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Prameswari, Mayesq
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Optimalisasi Pengelolaan Data Tumbuh Kembang Anak PAUD dengan Aplikasi Excel: Studi Kasus KB Kenanga Desa Pesantren Ayu, I Gusti De; Prameswari, Mayesq; Kania, Putri Emas; Harnoko, Sri Namira Putri; Putri, Aina Latifa Riyana
Jurnal Pengabdian Sosial Vol. 2 No. 2 (2024): Desember
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/pj95y776

Abstract

Kelompok Bermain Kenanga di Desa Pesantren menggunakan buku Deteksi Dini Tumbuh Kembang Anak (DDTK) sebagai acuan untuk melihat perkembangan dari peserta didiknya. Saat ini, guru PAUD mencatat perkembangan anak secara manual, yang berpotensi menimbulkan kesalahan dan keterbatasan dalam akses data. Penelitian ini bertujuan untuk mengoptimalkan pengelolaan data perkembangan anak PAUD di Kelompok Bermain Kenanga, Desa Pesantren, menggunakan aplikasi Microsoft Excel. Prosedur penelitian mencakup identifikasi kebutuhan, perencanaan program, pelaksanaan pelatihan Excel, serta monitoring dan evaluasi. Hasil penelitian menunjukkan bahwa aplikasi Excel membantu mempermudah pencatatan dan meningkatkan akurasi dalam pengelolaan data perkembangan anak. Selain itu, penggunaan Excel efektif untuk mendukung proses pemantauan perkembangan anak dan memperkuat keterlibatan orang tua dalam proses pendidikan anak.
TelUP Human Fall Dataset: A Motion Forecasting Study of Human Falls Widiyanto, Agung; Candraningtyas, Raphon Galuh; F.F, Andi Hisyam Helmi; Prameswari, Mayesq; Bashiran, Himam; Surahmat, Geugeut Nyarikawanti; Rahmah, Balqis Awaluna; Manika Dewi, Anak Agung Istri Candra; Yunus, Andi Prademon
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1420

Abstract

This study investigates multitask learning approaches for human motion forecasting and fall classification using pose data extracted from video sequences. A custom dataset, the TelUP HumanFall Forecasting Dataset, was developed, containing annotated video frames representing fall and non-fall scenarios captured from six participants. Pose information was extracted using YOLOv11, producing 17 keypoints per frame, which were normalized and segmented into temporal sequences for training. Three deep learning architectures, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), were implemented and evaluated. The models were assessed in a subject-independent test set consisting of two participants to ensure generalization. Quantitative evaluation measured the forecast error using the mean per joint position error (MPJPE) and classification accuracy. The MLP achieved the lowest MPJPE of 0.2630 (131.5 pixels), while the LSTM obtained the highest classification accuracy of 92.89%. Qualitative analysis revealed limitations in the capture of complex joint dynamics. Despite fast training convergence, the results emphasize a trade-off between forecast precision and classification accuracy. Future work will explore more expressive architectures and improved pose extraction methods to enhance forecast realism.
Co-Authors Andi Prademon Yunus Ayu, I Gusti De Bashiran, Himam Candraningtyas, Raphon Galuh F.F, Andi Hisyam Helmi Harnoko, Sri Namira Putri Kania, Putri Emas Manika Dewi, Anak Agung Istri Candra Putri, Aina Latifa Riyana Rahmah, Balqis Awaluna Surahmat, Geugeut Nyarikawanti Widiyanto, Agung