Covid-19 is something that was never expected, it can turn into an endemic virus in the community. There is a possibility that this virus will not be completely destroyed. This makes the world and Indonesia in an uncomfortable position. Two months with Social Distancing conditions, the Government of Indonesia has been preparing to roll back the sluggish economic wheel as a result of the implementation of Social Distancing. Therefore, the Indonesian people must live in peace with Covid-19 until the discovery of an effective vaccine. This condition is called new normal. This study designed a detection system of facial patterns using masks during the pandemic based on Real-Time Raspberry. The purpose of detecting face patterns by using a mask is to find out if there are masked faces in the image. Although it seems easy to do by humans, it turns out that this detection system is difficult to do without the help of a computer to process facial recognition because there are some difficulties related to location, point of view, light, and occlusion. This research has implemented a detection system using the Viola Jones method. Viola Jones method is a method to get fast, accurate and efficient results in face detection on images. This study using the Viola Jones method to adjust the threshold value, and form the Cascade Classifier in determining the face area in the image. This training can be evaluated the accuracy of the system by modifying the parameter values in the Viola Jones method so that this design can produce the highest accuracy for face images using masks and low accuracy for face images without using masks. From the results of trials with 100 face samples, the accuracy percentage is 90.9% and it takes a relatively short time to detect faces using a mask that is on average 15 seconds per sample tested.

The scholarship is a financial aid given to candidate student who is eligible. This aid is aimed to help the students to pursue their study. In UTM, the scholarship system awards applied to select the right candidate of college students is still based on the principles of the proximity of the campus party. The scholarship given isn't for the right target. This research implemented a system of determining admission scholarships using the method of SAW and TOPSIS as a solution to support decision makers based on criteria that have been defined, including GPA, income of the parents, the number of dependant of the parents, tuition fees, semester and the involvement in association. The result of some trials had been shows that the average accuracy for five years is 88.4% compared to manual calculations or the equivalent of 6 to 8 days.

The dormitory is one of the facilities provided with the aim of helping students to get a place to live because some of them come from distant places. The Trunojoyo Madura University dormitory has regulations that serve as a place for the process of character education, spiritual deepening, moral improvement. With these regulations, plus the boarding house is a conducive, economical, and strategic place to stay making many students interested in being able to live there. The problem is that each semester change is carried out by the selection of dorm residents and so far it is still done manually by way of discussion of each individual. Therefore, the purpose of this research is to help the board by building a decision support system in determining residents who are still eligible to live in a dormitory and provide opportunities for other students to live in a dormitory. We develop systems based on mobile applications. TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is implemented as a multi criteria decision making method with four main criteria including routine absence, non-routine absence, violation and notes. The final results of this decision support system are ranks and colors that indicate the status of boarding residents. Ranking starts from the top (1) to the bottom (216) accompanied by a status of green to red. The color status is divided into 3 namely green (safe), yellow (vulnerable), and red (issued). From 216 boarders we took 10 samples of dormitory data for testing. The results of trials with 10 data samples by applying the TOPSIS method obtained an accuracy of 90%.

Montoring is a systematic process of collecting data from that data to be evaluated to find out the quality of the system, along with times the monitoring and evaluation process is done using technology that can be web-based or android mobile, so that the process of monitoring & evaluation is easier and more efficient. In addition to getting an accurate assessment, it is necessary to apply a method that can process data into an objective assessment, the SMART method is a simple multi-attribute method that can be used in processing data into an accurate assessment. Trunojoyo Madura University Dormitory has many activities aimed at realizing the education of the dormitory character, the activity data is monitored and evaluated to provide an assessment of the activeness of students living in the dormitory. However, the process of monitoring & evaluation of the dormitory is still carried out manually, so an application for monitoring & evaluating the activities of the hostel which uses the SMART method is needed to facilitate the monitoring process and obtain an objective assessment result.

The development of technology, information and communication provides a new alternative to predict cow weight through Image Processing. This study utilizes Image Processing in visualizing the measurement of Chest Circumference and cow body length automatically. The cow weight estimation are very dependent on cow image segmentation result. Image segmentation method used in this study is local adaptive thresholding combined with the Connected Component Labeling (CCL) method. The implementation of the Chest Circumference and Body Length endpoints in the foreground is converted into centimeters (cm) to ensure cow weight estimation can be calculated using the Lambourne formula. In this study, the accuracy of RMSE was obtained from the cow weight data taken at 150, 170 and 190 cm distance. The accuracy is 20.35, 30.77 and 23.33 respectively. This research can be contribution to development of local cattle farms in Indonesia.