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All Journal Dharma: Jurnal Pengabdian Masyarakat Jurnal Pendidikan Teknologi Informasi dan Komunikasi International Journal of Advances in Artificial Intelligence and Machine Learning
Dyah Ayu Irawati
Industrial Engineering Faculty UPN “Veteran” Yogyakarta
Computer Science & IT Education Social Sciences Other

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PENDAMPINGAN UMKM KWT SUKA MAJU UNTUK MENINGKATKAN PRODUKSI DAN PEREKONOMIAN MASYARAKAT DUSUN PALIHAN Heriyanto Heriyanto; Yuli Fauziah; Dyah Ayu Irawati
Dharma: Jurnal Pengabdian Masyarakat Vol 1, No 2 (2020): November
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.956 KB) | DOI: 10.31315/dlppm.v1i2.4043

Abstract

The SUKAMAJU Women's Farmer Group (KWT) is a group of women craftsmen of banana tree processing. During the Covid-19 pandemic, sales and marketing of processed banana food were very limited. Online marketing in times of the Covid-19 pandemic is urgently needed and requires support. Community service from UPN Veteran Yogyakarta, in this case, is programmed to help solve problems during the pandemic. Marketing through the internet and social media is very much needed, while the ability of mothers to master social media and the internet is very limited. The service team from UPN Veteran Yogyakarta is trying to help with solutions going into the field to help provide full assistance and also assistance for production equipment so that food processing craftsmen maintain production in KWT. The hope of the community service team is that there will be an increase in sales results by providing full assistance in both marketing media and increasing production equipment with an average increase of 8-9 pieces per day.
Plant Disease Detection Using Image Processing and Machine Learning Ranga Swamy Sirisati; J. Sravya; D. Sruthi; A. Nandhu; R. Navya Sree; Dyah Ayu Irawati
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.433

Abstract

Background: Plant diseases continue to threaten agricultural productivity worldwide, causing significant reductions in crop yield and quality. Traditional visual inspection by farmers or experts is often slow, subjective, and unreliable, especially across large plantation areas. With the increasing availability of digital imaging technologies, automated detection through image processing and machine learning presents a promising alternative.Aims: This study aims to develop an enhanced plant disease detection framework using image processing combined with machine learning algorithms, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN).Methods: A dataset of 54,306 leaf images from the PlantVillage collection was used to train and test the models. Preprocessing steps included resizing, noise removal, background segmentation, and feature extraction. CNNs were trained for end-to-end classification, while SVM operated on manually extracted features. A 10-fold cross-validation procedure was employed to ensure robustness. Fine-tuning strategies and comparative experiments were implemented to evaluate performance consistency across dataset variants.Result: The system demonstrated strong capability in early disease detection, achieving 97% accuracy for healthy leaves and moderate performance (56%) for certain diseased classes due to visual similarity and image noise. Background segmentation improved focus on disease features, while grayscale images reduced reliance on color cues but lowered classification accuracy.Conclusion: The findings confirm that machine learning, particularly CNN-based models, can significantly enhance plant disease diagnosis and support timely agricultural decision-making. Future improvements will explore advanced deep learning architectures, expanded datasets, multimodal imaging, and IoT integration for real-time field deployment.
Interactive Scratch-Based Learning to Enhance Engagement and Digital Literacy in Early Childhood Education Dyah Ayu Irawati; Yudhy Widya Kusumo; Trismi Ristyowati
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2026): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v5i2.674

Abstract

Purpose of the Study: The purpose of this study is to explore how interactive Scratch-based learning influences children's engagement and digital literacy within the context of early childhood education (PAUD). Specifically, the study examines whether integrating Scratch into the learning process improves children's attention, motivation, active participation, and fundamental digital interaction skills compared with conventional instructional methods.Methodology: The study adopted a quasi-experimental design using a non-equivalent control group approach. The participants comprised 30 children aged 4–5 years enrolled in a single early childhood education (PAUD) institution. They were assigned to two groups: an experimental group (n = 15), which participated in Scratch-based interactive learning activities, and a control group (n = 15), which received conventional storytelling-based instruction. Data were collected using observation sheets with a 4-point scale measuring focus, enthusiasm, participation, and digital literacy through pre-test and post-test observations and subsequently analyzed using paired-samples and independent-samples t-test.Main Findings: The findings revealed that children in the experimental group demonstrated significantly greater improvements across all measured variables than those in the control group. Interactive Scratch-based learning effectively increased attention, active participation, and enthusiasm during learning activities. Additionally, children demonstrated improved ability to interact with digital media and follow simple digital instructions. The effect size analysis also indicated large practical effects in the experimental group.Novelty/Originality of This Study: This study offers an empirical contribution by examining the effectiveness of Scratch-based interactive learning in early childhood education through a quasi-experimental research design. It integrates digital game-based learning with measurable engagement and digital literacy outcomes, which remains limited in research on PAUD, particularly in real classroom contexts.
Co-Authors A. Nandhu D. Sruthi Heriyanto Heriyanto J. Sravya R. Navya Sree Ranga Swamy Sirisati Trismi Ristyowati Yudhy Widya Kusumo Yuli Fauziah