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All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Tabina Athifa Rahmaniya
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Implementasi Literasi Digital Sebagai Strategi Peningkatan Computational Thinking Pada Anak Binaan Perlindungan Sosial Desa Balewangi Pamella M Sri Rezeki; Tabina Athifa Rahmaniya; Karina Hoirun Nisa; Girah Ismi Nugraha; Hanif Luqman Muttaqin; Euis Novianti; Muthia Sandi Firamid; Abizar Algifari; Alfarabi Kurniawan; Alfian Arsyad Wijaya; Ali Izzudin; Anyelir Kuntum Sari; Gaga Gunawan Ginanjar; Hildan Albar Islami; Mochamad Solahudin; Muhamad Faisal Fadilah; Muhammad Dzikri Abdi Fathir; Muhammad Lutfi Nurrizal; Rifki Ramdani; Riski Alwi Al-Idrus; Salman Haddad Baihaqi
Jurnal PkM MIFTEK Vol 7 No 1 (2026): Jurnal PkM Miftek
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.7-1.2899

Abstract

Pengabdian kepada masyarakat ini diarahkan untuk meningkatkan pemahaman Computational Thinking pada anak binaan Perlindungan Sosial Anak (PSA) Desa Balewangi. Computational Thinking merupakan kemampuan berpikir logis, sistematis, dan terstruktur yang relevan dalam pemecahan masalah sehari-hari maupun berbasis teknologi. Strategi pembelajaran yang digunakan adalah edukasi interaktif melalui penyajian materi visual, penayangan video, serta diskusi kelompok. Desain penelitian menerapkan pre-test dan post-test guna menilai efektivitas program. Kegiatan dilaksanakan pada 9 Agustus 2025 dengan partisipasi 30 anak. Hasil evaluasi menunjukkan peningkatan nilai pada post-test sebesar 12,5%. Pencapaian ini menegaskan bahwa pendekatan edukasi interaktif mampu memperkuat pemahaman Computational Thinking pada anak binaan PSA, sekaligus berkontribusi pada pengembangan literasi digital di tingkat komunitas pedesaan.
FedProx Optimization Based on Compression and Quantization for Efficient Heart Disease Prediction on Edge Devices Anyelir Kuntum Sari; Tabina Athifa Rahmaniya
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.236

Abstract

Cardiovascular disease remains a leading cause of mortality worldwide, posing challenges for early detection within privacy-sensitive medical environments. Federated Learning (FL) offers a promising solution by enabling distributed model training without transferring raw patient data to a central server. However, FL deployment on edge devices is constrained by limited resources, system heterogeneity, and non-IID data, which degrade model stability and convergence. This study integrates sparsification-based compression and 16-bit quantization into FedProx and evaluates their combined and individual effects on heart disease prediction under compounded non-IID and straggler conditions. Using the Heart Disease Train-Test dataset from the UCI repository (1,025 samples, 14 attributes) and a KDD methodology, experiments simulated a non-IID FL environment with 10 clients and 30% stragglers over 50 rounds, averaged over five seeds. Results show that standard FedProx achieves the highest mean performance (86.44% accuracy, 87.34% F1-Score), closely followed by FedAvg (86.15%, 87.18%), while the proposed method attains a lower mean accuracy (84.88%) and F1-Score (85.87%), not statistically significant (p = 0.061, 0.098). ROC-AUC, however, is significantly lower for the proposed method (93.52% vs. 94.42%, p < 0.01), indicating reduced discriminative ability. An ablation study shows compression, not quantization, primarily drives the efficiency gains, reducing transmitted parameter size by 45.09% and accelerating convergence to 20.2 rounds compared with the baseline methods. These findings indicate that integrating compression and quantization into FedProx achieves substantial communication efficiency and faster convergence with only minor performance trade-offs, offering a viable option for resource-constrained edge deployments.
Co-Authors Abizar Algifari Alfarabi Kurniawan Alfian Arsyad Wijaya Ali Izzudin Anyelir Kuntum Sari Euis Novianti Gaga Gunawan Ginanjar Girah Ismi Nugraha Hanif Luqman Muttaqin Hildan Albar Islami Karina Hoirun Nisa Mochamad Solahudin Muhamad Faisal Fadilah Muhammad Dzikri Abdi Fathir Muhammad Lutfi Nurrizal Muthia Sandi Firamid Pamella M Sri Rezeki Rifki Ramdani Riski Alwi Al-Idrus Salman Haddad Baihaqi