Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compare some background subtraction algorithms to detect and count moving sperm in microscopic videos of sperm fluid, so we get a background subtraction algorithm that is suitable for the case of sperm detection and sperm count. The research methodology begins with the acquisition of sperm video data. Then, preprocessing using a Gaussian filter, background subtraction, morphological operations that produce foreground masks, and compared with moving sperm ground truth images for validation of the detection results of each background subtraction algorithm. It also shows that the system has been able to detect and count moving sperm. The test results show that the MoG (Mixture of Gaussian) V2 (2 Dimension Variable) algorithm has an f-measure value of 0.9449 and has succeeded in extracting sperm shape close to its original form and is superior compared to other methods. To conclude, the sperm analysis process can be done automatically and efficiently in terms of time.

Brain tumor diagnosis is challenging due to complex brain anatomy and tumor variability across imaging views. Traditional methods are manual and error-prone, making deep learning, particularly ResNetbased Convolutional Neural Network (CNN), essential for improving accuracy. The research investigates the enhancement of brain tumor classification using Magnetic Resonance Imaging (MRI) images through a novel modification of the ResNet50 model. It specifically addresses data imbalance challenges in medical image analysis. By proposing a targeted approach to partial data augmentation, the researchers aim to overcome limitations in traditional deep-learning classification methodologies, particularly the performance bottlenecks encountered in differentiating complex brain tumor subtypes. The research uses MRI dataset containing 5,249 labeled images (glioma, meningioma, pituitary, no tumor) across axial, coronal, and sagittal planes, highlighting class and view-based imbalances addressed through targeted augmentation. The research employs transfer learning to analyze three scenarios: non-augmented, partially augmented, and rounding-down data. Results reveal that the partially augmented scenario achieves the highest classification accuracy at 85%, significantly surpassing the non-augmented scenario, which peaks at 79%. In contrast, the rounding-down scenario yields only 60.16% accuracy during validation, highlighting the negative impact of drastically reducing data quantities. The unique contribution lies in demonstrating how strategic partial augmentation can enhance pattern recognition and mitigate overfitting risks, particularly in medical imaging where precise differentiation is crucial. The findings highlight the critical role of nuanced data distribution in enhancing model robustness, as evidenced by improved pattern recognition and reduced overfitting risks in the augmented scenario.

Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

Graph-QL (Query Language) is a new concept in the Application Programming Interface (API). Graph-QL was developed by Facebook which is implemented on the server-side. Although it is a query language, Graph-QL is not directly related to the database, in other words, Graph-QL is not limited to certain databases, either SQL or NoSQL. The position of Graph-QL is on the client and server-side that access an API. One of the objectives of developing this query language is to facilitate data communication between the backend and frontend / mobile applications. For this reason, this paper will examine the responsibility of Graph-QL in terms of response time and response size in the development of an integrated competency certification test system based on web service and compared with efficiency and flexibility using the REST API. From the test results, it was found that Graph-QL provided some advantages compare to REST API. It give more flexibility for the clients to access the data and solve the most typical problem that was over or under fetching cause by fixed data given by REST API endpoints.  

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.

The development of an interactive SIOBEL (Sistem Informasi Bela Neagara) application requires the application of User Centered Design in the UI/UX design phase. In this context, User Centered Design becomes an important cornerstone in ensuring an optimal user experience and meeting the needs of users when using the SIOBEL application as a platform to integrate state defense values with oubound. By applying UCD, the UI/UX wireframe design of the SIOBEL application can create a better and satisfying user experience. Users will feel engaged and comfortable when using the application.