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Along with the times, computer technology is developing very rapidly. The increasingly rapid development of computer technology means that everyone is required to utilize computer technology in their daily lives. Utilization of technology is one of the implementation roles of scientific disciplines. The reason behind the formation of this research is so that in the future it will become a fun learning concept in the introduction of objects and shapes in children and the motor development of children. children are usually more interested in seeing pictorial text, or pictures that contain lots of color. The Viola Jones method itself was chosen as the research completion algorithm. The Viola Jones method is usually used as a method in research that discusses the detection of objects, faces and others. The Viola Jones method was chosen because it has a high level of accuracy that can reach 100% probability.

Tiku Beach in Tanjung Mutiara District, Agam Regency, West Sumatra is a 12.77 km coastal area experiencing continuous shoreline changes due to abrasion and accretion. This study aims to identify and map shoreline changes and calculate the area of abrasion and accretion in 2014, 2019, and 2024 using the Modified Normalized Difference Water Index (MNDWI) method based on Landsat 8 OLI satellite imagery. The MNDWI method utilizes the reflectance difference in the Green band (Band 3) and SWIR band (Band 6) to automatically separate the land-water boundary. Shoreline change analysis was performed using Net Shoreline Movement (NSM) and End Point Rate (EPR) methods through ArcGIS 10.8 software with the Digital Shoreline Analysis System (DSAS) extension. Analysis of 336 transects shows that accretion is more dominant than abrasion along Tiku Beach. The largest accretion was recorded at transect 230 with an NSM value of 71.3 m and an EPR rate of 7.12 m/year, while extreme abrasion occurred at transect 249 with an NSM value of -121.67 m and an EPR rate of -12.15 m/year. The evolution of the shoreline shows that in 2014 the coastline was still relatively stable, then in 2019 mild abrasion occurred in the west along with accretion in the east, and by 2024 this pattern became more pronounced. The results of this study are expected to serve as a scientific basis for decision-making in coastal disaster mitigation planning and sustainable coastal management in Agam Regency.

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.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

This community service activity aims to enhance the creativity of UIN Palangka Raya students by producing functional herbal tea bags using cat's whiskers (Orthosiphon aristatus) and lemongrass (Cymbopogon citratus). The activity was implemented using a Participatory Action Research (PAR) approach, emphasizing active student participation in all stages of the production process. Participants, third-semester students of the Biology Education Study Program, were introduced to the health benefits of both herbs and were encouraged to practice drying ingredients, weighing formulations, and packaging the products. The results of the activity indicated that students gained a comprehensive understanding of the bioactive compounds in cat's whiskers and lemongrass, proper processing techniques to maintain ingredient quality, and the ideal formulation for making herbal tea bags. Students were also able to produce safe, hygienic, and functional tea bags. This activity has proven effective in enhancing students' knowledge, skills, creativity, and ability to develop innovative products based on local natural ingredients.

In life as a congregation and as servants of God, we cannot escape conflict within the church. Sometimes, conflict is inevitable within a church. This is because sometimes things start out fine, but when conflict arises, each individual makes their own decisions. Some choose to remain on the other side, while others choose to leave the fellowship. This article aims to explain the factors that cause divisions in the church, specifically the GKSI Makarios church, and what actions God's servants should take when faced with such a situation. Of course, this article uses two sources to resolve this issue, namely interviews and data such as books or other reading sources that discuss similar events related to the topic. It is hoped that this article will be a valuable lesson for ministers and congregations in dealing with every situation in the church. It is important to discuss this topic in order to educate readers that disputes are inevitable in church organizations. Therefore, when joining a community, it is important to consider the best solutions for dealing with disputes so that one does not act rashly when trying to resolve problems properly.

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.

Manual collection of vehicle license plates is often inefficient and prone to errors, so an automatic identification system is needed. This research aims to implement and evaluate the performance of a license plate character detection system, focusing on the accuracy comparison between black and white base plates in Indonesia. The method used is Optical Character Recognition (OCR) with image preprocessing workflow including Grayscale, Gaussian Blur, and edge detection implemented in Google Colab. The system was tested using 100 primary data samples consisting of 50 black base plates and 50 white base plates. The findings showed that the system achieved a combined average accuracy of 84.36%. Specifically, it was found that the accuracy on the black base plate (85.40%) was slightly superior to that on the white base plate (83.32%). The implication of this study is that the change in license plate standards has a measurable technical impact on the ANPR system, where the findings can serve as a foundation for developers to calibrate the system to be reliable on both plate types during the transition period.

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

Image processing is a branch of computer science that is growing rapidly and is widely used in various fields, including in security systems. Face identification is one of the main applications of image processing that aims to recognize and distinguish individual faces in a system. The methods used in face identification involve various techniques, such as facial feature detection, characteristic extraction, and classification using machine learning algorithms. This article discusses the application of image processing in a security system based on face identification and the technology used to improve the accuracy and reliability of the system. The results of the study show that the combination of deep learning algorithms with image pre-processing techniques can increase the success rate of face identification in security systems.

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.

In 2022, a significant fire incident occurred at a steel tower structure in an industrial plant in Lampung, Indonesia, raising serious concerns about the structural integrity and serviceability of the affected steel framework. Fire exposure is known to alter the properties of steel, weaken bolt pretension, and cause defects in welds, underscoring the necessity of thorough post-fire assessments. Nondestructive testing (NDT) methods are crucial in evaluating the safety and stability of structures after fire exposure, as they can detect potential weaknesses without compromising the material further. This study employed two field inspection methods: the bolt torque test and dye penetrant inspection (DPI). A total of 21 bolts (Tor-1 to Tor-21) were tested for their integrity. The results showed that, while all bolts were present, more than half were found to be loosened, indicating the need for re-tightening to restore the specified torque and maintain the required preload for the bolted connections. In addition, 20 welded joints (DP-1 to DP-20) were examined using DPI to detect surface defects. The inspection revealed that 10 welds (50%) exhibited surface defects exceeding 5 mm in length, indicating areas where the welds had been compromised by the fire exposure. Seven welds (35%) were found to be in acceptable condition, while 2 welds (10%) were incomplete or had poor bonding. These findings suggest that while the bolted joints can be restored through corrective re-tightening, the welded joints require more extensive evaluation, local repairs, or even rewelding to ensure their structural integrity. This study highlights the importance of NDT methods in post-fire structural evaluations and recommends periodic inspections and targeted rehabilitation to ensure the long-term reliability and safety of industrial steel structures.

Building fires can significantly degrade the strength and integrity of steel structures, so post-incident evaluation is crucial to ensure building safety and feasibility. This study aims to evaluate the condition of the steel tower structure after the fire through a visual inspection method. A total of 35 structural elements were examined, including columns, beams, and bracing, to identify damage caused by heat exposure. The inspection results showed that 6 elements (17%) were in the category of Acceptable, 8 elements (23%) Needs Attention, 5 elements (14%) Not Acceptable, and 1 element (3%) Not Applicable because they had been removed. Steel columns generally remain upright without deformation, but suffer damage to the protective layer (coating). In contrast, most blocks lose their protective layers, are directly exposed to fire, show early signs of corrosion, and some suffer severe damage such as flange tears and cuts. These findings confirm the importance of systematic documentation and classification of element conditions as the basis for technical decision-making for structural improvement. Visual inspection proved effective as an initial step in the evaluation process, providing a relevant initial picture of the extent of damage and the need for intervention. This study recommends follow-up in the form of advanced structural analysis and material testing to ensure the feasibility of reusing the affected steel elements.

This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

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.

Ekklesia School is a holistic school that prioritizes the personal development of its students. The limited information about Ekklesia School, including its teaching system, facilities, and the quality of its teachers, has resulted in a low level of awareness about the school, This has resulted in a lack of students enrolling in the school. Therefore, there is a need for informational media that can summarize information about Ekklesia School through audiovisual media, such as a video profile. Research in the form of a video profile with a clear message and information, supported by strong visuals and narration, can help the audience understand the message conveyed in the video profile. This research applies qualitative methods and a linear strategy in its design process, enabling the delivery of current and in-depth information and messages about Ekklesia School