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Decode: Jurnal Pendidikan Teknologi Informasi
Published by Universitas Muhammadiyah Kendari
ISSN : 27752984     EISSN : 27751813     DOI : https://doi.org/10.51454/decode
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Other
DECODE: Jurnal Pendidikan Teknologi Informasi publishes articles in English and Indonesian which will be published 3 times a year, namely March, July and November. Articles that can be considered for publication in this journal are the results of research that is in accordance with the focus and scope of the journal including: (1) Software Engineering, (2) Data Mining, (3) Artificial Intelligence, (4) Computer Network, (5) Robotics System, (6) Information Security, (7) Mobile Learning, (8) Online Learning, (9) Multimedia, (10) Development Learning Media, (11) Integration of Technology in Learning.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 342 Documents
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Theory-Informed Visual Tactile Augmented Learning for Breast Self-Examination in Rural Indonesia Wahyuny, Romy; Janiarli, Masdi; Fahmi, Yuyun Bewelli; Wardani, Ratna; Yanto, Budi
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 3: NOVEMBER 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i3.1878

Abstract

Breast self-examination (BSE) is a breast-awareness practice, but education may improve knowledge without ensuring skill or sustained behavior. This study developed and evaluated a Theory of Planned Behavior-informed Visual Tactile Augmented Learning (VTAL) model for women of reproductive age in rural Indonesia. A quasi-experimental non-equivalent control-group design included 160 women (80 VTAL; 80 routine education). The 12-phase intervention combined visual demonstration, tactile breast-model practice, corrective feedback, action planning, and AYU CARE digital reinforcement. Content validity was excellent (S-CVI/Ave=0.96). At immediate post-test, adjusted outcomes favored VTAL for BSE knowledge (β=2.15; d=1.00), attitude (β=2.47; d=0.81), perceived behavioral control (β=2.81; d=0.91), behavioral intention (β=0.98; d=0.65), and procedural competence (β=2.02; d=0.81), all p<0.001. One-month BSE regularity favored VTAL but was not significant (OR=1.92; p=0.082). VTAL can support public-health education by linking supervised practice, feedback, digital reinforcement, and referral-oriented breast awareness, although sustained behavioral benefit requires longer follow-up.
Resilience Under Constraint: Benchmarking ResNet-50, MobileNetV3, and EfficientNet-B0 for Fine-Grained Indonesian Food Recognition Syidada, Shofiya; Prasetya, Noven Indra; Pandunata, Priza
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 3: NOVEMBER 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i3.1892

Abstract

Fine-grained recognition of traditional Indonesian cuisine poses a significant challenge for computer vision systems. The primary problem stems from high inter-class texture and color similarities among regional dishes, coupled with severe model performance degradation when training is constrained by limited dataset size. To address these limitations, this study implements a structured machine learning pipeline comprising digital image pre-processing (resizing to 224 x 224, normalization, and geometric data augmentation), a two-stage transfer learning protocol, and an automated recipe retrieval system. Three distinct Convolutional Neural Network (CNN) architectures—ResNet-50 (deep residual learning), MobileNetV3-Large (lightweight attention-guided depthwise convolutions), and EfficientNet-B0 (compound scaling)—were evaluated to classify five traditional Indonesian dishes (Ayam Betutu, Coto Makassar, Gudeg, Papeda, and Rendang). A two-stage transfer learning protocol was implemented across two experimental data partitioning schemes: Scenario A (80:10:10) and Scenario B (60:20:20). Experimental results show that MobileNetV3-Large performed best under optimal data availability (Scenario A), achieving a 95.65% test accuracy. However, under a 25% training data reduction (Scenario B), ResNet-50 demonstrated superior structural resilience, surging to a top test accuracy of 97.83%, whereas MobileNetV3-Large and EfficientNet-B0 dropped to 91.30% and 89.13%, respectively. Confusion matrix analysis revealed that primary misclassifications stemmed from shared liquid broth backgrounds and shredded protein textures between Gudeg and Ayam Betutu. Finally, real-world inference testing on unobserved images confirmed practical deployment viability, successfully mapping predicted class labels and confidence scores (ranging from 30.41% to 69.57%) to corresponding recipe database entries.

Page 35 of 35 | Total Record : 342

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