color space to detect three basic colors, namely red, green, and blue, in digital images. The research process begins with uploading images using the Google Colab platform, a cloud-based computing environment that makes it easy for users to run Python programs without requiring additional software installation. After the image is uploaded, the next step is to convert it from the RGB (Red, Green, Blue) color space to the HSV color space. This conversion is important because the HSV color space is more suitable for use in the color segmentation process. The Hue value represents the type of color, Saturation shows the level of saturation, while Value describes the level of brightness. Once the image is in the HSV color space, the next step is to determine the HSV value range for each basic color. This range is determined based on experimental results and references from related literature. Using this range, masking is performed to extract the appropriate pixels so that only the red, green, or blue portions of the image are visible, while the other colors are reduced. The results show that the thresholding method in the HSV color space is capable of detecting primary colors with a good level of visual accuracy, especially in simple images with contrasting backgrounds. The implementation of this program is relatively lightweight, easy to run directly in Google Colab, and does not require high-spec hardware. Therefore, this method is very suitable for use as basic learning material for digital image processing, both for students and novice researchers.

This study aims to determine audience perceptions of the Instagram Reels advertisement content of Wardah Matte Lip Cream "Tasya Farasya Approved" edition. This advertisement is the result of a collaboration between the local cosmetics brand Wardah and the famous beauty influencer Tasya Farasya. This phenomenon is interesting to study considering the trend of influencer-based marketing is increasingly dominant in beauty product promotion strategies, especially in building emotional closeness with consumers and maintaining loyalty to local brands. Consumer perception is an important aspect because it will influence purchasing interest, brand image, and future purchasing decisions. This study uses the perception theory from Jalaludin Rakhmat (2015) which explains that perception is a person's experience of an object or phenomenon obtained through the process of processing, interpreting, and drawing conclusions from the information received. Based on this theory, this study adopted a quantitative descriptive approach. Data were collected by distributing questionnaires to 100 respondents who are followers of Wardah's official Instagram account. Respondents were selected using purposive sampling to ensure the relevance of the data to the research focus. Descriptive data analysis was conducted using SPSS software to identify audience perception patterns regarding advertising content elements, such as visual appeal, message relevance, influencer credibility, and the product image's alignment with Wardah's brand image. This analysis is expected to reveal the extent to which brand and beauty influencer collaborations can create positive perceptions in consumers' minds. The research findings are expected to contribute to the development of digital marketing strategies in the cosmetics industry, particularly in utilizing influencer marketing effectively. Furthermore, the findings of this study can serve as a reference for other local brands in designing promotional campaigns that are relevant, engaging, and aligned with the characteristics of their target audience on social media.

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

Photogrammetry is an important technique in the reverse engineering process to reconstruct 3D images without requiring initial design data. This technique allows the creation of digital models of physical objects through processing a series of two-dimensional images. This study aims to analyze the effect of camera ISO settings and shutter speed on the level of dimensional deviation in the reconstructed 3D model. The method used is an experiment with a quantitative approach, involving a series of software for the 3D image reconstruction process, mesh structure preparation, and digital dimension measurement. The ISO variations used in the image capture were 250, 400, 500, and 800, while the shutter speed variations applied included 1/125 second, 1/100 second, 1/50 second, and 1/25 second. The test object was a cylinder with an actual diameter of 50 mm. The obtained 3D model results were compared with the actual dimensions through a Two-Way ANOVA statistical analysis to test the significance of the influence of both variables. The results showed that both ISO and shutter speed had a significant effect on the dimensional deviation of the 3D model. The combination of camera settings with ISO 500 and shutter speed 1/125 second produced the smallest deviation, while the combination of ISO 800 and shutter speed 1/25 second gave the largest deviation. The coefficient of determination (R²) value of 99.02% indicates that the statistical model used is very strong in explaining the variation of deviation. This research contributes to the optimal setting of camera parameters to improve the accuracy of photogrammetry results in reverse engineering applications.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

Automatic object detection is one of the crucial aspects in the field of digital image processing that plays a vital role in various modern applications, ranging from security systems to pattern recognition in medical and industrial fields. This study aims to implement an automatic object detection method with a digital image representation-based approach using MATLAB software. The main focus is directed at the pixel-based image processing process, where each image element is processed to extract relevant visual information. In this study, stages are carried out starting from image acquisition, color conversion, image quality enhancement, threshold-based segmentation, to extraction of targeted object features. Digital images are analyzed through transformation into grayscale and binary forms to facilitate the detection process. The use of MATLAB provides flexibility in numerical and visual data processing, and supports various efficient image processing libraries.

Object detection in digital images is a crucial aspect of image processing and computer vision, with applications ranging from surveillance systems and robotics to image-based search. One commonly used approach is template matching, a technique that compares a template image with sections of the target image to identify similar patterns. This study explores the implementation of the template matching method for object recognition in digital images. The process begins with image preprocessing to enhance data quality, followed by a matching procedure using normalized cross-correlation. Experimental results indicate that this method can accurately detect objects under stable lighting and scale conditions. However, its performance decreases when images undergo rotation or scale variations. Therefore, while template matching proves effective under ideal conditions, further methodological development is needed to improve its robustness against geometric transformations.s