In a Saheeh hadith it is explained that the cat is a clean animal that is free from najis, so keeping a cat is not something that is forbidden. One of the things that is important when keeping a cat is feeding. However, keeping a cat at home takes time and effort. In this digital era, the use of technology has penetrated all aspects of life. The objective of this research is to create a monitoring system for an automatic cat feeder using a webcam and a stepper motor that is connected to a Raspberry Pi as the main controller. The webcam can take pictures (photographs or videos) processed with the fswebcam and the avconv functions on the Raspberry Pi. The stepper motor can rotate the feed valve by utilising a General-Purpose Input Output (GPIO) pin and a program which is inserted into the Raspberry Pi. Next, the Raspberry Pi will be connected to the Internet and a server network so that the system control can be done remotely by using a web browser or web view on a mobile. The overall function of the system in the form of feeding the cat either directly or scheduled, as well as monitoring of photographs or videos around the feed. The results of a questionnaire showed that this system has a need value of 87.3% of 79 respondent cat keepers, meaning cat keepers will be greatly helped by the existence of this system.
Sign language is a non-verbal language that Deaf persons exclusively count on to connect with their social environment.The problem that occurs in two-way communication using sign language is a misunderstanding when learning new terms that need to be taught to deaf and mute people. To minimize these misunderstandings, a system is needed that can assist in correcting hand gestures so that there is no misinterpretation in teaching new terms. Several optimality properties of PCA have been identified namely: variance of extracted features is maximized; the extracted features are uncorrelated; finds best linear approximation in the mean-square sense and maximizes information contained in the extracted feature. The classification uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. From the results of experiments with different image size variables, the largest accuracy was obtained with an image size of 449x449 of 76.20%. While the lowest accuracy of 52.38% is obtained through scenarios with image sizes of 57x57 and 45x45. Therefore, differences in the use of image sizes have an influence on the accuracy of hand signal prediction. The smaller the size given, the smaller the accuracy obtained. This is indicated by the decreasing accuracy value when given a smaller size in the four scenarios that have been studied.
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