Publication Search

This research investigated the biomechanical variables of movement analysis and its essentialcomponents for handball players at the College of Physical Education and Sports Sciences, Al-MustansiriyaUniversity, during a league match and university qualifiers. The data was analyzed using binary logisticregression, a mathematical model that defines the link between a dependent variable, which takes the valueof one when a goal is scored against the opposing team and zero when no goals are scored, and independentfactors.The bird swarm algorithm will be used in this research. It is one of the artificial intelligencealgorithms that rely on the intelligence of living flocks by monitoring their movements, such as birds, bees,cats, chickens, and many other swarm algorithms.The conclusions we reached from this study are as follows: When using logistic regression, we foundonly four explanatory variables that affect the dependent variable. They are detailed as follows: The twoexplanatory variables (The maximum height of the hip and Flight time until leaving the ball) and thedependent variable (shooting) have an inverse relationship and affect it. The two variables (Knee angle at themoment of thrust and The instantaneous speed of the ball) have a positive relationship with aiming and affectit.When we used the Bird Swarm algorithm, we found that all the explanatory variables included in thestudy had a significant effect on the dependent variable. The variables (Knee angle at the moment of thrust,Rising angle, Flight angle, The instantaneous speed of the ball, and the horizontal distance of theperformance) have a positive relationship, with the dependent variable (shooting). In contrast (The maximumheight of the hip and Flight time until leaving the ball) have an inverse relationship with the dependentvariable.Using the logistic model helps sports coaches and researchers to estimate and predict models,especially when the dependent variable takes values ​​(one or zero). In contrast, we noticed that the resultswere more accurate and objective when using the bird swarm algorithm. It further helps academics, thoseinterested in sports, and coaches benefit from these results.

Fish waste and tempeh have significant nutritional advantages in making cat food. Some of the advantages that can be obtained from using these ingredients include the vegetable protein content in tempeh which is easier for cats to digest, making it very beneficial for cats with certain digestive sensitivities or allergies. Tempeh also contains essential amino acids that help meet the nutritional needs of cats, as well as support muscle growth and maintenance. Fish waste contains important nutrients such as 29.70% protein, 18.83% fat, 1.94% carbohydrates, 8.97% water content, and 1.07% crude fiber, which overall meet the nutritional needs of cats. In addition, the omega-3 fatty acid content in eggs is also useful for the health of the cat's skin and fur, making the food more nutritious. Tempeh is also known as first aid for cats with diarrhea, because the bacteria contained in tempeh can help neutralize harmful bacteria in the cat's intestines. By combining fish waste and tempeh in cat food, cats can get complete and balanced nutritional intake, while reducing feed production costs, making it a more environmentally friendly and economical choice.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

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.      

Cats are one of the beloved animals that require care and development. As beloved animals, cats have their own charm thanks to their different body shapes, eyes and fur colors. Cats whose pedigrees are officially registered as purebred cats. Cats, in Latin Felis silvestris catus, are a type of carnivore. The word cat usually means a domesticated cat but can also refer to large cats such as lions, tigers and leopards. Cats have been mixed with human life for at least 6000 years BC, since cat skeletons were discovered on the island of Cyprus (Arquitectura et al., 2015).  One of the diseases that often appears in cats is feline lower urinary tract disease (FLUTD), also known as feline urological syndrome (FUS), which is a health problem that often occurs in cats, especially male cats. This health problem attacks the cat's bladder and urethra. Urethral disorders are caused by the structure of the male cat's urethra, which is tube-shaped and has a narrow section, which often causes obstruction of urine from the bladder (VU) to the outside of the body. FLUTD includes several diseases that occur in the cat's urinary tract (Indonesia, 2022).  Knowledge about diseases in cats is one of the problems. Many owners do not realize that their cats are suffering from diseases that can cause death. The cause of the cat's death occurred due to the keeper's lack of knowledge regarding the disease and symptoms the cat was experiencing. Apart from that, the problem is that there are so few doctors available that they are difficult to find, and the information obtained is only in accordance with the condition of the cat when it goes to the vet. If you see other symptoms, like it or not, you have to consult the veterinarian again and it will take additional time and money. This problem can be solved with an expert system that can diagnose the disease suffered by the cat based on the selected symptoms.