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Bashiran, Himam
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Religion Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pengembangan Keterampilan Spreadsheet untuk Siswa Sekolah Dasar: Pelatihan dan Pembuatan Database Koleksi Buku di Rumah Literasi Desa Tambaksogra Bashiran, Himam; Dewanti, Anggun; Zahra, Indy Aurellia Az; Silaen, Ferdinan; 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: 10.59837/0a8ccc31

Abstract

Rumah Literasi Desa Tambaksogra menghadapi tantangan dalam pengelolaan koleksi buku yang belum terorganisasi secara efektif. Di sisi lain, siswa sekolah dasar (SD) di desa tersebut, khususnya siswa kelas akhir yang akan melanjutkan ke jenjang sekolah menengah pertama (SMP), umumnya belum memiliki keterampilan dasar dalam pengolahan data. Program pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan siswa dalam penggunaan spreadsheet dan memberikan solusi pengelolaan koleksi buku melalui pelatihan terstruktur. Pelatihan mencakup pengenalan antarmuka spreadsheet, fungsi dasar seperti SUM dan AVERAGE, serta simulasi pembuatan database buku. Hasil survei menunjukkan peningkatan pemahaman siswa dari 73% menjadi 94%, serta tingkat kepuasan rata-rata sebesar 71,48%. Program ini memberikan manfaat langsung bagi siswa dalam literasi teknologi dan membantu pengelola Rumah Literasi mengoptimalkan pencatatan koleksi buku secara lebih efektif.
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 Candraningtyas, Raphon Galuh Dewanti, Anggun F.F, Andi Hisyam Helmi Manika Dewi, Anak Agung Istri Candra Prameswari, Mayesq Putri, Aina Latifa Riyana Rahmah, Balqis Awaluna Silaen, Ferdinan Surahmat, Geugeut Nyarikawanti Widiyanto, Agung Zahra, Indy Aurellia Az