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LAPORAN PRAKTIK MAGANG PERANCANGAN SISTEM PENCATATAN KEUANGAN DOMPET IBU SEBAGAI UPAYA OPTIMASI PENGELOLAAN KEUANGAN DIGITAL KELUARGA DI PIMPINAN RANTING AISYIYAH KAUMAN YOGYAKARTA
No full textPERANCANGAN DESAIN USER INTERFACE/USER EXPERIENCE APLIKASI LELANG “LERAN” (LELANG RANDOM)
Full text linkDesigning UI/UX on Adaptive Skills Learning Application for Autistic Children Using Design Thinking Method and Applied Behavior Analysis Theory
Full text linkThis paper introduces he design of SemaiSelaras, an adaptive learning application tailored for children with Autism Spectrum Disorder (ASD), utilizing the Applied Behavior Analysis (ABA) theory and developed through the Design Thinking (DT) methodology. The application aims to address challenges faced by children with ASD in acquiring essential adaptive living skills. While prior studies have explored applications employing the DT methodology, this research uniquely focuses on integrating ABA theory to better meet the specific needs of users. The user-centered and iterative nature of DT ensured the application was designed to effectively address these requirements. The ABA approach, which breaks learning materials into manageable steps, supports children with ASD in gradually mastering life skills. SemaiSelaras integrates advanced technologies such as Optical Character Recognition (OCR), digital storyboard, audio discrimination learning, and video-based learning. The research contribution emphasizes the role of ICT in supporting accessibility and inclusion, helping children with ASD develop essential life skills. Usability testing was conducted using the System Usability Scale (SUS) and the SemaiSelaras prototype achieved an average score of 86.5, reflecting an excellent rating and demonstrating a high level of acceptance and usability for the application
Optimizing Cleaning Path for Coal Dust Removal Using Dual Stage Tracking Method
Full text linkManual disaster mitigation at the Java Bali power plant, particularly related to fire risks from coal dust during electricity production, often requires halting operations, leading to significant revenue loss and power outages. This study aims to address this issue by proposing an automated solution to clean coal dust without interrupting production, utilizing a dual-stage tracking method for robot-assisted coal dust cleaning. The research contributes by developing a dual-stage A* algorithm that optimizes robot movements for cleaning tasks in power plant environments, outperforming single-stage BFS and single-stage A* algorithms. The research is divided into two phases: object detection and robot motion path selection. The dual-stage A* algorithm is compared against single-stage BFS and single-stage A* methods through a series of experiments evaluating their efficiency and effectiveness. The dual-stage A* method demonstrates superior performance in terms of path optimization, reducing cleaning time, and improving operational safety. Specifically, the dual-stage A* algorithm reduces energy consumption by 169 units and grid traversal by 84 units compared to single-stage methods, ensuring thorough dust removal while minimizing fire hazards. The dual-stage A* algorithm proves to be the optimal solution for coal dust cleaning in power plants, allowing for safe, continuous operation without the need for production halts. Future work should focus on addressing implementation costs and technical constraints to enhance real-world applicability
