The creation of the website of MaQis Alhamidy 4 Islamic Boarding School aims to provide an effective means of information and promotion to support the Qismart self-service store managed by the pesantren. In today's digital era, the existence of a website is very important for organizations, including Islamic educational institutions such as Pondok Pesantren MaQis Alhamidy 4. This website is designed not only to convey information about pesantren activities and services, but also to promote the pesantren self-service which serves as one of the main sources of income. Website development is carried out through several stages, namely observation, identification, implementation, and evaluation. The KKN Inovasi Pesantren Group 02 team, consisting of 16 members, was directly involved in this process. In the observation stage, the team observed and recorded the phenomena that occurred in the pesantren. Problem identification was conducted through interviews with pesantren administrators to understand the needs and challenges faced, especially related to the self-service information and promotion system. The implementation stage involved information collection and assistance in using the website as an information medium. Evaluation was conducted to reflect on the process and results achieved. The results of this project show that the developed website successfully created an effective platform to introduce and promote Qismart Supermarket to the community. The website facilitates access to important information and strengthens the brandi.

In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.