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In accordance with Bylaws (ART) Article 6, the Head of the Tegalrejo City Muhammadiyah Branch (PCM) Yogyakarta fosters, empowers, coordinates, manages, and organises charity business. PCM has ten Muhammadiyah Branch Leaders (PRM), namely: Bangirejo, Bluyahrejo, Karangwaru Lor, Petinggen, Jatimulyo, Kricak Kidul, Bangunrejo, Sidomulyo, Tompeyan, and Tegal, while for Aisyiyah Branch Leaders (PRA), there are 11 branches, namely: Karangwaru Lor, Blunyahrejo, Bangirejo, Jatimulyo, Kricak Kidul, Bangunrejo, Sidomulyo, Tompeyan, Tegalrejo, and Sudagarn. PCM Tegalrejo Yogyakarta is led by the Aisyiyah branch. Lokam Wiratama 0102 leads the Muhammadiyah Youth Troop Preparedness Command. PCM Tegalrejo, Yogyakarta City, has a Muhammadiyah Charity Foundation, which includes 2 elementary schools, 1 Islamic boarding school, 22 mosques and prayer rooms, LazisMu, and BTM Charity High School Tegalrejo Mulyo Santoso, SMA Muhammadiyah 1 Yogyakarta, under the management of the Yogyakarta Muhammadiyah Regional Leadership (PWM). PCM faces a variety of challenges related to slow, inaccurate, and incomplete reporting. To monitor immovable and movable assets, Amal Usaha Muhammadiyah (AUM) collects non-integrated asset data. PCM Tegalrejo must resolve several problems in stages. This solution should involve building an information system using advanced technology to collect PRM, PRA, Ortom, and AUM assets, online news uploads to share PCM activities, and LAZIZMU donation and allocation data. The Simcarata Management Information System’s development results are beneficial for PCM Tegalrejo Yogyakarta.

SD Muhammadiyah Sangonan 1 has implemented fingerprint attendance, where teachers and staff record their attendance when they arrive and leave. Attendance records are still manually recorded by administrative personnel, and attendance reporting is limited to attendance recapitulation. In short, the efficiency of managing teacher and staff attendance administration is still low. Therefore, this research aims to improve the efficiency of teacher and staff administration through the implementation of image processing technology for fingerprint attendance. The planned stages of the research are planning, fingerprint attendance system development, administration system training, and program evaluation. In its implementation, this program has proven to be effective in improving the effectiveness of SD Muhammadiyah Sangonan 1 Godean’s school administration in terms of easier and faster attendance data processing. The outputs achieved include mass media news and videos.

Coronary artery disease (CAD) is a pathological condition that is often fatal and is the main cause of death throughout the world. Early detection of this disease is very important to avoid severe complications such as heart attacks and sudden death. This study employs artificial intelligence, specifically deep learning via Convolutional Neural Networks (CNNs), to enhance CAD detection. While CNN architectures like ResNet50V2 and MobileNetV2 exhibit satisfactory performance individually, they possess distinct strengths and weaknesses. ResNet50V2 requires significant computing resources, hindering its scalability, while MobileNetV2 struggles with extracting complex features from medical images. Therefore, this research aims to combine the EfficientNetV2B0, ResNet50V2, and MobileNetV2 using transfer learning techniques to enhance CAD detection. The methodology involves leveraging pre-trained models and fine-tuning them on a coronary artery disease dataset. Modified models, particularly EfficientNetV2B0 and MobileNetV2, achieve high accuracies of 94% and 86%, respectively, while ResNet50V2 yields 72%. However, combining the models boosts accuracy to 95%, addressing individual model limitations. The concatenated model demonstrates superior predictive capabilities, with more accurate predictions and fewer errors than individual models. [ABSTRACT FROM AUTHOR]