The agricultural industry plays a vital role in the global demand for food production. Along with population growth, there is an increasing need for efficient farming practices that can maximize crop yields. Conventional methods of harvesting lettuce often rely on manual labor, which can be time-consuming, labor-intensive, and prone to human error. These challenges lead to research into automation technology, such as robotics, to improve harvest efficiency and reduce reliance on human intervention. Deep learning-based object detection models have shown impressive success in various computer vision tasks, such as object recognition. RetinaNet model can be trained to identify and localize lettuce accurately. However, the pre-trained models must be fine-tuned to adapt to the specific characteristics of lettuce, such as shape, size, and occlusion, to deploy object recognition models in real-world agricultural scenarios. Fine-tuning the models using lettuce-specific datasets can improve their accuracy and robustness for detecting and localizing lettuce. The data acquired for RetinaNet has the highest accuracy of 0.782, recall of 0.844, f1-score of 0.875, and mAP of 0,962. Metrics evaluate that the higher the score, the better the model performs.

One of the uses of information technology is augmented reality technology in the health sector. Augmented reality is used in the development of applications that are used for speech therapy for children with autism or children with speech delays. The method used for the development of speech therapy applications is the extreme programming method. This method can adapt to the development of an application in a short time and quite a lot of changes. The stages in the extreme programming method include identifying system requirements, planning activities during system/application development, system development process, iteration for system improvement until the final iteration, and no more user feedback, system/application production, and system maintenance with data backup and system recovery. After testing the system, it was concluded that three iterations occurred during the development of the speech therapy application. The last test showed that the user accepted the speech therapy application with a percentage of 77,14%. The output of this research is an augmented reality-based speech therapy application that is useful for independent speech therapy for children with speech delays.

The use of technology can transform conventional systems into electronic-based systems. Electronic systems have been widely used in governance, organizations, and companies where administration is carried out electronically (e-Government). Hospitals usually already have systems in place, but they are not yet integrated, including integration with BPJS Services, EClaim, and the SATUSEHAT Platform, a new policy from the Ministry of Health Republic of Indonesia starting July 2022. BPJS integration includes diagnosis standards guided by Minister of Health Regulation No. 76 of 2016 concerning INA-CBGs Technical Guidelines, funds application to BPJS, cost proportions, and medical personnel fees. Another service at the Teaching Hospital is the management of Education for Professional Doctors (Co-ass) and Specialists (Residents). Another service at the Teaching Hospital is the management of Education for Professional Doctors (Co-ass) and Specialists (Residents). The solution provided is to create E-Hospital. It is an integrated hospital management information system with an SSO Model and Multi-Channel Access Technology for notification. This system consists of Front Office Modules, including Admission Queues, Medical Services, Pharmacy, Employment, Payroll and Medical Personnel Fees, Automatic integration with BPJS, EClaim, SATUSEHAT, Finance, and Warehouse and Equipment.

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.

The agricultural sector in Indonesia is still faced with low agrarian production caused by pests and diseases. Therefore, agricultural land that is still vulnerable to pests but can detect the development of pest attacks must be designed. This study uses the PlantVillage dataset. The dataset will go through the preprocessing stage for dimension adjustment, and then the result will be used for building the network. The results are evaluated using a confusion matrix and showed that the convolutional neural network performs well in image processing and obtains architectural optimization in its field. The method we propose is an Efficient Network by selecting the correct input size. Implementing an Efficient Network in the convolutional neural network architecture increases its F1-score to 93%, indicating that Efficient Network has a higher F1-Score than the baseline convolution neural network. Implementing this network architecture can quickly increase the CNN baseline to a more varied target resource while maintaining the efficiency of the resulting model.

Information System, Data Analysis, Natural Language Processing, Neural Networks, Pattern Recognition, Internet of Things (IoT), Remote Sensing, Image Processing, Fuzzy Logic, Bioinformatics/Biomedical Applications