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All Journal DEVOSI International Journal of Electronics and Communications Systems
Saputra, Nikola
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Religion Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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PENGUATAN RESPONSIVE CAREGIVING BAGI CAREGIVER Formen, Ali; Sugiana, Sugiana; Rohmah, Naili; Kiswanto, Kiswanto; Tarigan, Novelia Danesya; Muthianabila, Lisa; Saputra, Nikola
DEVOSI Vol 6 No 1 (2025): Devosi: Jurnal Pengabdian Masyarakat
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/devosi.v6i1.10492

Abstract

The primary and fundamental education for a child begins within the family. Early childhood requires optimal caregiving from those around them, particularly their parents. However, in today's reality, many young parents are engaged in work, leading to child caregiving responsibilities being entrusted to caregivers, daycare centers, or other family members. According to data, 84.33% of Indonesian children are raised by both biological parents, while 2.51% are raised solely by their father, 8.43% solely by their mother, and 4.76% by other family members. In the Mangunsari area, caregivers are not only responsible for childcare but are also burdened with household chores, which divides their attention and reduces focus on the child. Therefore, introducing the responsive caregiving module serves as a solution to enhance the quality of childcare for children under the age of three.This community service initiative aims to improve caregiver competencies through the introduction of the responsive caregiving module, enhance their understanding of child development milestones, and develop their skills in providing educational games and creating appropriate play materials for child development. The implementation method consists of five stages: preparation, training and education, practical experience, mentoring, and evaluation with feedback. The results indicate that the responsive caregiving module effectively enhances caregivers' competencies in providing responsive care. Based on interviews and observations, 92% of participants showed improved understanding, while 89% demonstrated increased skills in responding to children's needs and desires. Therefore, this initiative contributes to improving the quality of childcare in the Mangunsari area of Semarang City.
Lightweight Image-Based Mold Detection System for Real-Time Bread Quality Monitoring Using Artificial Neural Networks (ANN) Saputra, Nikola; Ilyas, Muh.; Riswansyah , Muh Fikra Junian; Kaswar, Andi Baso; Lamada, Mustari S.
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28706

Abstract

Mold contamination of white bread is an ongoing challenge for quality monitoring, while conventional visual inspection remains unreliable for early and consistent detection. This study aims to propose a lightweight image-based mold detection system for white bread oriented towards real-time quality monitoring using Artificial Neural Networks (ANNs). An experimental workflow combining digital image acquisition, pre-processing, Otsu-based segmentation, morphological refinement, multicolor color space feature extraction, and an Artificial Neural Network (ANN) classifier is implemented. Results indicate that color information is the dominant discriminatory cue for mold identification, while texture descriptors provide complementary structural information that improves class separation. The RGB+HSV+LAB combination achieved the highest performance, with a training accuracy of 97.91 percent and a testing accuracy of 96.66 percent. These findings demonstrate that effective mold classification can be achieved without relying on deep or computationally intensive architectures when the feature representation is well-designed. In conclusion, a lightweight, feature-centric ANN (Artificial Neural Network) is sufficient for reliable classification of mold growth levels on white bread. This study confirms that a compact, feature-based learning strategy is sufficient for reliable classification of mold on white bread, providing a technically efficient basis for a vision-based food quality assessment system.
Co-Authors Ali Formen Andi Baso Kaswar Andi Baso Kaswar Ilyas, Muh. Muthianabila, Lisa Naili Rohmah, Naili Riswansyah , Muh Fikra Junian RR. Ella Evrita Hestiandari Sugiana Sugiana, Sugiana Tarigan, Novelia Danesya