Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.
— The Software Engineering and Information System Laboratory of Sriwijaya University is in charge of building a technology-based system, in accordance to the strategic objectives of Sriwijaya University which prioritizes the principles of Good governance. To support laboratory operations, a management information system is needed to improve efficiency in planning, managing and reporting. The purpose of this research is to develop a laboratory management information system that adheres to the principles in the SNI ISO/IEC 17025:2017 standard. The software was developed using the Framework for the Application System Thinking (FAST). In the requirement analysis phase, an in-depth study was carried out to formulate requirements in accordance with the principles in the SNI ISO/IEC 17025:2017 clause control standard. In the end, a Laboratory MIS was successfully developed using this approach. It improves laboratory performance, so that capable to provide better services
Pengkukuran kematangan Teknologi dan Sistem Informasi di Pendidikan Tinggi dilatarbelakangi oleh kesadaran pada nilai strategis dan pentingnya teknologi dan sistem informasi dalam mendukung program kegiatan akademik. Secara umum pengukuran kematangan proses penyampaian layanan TI di Sistem informasi bertujuan un tuk memberikan gambaran atau dokumen pedoman kondisi teknologi dan sistem informasi secara utuh terkait dengan kepentingan akademik. Penelitian ini bertujuan untuk
Parallel processing sering digunakan untuk melakukan optimasi execution time terhadap algoritma data mining. Pada penelitian ini, parallel processing digunakan untuk melakukan optimasi pada algoritma clustering K-Means. Implementasi algoritma K-means dilakukan dengan memanfaatkan package yang tersedia pada framework R. Algoritma K-Means dijalankan secara serial dan parallel. Untuk mendapatkan persentase optimasi, maka dilakukan perbandingan antara execution time pada parallel processing dan execution time pada serial processing. Penelitian ini menggunakan dataset Boston Housing yang umum digunakan pada data mining. Skenario pengujian dibedakan berdasarkan jumlah core dan jumlah centroid. Hasil pengujian menunjukkan bahwa parallel processing untuk tiap skenario memiliki execution time yang lebih kecil daripada serial processing. Optimasi yang dihasilkan cukup signifikan, yakni bernilai 20% hingga 52%. Optimasi tertinggi didapatkan pada jumlah core terbanyak dan jumlah centroid terbesar.
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